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:

Show Criteria

Hide Criteria

Comparing:

EM-63
EM-84
EM-92
EM-127
EM-449
EM-660
EM-709
EM-788
EM-841
EM-862
EM-937
EM-992

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID

EM-63

EM-84

EM-92

EM-127

EM-449

EM-660

EM-709

EM-788

EM-841

EM-862

EM-937

EM-992

EM Short Name

EnviroAtlas - Natural biological nitrogen fixation

ACRU, South Africa

Runoff potential of pesticides, Europe

Annual profit - carbon plantings, South Australia

Decrease in erosion (shoreline), St. Croix, USVI

RUM: Valuing fishing quality, Michigan, USA

Pollinators on landfill sites, United Kingdom

Wild bees over 26 yrs of restored prairie, IL, USA

Brown-headed cowbird abundance, Piedmont, USA

Recreational fishery index, USA

EPA national stormwater calculator tool

DAISY model, Taastrup, Copenhagen

EM Full Name

US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA

ACRU (Agricultural Catchments Research Unit), South Africa

Runoff potential of pesticides, Europe

Annual profit from carbon plantings, South Australia

Decrease in erosion (shoreline) by reef, St. Croix, USVI

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

Brown-headed cowbird abundance, Piedmont ecoregion, USA

Recreational fishery index for streams and rivers, USA

Environmental Protection Agency National stormwater calculator tool

Ecosystem function and service quantification and valuation in a conventional winter wheat production system with DAISY model in Denmark

EM Source or Collection

US EPA | EnviroAtlas

None

US EPA

None

US EPA

None

EM Source Document ID

262 ?

271

254

243

335

382 ?

389

401

405

414

428 ?

464

Document Author

US EPA Office of Research and Development - National Exposure Research Laboratory

Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S.

Schriever, C. A., and Liess, M.

Crossman, N. D., Bryan, B. A., and Summers, D. M.

Yee, S. H., Dittmar, J. A., and L. M. Oliver

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

Riffel, S., Scognamillo, D., and L. W. Burger

Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold

Rossman, L.A., Bernagros, J.T., Barr, C.M., and M.A. Simon

Ghaley, B. B., & Porter, J. R.

Document Year

2013

2008

2007

2011

2014

2013

2017

2008

2021

2022

2014

Document Title

EnviroAtlas - National

Mapping ecosystem services for planning and management

Mapping ecological risk of agricultural pesticide runoff

Carbon payments and low-cost conservation

Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI

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

Effects of the Conservation Reserve Program on northern bobwhite and grassland birds

Correspondence between a recreational fishery index and ecological condition for U.S.A. streams and rivers.

EPA National Stormwater Calculator Web App users guide-Version 3.4.0.

Ecosystem function and service quantification and valuation in a conventional winter wheat production system with DAISY model in Denmark

Document Status

Peer reviewed and published

Comments on Status

Published on US EPA EnviroAtlas website

Published journal manuscript

Published EPA report

Published journal manuscript

Software and Access

EM ID

EM-63

EM-84

EM-92

EM-127

EM-449

EM-660

EM-709

EM-788

EM-841

EM-862

EM-937

EM-992

EM Software Access info Exit

https://www.epa.gov/enviroatlas

Not applicable

https://www.epa.gov/water-research/national-stormwatercalculator

Not applicable

Contact Name

EnviroAtlas Team ?

Roland E Schulze

Carola Alexandra Schriever

Neville D. Crossman

Susan H. Yee

Richard Melstrom

Sam Tarrant

Sean R. Griffin

Sam Riffell

Gregg Lomnicky

Lewis Rossman

Bhim Bahadur Ghaley

Contact Address

Not reported

School of Bioresources Engineering and Environmental Hydrology, University of Natal, South Africa

Helmholtz Centre for Environmental Research - UFZ, Department of System Ecotoxicology, Permoserstrasse 15, 04318 Leipzig, Germany

CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia

US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA

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.

Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA

200 SW 35th St., Corvallis, OR, 97333

Center for environmental solutions and emergency response, Cincinnati, Ohio

Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen, Højbakkegård Allé 30, DK-2630 Taastrup, Denmark.

Contact Email

enviroatlas@epa.gov

schulzeR@nu.ac.za

carola.schriever@ufz.de

neville.crossman@csiro.au

yee.susan@epa.gov

melstrom@okstate.edu

sam.tarrant@rspb.org.uk

srgriffin108@gmail.com

sriffell@cfr.msstate.edu

lomnicky.gregg@epa.gov

n.a.

bbg@life.ku.dk

EM Description

EM ID

EM-63

EM-84

EM-92

EM-127

EM-449

EM-660

EM-709

EM-788

EM-841

EM-862

EM-937

EM-992

Summary Description

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."

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."

ABSTRACT: "The approach is based on the runoff potential (RP) of stream sites, by a spatially explicit calculation based on pesticide use, precipitation, topography, land use and soil characteristics in the near-stream environment. The underlying simplified model complies with the limited availability and resolution of data at larger scales." AUTHOR'S DESCRIPTION: "The RP is based on a mathematical model that describes runoff losses of a compound with generalized properties and which was developed from a proposal by the Organisation for Economic Co-operation and Development (OECD) for estimating dissolved runoff inputs of a pesticide into surface waters (OECD, 1998)...The runoff model underlying RP calculates the dissolved amount of a generic substance that was applied in the near environment of a stream site and that is expected to reach the stream site during one rainfall event. The dissolved amount results from a single application in the near-stream environment (i.e., a two-sided 100-m stream corridor extending for 1500 m upstream of the site) and is the amount of applied substance in the designated corridor reduced due to the influence of the site-specific key environmental factors precipitation, soil characteristics, topography, and plant interception."

ABSTRACT: "A price on carbon is expected to generate demand for carbon offset schemes. This demand could drive investment in tree-based monocultures that provide higher carbon yields than diverse plantings of native tree and shrub species, which sequester less carbon but provide greater variation in vegetation structure and composition. Economic instruments such as species conservation banking, the creation and trading of credits that represent biological-diversity values on private land, could close the financial gap between monocultures and more diverse plantings by providing payments to individuals who plant diverse species in locations that contribute to conservation and restoration goals. We studied a highly modified agricultural system in southern Australia that is typical of many temperate agriculture zones globally (i.e., has a high proportion of endangered species, high levels of habitat fragmentation, and presence of non-native species). We quantified the economic returns...from carbon plantings (monoculture and mixed tree and shrubs) under six carbon-price scenarios." AUTHOR'S DESCRIPTION: "The economic returns of carbon plantings are highly variable and depend primarily on carbon yield and price and opportunity costs (Newell & Stavins 2000; Richards & Stokes 2004; Torres et al. 2010)...The spatial variation in carbon yield and costs, including establishment, maintenance, transaction, and opportunity costs, means that the net economic returns of carbon plantings are also likely to vary spatially."

ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef."

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:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. "

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: EPA’s National Stormwater Calculator (SWC) is a software application tool that estimates the annual amount of rainwater and frequency of runoff from a specific site using green infrastructure as low impact development controls. The SWC is designed for use by anyone interested in reducing runoff from a property, including site developers, landscape architects, urban planners, and homeowners. This User’s guide contains information on the SWC web application. SWC Version 3.4 contains has updated historical meteorological data (from 1970 - 2006 to 1990 - 2019), updated Bureau of Labor Statistics Cost Data (from 2018 to 2020), and the 5.1.015 Stormwater Management Model (SWMM) engine (from 5.1.007). Evaporation was calculated by the Hargreaves method (EPA, 2015), based on historical or future daily temperature data."

With inevitable link between ecosystem function (EF), ecosystem services (ES) and agricultural productivity, there is a need for quantification and valuation of EF and ES in agro-ecosystems. Management practices have significant effects on soil organic matter (SOM), affecting productivity, EF and ES provision. The objective was to quantify two EF: soil water storage and nitrogen mineralization and three ES: food and fodder production and carbon sequestration, in a conventional winter wheat production system at 2.6% SOM compared to 50% lower (1.3%) and 50% higher (3.9%) SOM in Denmark by DAISY model. At 2.6% SOM, the food and fodder production was 6.49 and 6.86 t ha−1 year−1 respectively whereas carbon sequestration and soil water storage was 9.73 t ha−1 year−1 and 684 mm ha−1 year−1 respectively and nitrogen mineralisation was 83.58 kg ha−1 year−1. At 2.6% SOM, the two EF and three ES values were US$ 177 and US$ 2542 ha−1 year−1 respectively equivalent to US$ 96 and US$1370 million year−1 respectively in Denmark. The EF and ES quantities and values were positively correlated with SOM content. Hence, the quantification and valuation of EF and ES provides an empirical tool for optimising the EF and ES provision for agricultural productivity.

Specific Policy or Decision Context Cited

None Identified

None identified

European Commission Water Framework Directive (WFD, Directive 2000/60/EC)

None identified

None reported

None identified

None given

None identified

Biophysical Context

No additional description provided

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.

Not applicable

Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm.

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.

Conservation Reserve Program lands left to go fallow

None

Sites up to 12 acres

Agro-ecosystem test farm, Copenhagen, Denmark.

EM Scenario Drivers

No scenarios presented

Carbon prices at $10/t CO2^-e, $15/t CO2^-e, $20/t CO2^-e, $25/t CO2^-e, $30/t CO2^-e, and $40/t CO2^-e

No scenarios presented

targeted sport fish biomass

No scenarios presented

N/A

Climate change scenarios

A soil organic matter value of 1.3% was used for this model run

EM Relationship to Other EMs or Applications

EM ID

EM-63

EM-84

EM-92

EM-127

EM-449

EM-660

EM-709

EM-788

EM-841

EM-862

EM-937

EM-992

Method Only, Application of Method or Model Run

Method + Application

Method + Application (multiple runs exist) View EM Runs ?

Method + Application

Method + Application (multiple runs exist) View EM Runs

Method + Application

Method Only

Method + Application (multiple runs exist) View EM Runs

New or Pre-existing EM?

New or revised model

Application of existing model

New or revised model

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-63

EM-84

EM-92

EM-127

EM-449

EM-660

EM-709

EM-788

EM-841

EM-862

EM-937

EM-992

Document ID for related EM

Doc-346 | Doc-347 ?

Doc-272 ?

Doc-255 | Doc-256 | Doc-257

Doc-245 | Doc-246 | Doc-247 | Doc-243

Doc-335

None

Doc-389

None

Doc-405

None

EM ID for related EM

None

EM-128 | EM-141

EM-447 | EM-448

None

EM-697

None

EM-831 | EM-838 | EM-839 | EM-842 | EM-843 | EM-844 | EM-845 | EM-846 | EM-847

None

EM Modeling Approach

EM Relationship to Time

EM ID

EM-63

EM-84

EM-92

EM-127

EM-449

EM-660

EM-709

EM-788

EM-841

EM-862

EM-937

EM-992

EM Temporal Extent

2006-2010

1950-1993

2000

2009-2050

2006-2007, 2010

2008-2010

2007-2008

1988-2014

2008

2013-2014

Not applicable

2003-2013

EM Time Dependence

time-stationary

time-dependent

time-stationary

time-dependent

time-stationary

time-dependent

EM Time Reference (Future/Past)

Not applicable

future time

Not applicable

past time

Not applicable

past time

EM Time Continuity

Not applicable

discrete

Not applicable

discrete

Not applicable

continuous

EM Temporal Grain Size Value

Not applicable

EM Temporal Grain Size Unit

Not applicable

Day

Year

Not applicable

Year

Not applicable

EM Spatial Extent

EM ID

EM-63

EM-84

EM-92

EM-127

EM-449

EM-660

EM-709

EM-788

EM-841

EM-862

EM-937

EM-992

Bounding Type

Geopolitical

Physiographic or Ecological

Physiographic or ecological

Watershed/Catchment/HUC

Multiple unrelated locations (e.g., meta-analysis)

Physiographic or ecological

Geopolitical

Not applicable

Physiographic or ecological

Spatial Extent Name

counterminous United States

South Africa

EU-15

Agricultural districts of the state of South Australia

Coastal zone surrounding St. Croix

HUCS in Michigan

East Midlands

Nachusa Grasslands

Piedmont Ecoregion

United States

Not applicable

Taastrup experimental farm

Spatial Extent Area (Magnitude)

>1,000,000 km^2

100,000-1,000,000 km^2

100-1000 km^2

100,000-1,000,000 km^2

1000-10,000 km^2.

10-100 km^2

100,000-1,000,000 km^2

>1,000,000 km^2

Not applicable

1-10 km^2

Spatial Distribution of Computations

EM ID

EM-63

EM-84

EM-92

EM-127

EM-449

EM-660

EM-709

EM-788

EM-841

EM-862

EM-937

EM-992

EM Spatial Distribution

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 lumped (in all cases)

other or unclear (comment)

Spatial Grain Type

other (specify), for irregular (e.g., stream reach, lake basin)

area, for pixel or radial feature

other (specify), for irregular (e.g., stream reach, lake basin)

Not applicable

length, for linear feature (e.g., stream mile)

Not applicable

Spatial Grain Size

irregular

Distributed by catchments with average size of 65,000 ha

10 km x 10 km

1 ha x 1 ha

10 m x 10 m

reach in HUC

multiple unrelated locations

Area varies by site

Not applicable

stream reach (site)

Not applicable

EM Structure and Computation Approach

EM ID

EM-63

EM-84

EM-92

EM-127

EM-449

EM-660

EM-709

EM-788

EM-841

EM-862

EM-937

EM-992

EM Computational Approach

Analytic

Numeric

Analytic

Numeric

Analytic

EM Determinism

deterministic

Statistical Estimation of EM

Conventional (frequentist) statistics

None

None

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-63

EM-84

EM-92

EM-127

EM-449

EM-660

EM-709

EM-788

EM-841

EM-862

EM-937

EM-992

Model Calibration Reported?

Yes

Not applicable

Yes

Not applicable

Unclear

Model Goodness of Fit Reported?

Yes

Not applicable

Unclear

Goodness of Fit (metric| value | unit)

None

R squared | 0.552 | unitless

None

Model Operational Validation Reported?

Yes

Not applicable

Yes

Model Uncertainty Analysis Reported?

Yes

Not applicable

Unclear

Model Sensitivity Analysis Reported?

Yes

Not applicable

Yes

Not applicable

Unclear

Model Sensitivity Analysis Include Interactions?

Not applicable

Unclear

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-84	EM-92	EM-127	EM-449	EM-660	EM-709	EM-788	EM-841	EM-862	EM-937	EM-992
North America United States	Africa South Africa	Europe United Kingdom Portugal Spain Greece Austria Netherlands Sweden Belgium Ireland Luxembourg Finland Denmark Italy France Germany	Oceania Australia	None	North America United States Michigan	Europe United Kingdom	North America United States Illinois	North America United States North Carolina Georgia Alabama Maryland New Jersey South Carolina Pennsylvania	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	North America United States	Europe Denmark

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

EM-63	EM-84	EM-92	EM-127	EM-449	EM-660	EM-709	EM-788	EM-841	EM-862	EM-937	EM-992
None	None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None	None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID

EM-63

EM-84

EM-92

EM-127

EM-449

EM-660

EM-709

EM-788

EM-841

EM-862

EM-937

EM-992

Centroid Latitude

39.5

-30

50.01

-34.9

17.73

45.12

52.22

41.89

36.23

36.21

Not applicable

55.4

Centroid Longitude

-98.35

4.67

138.7

-64.77

85.18

-0.91

-89.34

-81.9

-113.76

Not applicable

12.18

Centroid Datum

WGS84

Not applicable

None provided

Centroid Coordinates Status

Estimated

Provided

Estimated

Not applicable

Provided

Environments and Scales Modeled

EM ID

EM-63

EM-84

EM-92

EM-127

EM-449

EM-660

EM-709

EM-788

EM-841

EM-862

EM-937

EM-992

EM Environmental Sub-Class

Terrestrial Environment (sub-classes not fully specified)

Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified)

Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland

Agroecosystems

Near Coastal Marine and Estuarine

Rivers and Streams

Created Greenspace | Grasslands

Agroecosystems | Grasslands

Grasslands

Rivers and Streams

Terrestrial Environment (sub-classes not fully specified)

Agroecosystems

Specific Environment Type

Terrestrial

Not reported

Arable lands in near-stream environments

Agricultural land for annual crops, annual legumes, and grazing of sheep and cows

Coral reefs

stream reaches

restored landfills and grasslands

Restored prairie, prairie remnants, and cropland

grasslands

reach

Terrrestrial landcover

Agroecosystems

EM Ecological Scale

Ecological scale is finer than that of the Environmental Sub-class

Ecological scale is coarser 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

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID

EM-63

EM-84

EM-92

EM-127

EM-449

EM-660

EM-709

EM-788

EM-841

EM-862

EM-937

EM-992

EM Organismal Scale

Not applicable

Guild or Assemblage

Not applicable

Individual or population, within a species

Species

Guild or Assemblage

Not applicable

Guild or Assemblage ?

Taxonomic level and name of organisms or groups identified

EM-63	EM-84	EM-92	EM-127	EM-449	EM-660	EM-709	EM-788	EM-841	EM-862	EM-937	EM-992
None Available	None Available	None Available	Species Acacia implexa Eucalyptus microcarpa Acacia verniciflua Eucalyptus polyanthemos Eucalyptus globulus Eucalyptus sideroxylon Acacia mearnsii Acacia pycnantha Eucalyptus camaldulensis Eucalyptus kochii Acacia dealbata	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	Species Brown headed cowbird	None Available	None Available	Genus Wheat

EnviroAtlas URL

EM-63	EM-84	EM-92	EM-127	EM-449	EM-660	EM-709	EM-788	EM-841	EM-862	EM-937	EM-992
Average Annual Precipitation, Natural Biological Nitrogen Fixation, The Watershed Boundary Dataset (WBD)	Average Annual Precipitation	Average Annual Precipitation, Stream Length Impaired by Pesticides, The Watershed Boundary Dataset (WBD), Hectares of Vegetable Crops	Carbon Storage by Tree Biomass	National Hydrography Dataset Plus (NHD PlusV2)	The National Hydrography Dataset (NHD), The Watershed Boundary Dataset (WBD), Enabling Conditions, Employment Rate	GAP Ecological Systems	GAP Ecological Systems	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions	None Available	Average Annual Precipitation, Water supply from NID reservoirs (million gallons), Enabling Conditions	Average Annual Daily Potential Solar Energy, Average Annual Precipitation, Agricultural water use (million gallons/day), Stream Length Impaired by Pesticides

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-84	EM-92	EM-127	EM-449	EM-660	EM-709	EM-788	EM-841	EM-862	EM-937	EM-992
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes	[Ecosystem] Provisioning Nutrition Water Surface water for drinking Materials Water Surface water for non-drinking purposes	None	[Ecosystem] Regulation & Maintenance Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection	[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] 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] 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	None	[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-63	EM-84	EM-92	EM-127	EM-449	EM-660	EM-709	EM-788	EM-841	EM-862	EM-937	EM-992
Atmosphere Soil (2) Specific types of soil Quality Indicator	Rivers and Streams (111) Water (3) Liquid water Stock Indicator	None	None	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Extreme Event Indicator	Rivers and Streams (111) Fauna (4) Fish Quality Indicator	None	None	Grasslands (23) Fauna (4) Birds Stock Indicator	Rivers and Streams (111) Fauna (4) Fish Quality Indicator	Urban/Surburban (27) Composite (8) Regulation of extreme events Flow Indicator	Agroecosystems (22) Flora (5) food crops Stock Indicator Fodder Stock Indicator Soil (2) Specific types of soil Quality Indicator

US EPA

EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

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All EM Descriptors

EM Descriptors by Modeling Concepts

Comparing:

EM Identity and Description

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

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EM Modeling Approach

Comment:

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

Comment:

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)