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-79 | EM-81 |
EM-127 
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EM-374 |
EM-467
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EM-891 |
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EM Short Name
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Divergence in flowering date, Central French Alps | Cultural ES and plant traits, Central French Alps | Annual profit - carbon plantings, South Australia | InVEST carbon storage and sequestration (v3.2.0) | Yasso07 v1.0.1, Switzerland | Home ownership, Great Lakes, USA |
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EM Full Name
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Functional divergence in flowering date, Central French Alps | Cultural ecosystem service estimated from plant functional traits, Central French Alps | Annual profit from carbon plantings, South Australia | InVEST v3.2.0 Carbon storage and sequestration | Yasso07 v1.0.1 forest litter decomposition, Switzerland | Human well being indicator - home ownership, Great Lakes waterfront, USA |
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EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | InVEST | None | US EPA |
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EM Source Document ID
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260 | 260 | 243 | 315 | 343 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
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Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Crossman, N. D., Bryan, B. A., and Summers, D. M. | The Natural Capital Project | Didion, M., B. Frey, N. Rogiers, and E. Thurig | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick |
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Document Year
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2011 | 2011 | 2011 | 2015 | 2014 | None |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Carbon payments and low-cost conservation | Carbon storage and sequestration - InVEST (v3.2.0) | Validating tree litter decomposition in the Yasso07 carbon model | Human well-being and natural capital indictors for Great Lakes waterfront revitalization |
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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 | Peer reviewed but unpublished (explain in Comment) |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Website | Published journal manuscript | Journal manuscript submitted or in review |
Software and Access
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EM ID
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EM-79 | EM-81 |
EM-127
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EM-374 |
EM-467
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EM-891 |
| Not applicable | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | Not applicable | |
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Contact Name
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Sandra Lavorel | Sandra Lavorel | Neville D. Crossman | The Natural Capital Project |
Markus Didion ?Comment:Tel.: +41 44 7392 427 |
Ted Angradi |
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Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia | 371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | neville.crossman@csiro.au | invest@naturalcapitalproject.org | markus.didion@wsl.ch | tedangradi@gmail.com |
EM Description
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EM ID
em.detail.idHelp
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EM-79 | EM-81 |
EM-127
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EM-374 |
EM-467
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EM-891 |
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Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Functional divergence of flowering date was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The Cultural ecosystem service map was a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to cultural ecosystem services were based on stakeholders’ perceptions, given positive or negative contributions." | 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." | Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. ABSTRACT: "Terrestrial ecosystems, which store more carbon than the atmosphere, are vital to influencing carbon dioxide-driven climate change. The InVEST model uses maps of land use and land cover types and data on wood harvest rates, harvested product degradation rates, and stocks in four carbon pools (aboveground biomass, belowground biomass, soil, dead organic matter) to estimate the amount of carbon currently stored in a landscape or the amount of carbon sequestered over time. Additional data on the market or social value of sequestered carbon and its annual rate of change, and a discount rate can be used in an optional model that estimates the value of this environmental service to society. Limitations of the model include an oversimplified carbon cycle, an assumed linear change in carbon sequestration over time, and potentially inaccurate discounting rates." AUTHOR'S DESCRIPTION: "A fifth optional pool included in the model applies to parcels that produce harvested wood products (HWPs) such as firewood or charcoal or more long-lived products such as house timbers or furniture. Tracking carbon in this pool is useful because it represents the amount of carbon kept from the atmosphere by a given product." | ABSTRACT: "...We examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to (i) examine the effect of the application of three different published Yasso07 parameter sets on simulated decay rate; (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests;…" AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root litter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The result of the simulation was an annual estimate of the remaining fraction of the initial mass, which could then be compared with observed data." | ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context. " |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified | None identified | None identified |
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Biophysical Context
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Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | 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. | Not applicable | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | Waterfront districts on south Lake Michigan and south lake Erie |
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EM Scenario Drivers
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No scenarios presented | 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 | Optional future scenarios for changed LULC and wood harvest |
No scenarios presented ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
N/A |
EM Relationship to Other EMs or Applications
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EM ID
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EM-79 | EM-81 |
EM-127
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EM-374 |
EM-467
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EM-891 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application |
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 Only |
Method + Application (multiple runs exist) View EM Runs ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
Method + Application |
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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 | Application of existing 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-79 | EM-81 |
EM-127
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EM-374 |
EM-467
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EM-891 |
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Document ID for related EM
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Doc-260 | Doc-269 | None | Doc-245 | Doc-246 | Doc-247 | Doc-243 | Doc-309 | Doc-342 | Doc-344 | Doc-422 |
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EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-82 | EM-83 | EM-128 | EM-141 | EM-349 | EM-466 | EM-469 | EM-480 | EM-485 | EM-886 | EM-888 | EM-889 | EM-890 | EM-893 | EM-894 | EM-895 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-79 | EM-81 |
EM-127
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EM-374 |
EM-467
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EM-891 |
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EM Temporal Extent
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2007-2008 | Not reported | 2009-2050 | Not applicable | 1993-2013 | 2022 |
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EM Time Dependence
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time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | future time | future time | future time | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | discrete | discrete | discrete | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | 1 | 1 | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Year | Year | Year | Not applicable |
EM Spatial Extent
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EM ID
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EM-79 | EM-81 |
EM-127
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EM-374 |
EM-467
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EM-891 |
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Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Not applicable | Geopolitical | Geopolitical |
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Spatial Extent Name
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Central French Alps | Central French Alps | Agricultural districts of the state of South Australia | Not applicable | Switzerland | Great Lakes waterfront |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | Not applicable | 10,000-100,000 km^2 | 1000-10,000 km^2. |
Spatial Distribution of Computations
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EM ID
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EM-79 | EM-81 |
EM-127
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EM-374 |
EM-467
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EM-891 |
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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) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable |
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Spatial Grain Size
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20 m x 20 m | 20 m x 20 m | 1 ha x 1 ha | application specific | 5 sites | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-79 | EM-81 |
EM-127
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EM-374 |
EM-467
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EM-891 |
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EM Computational Approach
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Analytic | Analytic | Analytic | Analytic | Numeric | Numeric |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | stochastic | 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-79 | EM-81 |
EM-127
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EM-374 |
EM-467
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EM-891 |
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Model Calibration Reported?
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No | No | No | Not applicable | No | No |
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Model Goodness of Fit Reported?
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Yes | No | No | Not applicable | No | No |
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Goodness of Fit (metric| value | unit)
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None | None | None | None | None |
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Model Operational Validation Reported?
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No | No | No | Not applicable | Yes | No |
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Model Uncertainty Analysis Reported?
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No | No | No | Not applicable | No | No |
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Model Sensitivity Analysis Reported?
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No | No | No | Not applicable | No | Yes |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| EM-79 | EM-81 |
EM-127
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EM-374 |
EM-467
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EM-891 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-79 | EM-81 |
EM-127
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EM-374 |
EM-467
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EM-891 |
| None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-79 | EM-81 |
EM-127
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EM-374 |
EM-467
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EM-891 |
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Centroid Latitude
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45.05 | 45.05 | -34.9 | -9999 | 46.82 | 42.26 |
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Centroid Longitude
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6.4 | 6.4 | 138.7 | -9999 | 8.23 | -87.84 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 |
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Centroid Coordinates Status
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Provided | Provided | Estimated | Not applicable | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-79 | EM-81 |
EM-127
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EM-374 |
EM-467
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EM-891 |
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Not applicable | Forests | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Terrestrial environments, but not specified for methods | forests | Lake Michigan & Lake Erie waterfront |
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EM Ecological Scale
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Ecological scale is coarser 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 | Not applicable | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to 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-79 | EM-81 |
EM-127
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EM-374 |
EM-467
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EM-891 |
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EM Organismal Scale
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Community | Community | Guild or Assemblage | Not applicable | Community | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-79 | EM-81 |
EM-127
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EM-374 |
EM-467
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EM-891 |
| None Available | None Available |
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None Available | None Available | 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)
em.detail.cicesHelp
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| EM-79 | EM-81 |
EM-127
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EM-374 |
EM-467
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EM-891 |
| None |
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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>
fegs1Help
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
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| EM-79 | EM-81 |
EM-127
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EM-374 |
EM-467
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EM-891 |
| None | None | None | None | None | None |