EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?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.
EM Identity and Description
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EM ID
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EM-127 |
EM-660 |
EM-788 |
EM-841 | EM-937 |
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EM Short Name
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Annual profit - carbon plantings, South Australia | RUM: Valuing fishing quality, Michigan, USA | Wild bees over 26 yrs of restored prairie, IL, USA | Brown-headed cowbird abundance, Piedmont, USA | EPA national stormwater calculator tool |
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EM Full Name
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Annual profit from carbon plantings, South Australia | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA | Brown-headed cowbird abundance, Piedmont ecoregion, USA | Environmental Protection Agency National stormwater calculator tool |
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EM Source or Collection
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None | None | None | None | US EPA |
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EM Source Document ID
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243 |
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. |
401 | 405 |
428 ?Comment:This is a tool available on the web for downloading to personal computers. A manual is also available for further documentation of the tool. |
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Document Author
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Crossman, N. D., Bryan, B. A., and Summers, D. M. | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree | Riffel, S., Scognamillo, D., and L. W. Burger | Rossman, L.A., Bernagros, J.T., Barr, C.M., and M.A. Simon |
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Document Year
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2011 | 2014 | 2017 | 2008 | 2022 |
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Document Title
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Carbon payments and low-cost conservation | Valuing recreational fishing quality at rivers and streams | 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 | EPA National Stormwater Calculator Web App users guide-Version 3.4.0. |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report |
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EM ID
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EM-127 |
EM-660 |
EM-788 |
EM-841 | EM-937 |
| Not applicable | Not applicable | Not applicable | Not applicable | https://www.epa.gov/water-research/national-stormwatercalculator | |
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Contact Name
em.detail.contactNameHelp
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Neville D. Crossman | Richard Melstrom | Sean R. Griffin | Sam Riffell | Lewis Rossman |
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Contact Address
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CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | 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 | Center for environmental solutions and emergency response, Cincinnati, Ohio |
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Contact Email
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neville.crossman@csiro.au | melstrom@okstate.edu | srgriffin108@gmail.com | sriffell@cfr.msstate.edu | n.a. |
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EM ID
em.detail.idHelp
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EM-127 |
EM-660 |
EM-788 |
EM-841 | EM-937 |
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Summary Description
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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: " 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: "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: 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." |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None reported | None given |
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Biophysical Context
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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. | stream and river reaches of Michigan | 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 | Sites up to 12 acres |
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EM Scenario Drivers
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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 | targeted sport fish biomass | No scenarios presented | N/A | Climate change scenarios |
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EM ID
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EM-127 |
EM-660 |
EM-788 |
EM-841 | EM-937 |
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Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs ?Comment:Runs are differentiated based on the the expected annual profit from two types of carbon plantings: 1) Tree-based monocultures (i.e., monoculture carbon planting) and 2) Diverse plantings of native tree and shrub species (i.e., ecological carbon planting) |
Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only |
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New or Pre-existing EM?
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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
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EM ID
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EM-127 |
EM-660 |
EM-788 |
EM-841 | EM-937 |
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Document ID for related EM
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Doc-245 | Doc-246 | Doc-247 | Doc-243 | None | None | Doc-405 | None |
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EM ID for related EM
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EM-128 | EM-141 | None | None | EM-831 | EM-838 | EM-839 | EM-842 | EM-843 | EM-844 | EM-845 | EM-846 | EM-847 | None |
EM Modeling Approach
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EM ID
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EM-127 |
EM-660 |
EM-788 |
EM-841 | EM-937 |
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EM Temporal Extent
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2009-2050 | 2008-2010 | 1988-2014 | 2008 | Not applicable |
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EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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discrete | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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1 | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable | Not applicable | Not applicable |
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EM ID
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EM-127 |
EM-660 |
EM-788 |
EM-841 | EM-937 |
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Bounding Type
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Physiographic or Ecological | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Not applicable |
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Spatial Extent Name
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Agricultural districts of the state of South Australia | HUCS in Michigan | Nachusa Grasslands | Piedmont Ecoregion | Not applicable |
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Spatial Extent Area (Magnitude)
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100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | Not applicable |
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EM ID
em.detail.idHelp
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EM-127 |
EM-660 |
EM-788 |
EM-841 | EM-937 |
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EM Spatial Distribution
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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) |
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Spatial Grain Type
em.detail.spGrainTypeHelp
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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) | Not applicable | Not applicable |
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Spatial Grain Size
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1 ha x 1 ha | reach in HUC | Area varies by site | Not applicable | Not applicable |
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EM ID
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EM-127 |
EM-660 |
EM-788 |
EM-841 | EM-937 |
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EM Computational Approach
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Analytic | Numeric | Analytic | Analytic | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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EM ID
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EM-127 |
EM-660 |
EM-788 |
EM-841 | EM-937 |
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Model Calibration Reported?
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No | No | No | Yes | Not applicable |
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Model Goodness of Fit Reported?
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No | Yes | No | No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None |
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None | None | None |
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Model Operational Validation Reported?
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No | No | No | No | Not applicable |
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Model Uncertainty Analysis Reported?
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No | No | No | No | Not applicable |
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Model Sensitivity Analysis Reported?
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No | No | No | Yes | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Unclear | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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EM-127 |
EM-660 |
EM-788 |
EM-841 | EM-937 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-127 |
EM-660 |
EM-788 |
EM-841 | EM-937 |
| None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-127 |
EM-660 |
EM-788 |
EM-841 | EM-937 |
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Centroid Latitude
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-34.9 | 45.12 | 41.89 | 36.23 | Not applicable |
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Centroid Longitude
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138.7 | 85.18 | -89.34 | -81.9 | Not applicable |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | Not applicable |
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Centroid Coordinates Status
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Estimated | Estimated | Provided | Estimated | Not applicable |
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EM ID
em.detail.idHelp
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EM-127 |
EM-660 |
EM-788 |
EM-841 | EM-937 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Rivers and Streams | Agroecosystems | Grasslands | Grasslands | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | stream reaches | Restored prairie, prairie remnants, and cropland | grasslands | Terrrestrial landcover |
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EM Ecological Scale
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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 |
Scale of differentiation of organisms modeled
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EM ID
em.detail.idHelp
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EM-127 |
EM-660 |
EM-788 |
EM-841 | EM-937 |
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EM Organismal Scale
em.detail.orgScaleHelp
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Guild or Assemblage | Not applicable | Species | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
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EM-127 |
EM-660 |
EM-788 |
EM-841 | EM-937 |
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None Available |
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)
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EM-127 |
EM-660 |
EM-788 |
EM-841 | EM-937 |
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None |
<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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EM-127 |
EM-660 |
EM-788 |
EM-841 | EM-937 |
| None |
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None |
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