EcoService Models Library (ESML)
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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.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-345 
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EM-660
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EM-788
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EM-862 |
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EM Short Name
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InVEST habitat quality, Puli Township, Taiwan | RUM: Valuing fishing quality, Michigan, USA | Wild bees over 26 yrs of restored prairie, IL, USA | Recreational fishery index, USA |
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EM Full Name
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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 | Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA | Recreational fishery index for streams and rivers, USA |
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EM Source or Collection
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InVEST | None | None | US EPA |
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EM Source Document ID
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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. |
401 | 414 |
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Document Author
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Wu, C.-F., Lin, Y.-P., Chiang, L.-C. and Huang, T. | 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 | Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold |
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Document Year
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2014 | 2014 | 2017 | 2021 |
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Document Title
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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 | 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. |
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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 |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-345
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EM-660
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EM-788
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EM-862 |
| https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable | |
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Contact Name
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Yu-Pin Lin ?Comment:Tel.: +886 2 3366 3467; fax: +866 2 2368 6980 |
Richard Melstrom | Sean R. Griffin | Gregg Lomnicky |
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Contact Address
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Not reported | 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. | 200 SW 35th St., Corvallis, OR, 97333 |
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Contact Email
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yplin@ntu.edu.tw | melstrom@okstate.edu | srgriffin108@gmail.com | lomnicky.gregg@epa.gov |
EM Description
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EM ID
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EM-345
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EM-660
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EM-788
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EM-862 |
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Summary Description
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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: "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.] |
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Specific Policy or Decision Context Cited
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Environmental effects of Highway 6 construction on Puli Township, Taiwan | None identified | None identified | None identified |
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Biophysical Context
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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 | 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 |
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EM Scenario Drivers
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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 | N/A |
EM Relationship to Other EMs or Applications
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EM ID
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EM-345
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EM-660
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EM-788
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EM-862 |
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Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application |
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New or Pre-existing EM?
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Application of existing 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-345
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EM-660
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EM-788
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EM-862 |
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Document ID for related EM
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Doc-278 | None | None | None |
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EM ID for related EM
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EM-143 | None | None | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-345
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EM-660
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EM-788
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EM-862 |
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EM Temporal Extent
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2010-2025 | 2008-2010 | 1988-2014 | 2013-2014 |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | past time |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable | discrete |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | 1 |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Year |
EM Spatial Extent
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EM ID
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EM-345
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EM-660
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EM-788
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EM-862 |
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Bounding Type
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Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical |
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Spatial Extent Name
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Puli Township, Nantou County | HUCS in Michigan | Nachusa Grasslands | United States |
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Spatial Extent Area (Magnitude)
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100-1000 km^2 | 100,000-1,000,000 km^2 | 10-100 km^2 | >1,000,000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-345
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EM-660
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EM-788
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EM-862 |
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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 distributed (in at least some cases) |
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Spatial Grain Type
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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) | length, for linear feature (e.g., stream mile) |
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Spatial Grain Size
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40 m x 40 m | reach in HUC | Area varies by site | stream reach (site) |
EM Structure and Computation Approach
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EM ID
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EM-345
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EM-660
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EM-788
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EM-862 |
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EM Computational Approach
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Analytic | Numeric | Analytic | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-345
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EM-660
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EM-788
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EM-862 |
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Model Calibration Reported?
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Unclear | No | No | No |
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Model Goodness of Fit Reported?
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Not applicable | Yes | No | No |
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Goodness of Fit (metric| value | unit)
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None |
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None | None |
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Model Operational Validation Reported?
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Not applicable | No | No | No |
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Model Uncertainty Analysis Reported?
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No | No | No | No |
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Model Sensitivity Analysis Reported?
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No | No | No | No |
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Model Sensitivity Analysis Include Interactions?
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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])
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EM-345
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EM-660
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EM-788
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EM-862 |
Comment:Taiwan |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-345
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EM-660
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EM-788
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EM-862 |
| None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-345
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EM-660
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EM-788
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EM-862 |
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Centroid Latitude
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23.98 | 45.12 | 41.89 | 36.21 |
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Centroid Longitude
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120.96 | 85.18 | -89.34 | -113.76 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated | Provided | Estimated |
Environments and Scales Modeled
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EM ID
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EM-345
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EM-660
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EM-788
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EM-862 |
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EM Environmental Sub-Class
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Rivers and Streams | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Rivers and Streams | Agroecosystems | Grasslands | Rivers and Streams |
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Specific Environment Type
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Predominantly an agricultural area with associated forest land | stream reaches | Restored prairie, prairie remnants, and cropland | reach |
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EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-345
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EM-660
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EM-788
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EM-862 |
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EM Organismal Scale
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Community | Not applicable | Species | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
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EM-345
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EM-660
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EM-788
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EM-862 |
| None Available |
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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-345
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EM-660
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EM-788
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EM-862 |
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<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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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EM-345
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EM-660
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EM-788
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EM-862 |
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None |
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