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-99 | EM-106 | EM-374 | EM-379 | EM-449 |
EM-467 
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EM-593
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EM-632
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EM-895 |
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EM Short Name
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Landscape importance for crops, Europe | Value of Habitat for Shrimp, Campeche, Mexico | InVEST carbon storage and sequestration (v3.2.0) | VELMA soil temperature, Oregon, USA | Decrease in erosion (shoreline), St. Croix, USVI | Yasso07 v1.0.1, Switzerland | DayCent N2O flux simulation, Ireland | Waterfowl pairs, CREP wetlands, Iowa, USA | HWB indicator-College degree, Great Lakes, USA |
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EM Full Name
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Landscape importance for crop-based production, Europe | Value of Habitat for Shrimp, Campeche, Mexico | InVEST v3.2.0 Carbon storage and sequestration | VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | Decrease in erosion (shoreline) by reef, St. Croix, USVI | Yasso07 v1.0.1 forest litter decomposition, Switzerland | DayCent simulation N2O flux and climate change, Ireland | Waterfowl pairs, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Human well being indicator-College degree, Great Lakes waterfront, USA |
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EM Source or Collection
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EU Biodiversity Action 5 | None | InVEST | US EPA | US EPA | None | None | None | US EPA |
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EM Source Document ID
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228 | 227 | 315 | 317 | 335 | 343 | 358 | 372 |
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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Haines-Young, R., Potschin, M. and Kienast, F. | Barbier, E. B., and Strand, I. | The Natural Capital Project | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Didion, M., B. Frey, N. Rogiers, and E. Thurig | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick |
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Document Year
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2012 | 1998 | 2015 | 2013 | 2014 | 2014 | 2010 | 2010 | None |
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Document Title
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Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Valuing mangrove-fishery linkages: A case study of Campeche, Mexico | Carbon storage and sequestration - InVEST (v3.2.0) | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Validating tree litter decomposition in the Yasso07 carbon model | Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | 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 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 | Website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Journal manuscript submitted or in review |
Software and Access
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EM ID
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EM-99 | EM-106 | EM-374 | EM-379 | EM-449 |
EM-467
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EM-593
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EM-632
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EM-895 |
| Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | Not applicable | Not applicable | Not applicable | |
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Contact Name
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Marion Potschin | E.B. Barbier | The Natural Capital Project | Alex Abdelnour | Susan H. Yee |
Markus Didion ?Comment:Tel.: +41 44 7392 427 |
M. Abdalla | David Otis | Ted Angradi |
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Contact Address
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Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Environment Department, University of York, York YO1 5DD, UK | 371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | 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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marion.potschin@nottingham.ac.uk | Not reported | invest@naturalcapitalproject.org | abdelnouralex@gmail.com | yee.susan@epa.gov | markus.didion@wsl.ch | abdallm@tcd.ie | dotis@iastate.edu | tedangradi@gmail.com |
EM Description
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EM ID
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EM-99 | EM-106 | EM-374 | EM-379 | EM-449 |
EM-467
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EM-593
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EM-632
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EM-895 |
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Summary Description
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ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Crop-based production” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain." AUTHOR'S DESCRIPTION: "The analysis for "Crop-based production" maps all the areas that are important for food crops produced through commercial agriculture." | AUTHOR'S DESCRIPTION: "We assume throughout that shrimp harvesting occurs through open access management that yields production which is exported internationally, and we modify a standard open access fishery model to account explicitly for the effect of the mangrove area on carrying capacity and thus production.We derive the conditions determining the long-run equilibrium of the model, including the comparative static effects of a change in mangrove area, on this equilibrium. Through regressing a relationship between shrimp harvest, effort and mangrove area over time, we estimate parameters based on the combinations of the bioeconomic parameters of the model determining the comparative statics. By incorporating additional economic data, we are able to simulate an estimate of the effect of changes in mangrove area in Laguna de Terminos on the production and value of shrimp harvests in Campeche state." (153) | 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 used a new ecohydrological model, Visualizing Ecosystems for Land Management Assessments (VELMA), to analyze the effects of forest harvest on catchment carbon and nitrogen dynamics. We applied the model to a 10 ha headwater catchment in the western Oregon Cascade Range where two major disturbance events have occurred during the past 500 years: a stand-replacing fire circa 1525 and a clear-cut in 1975. Hydrological and biogeochemical data from this site and other Pacific Northwest forest ecosystems were used to calibrate the model. Model parameters were first calibrated to simulate the postfire buildup of ecosystem carbon and nitrogen stocks in plants and soil from 1525 to 1969, the year when stream flow and chemistry measurements were begun. Thereafter, the model was used to simulate old-growth (1969–1974) and postharvest (1975–2008) temporal changes in carbon and nitrogen dynamics…" AUTHOR'S DESCRIPTION: "The soil column model consists of three coupled submodels:...a soil temperature model [Cheng et al., 2010] that simulates daily soil layer temperatures from surface air temperature and snow depth by propagating the air temperature first through the snowpack and then through the ground using the analytical solution of the one-dimensional thermal diffusion equation" | 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: "...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." | Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios. | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "Number of duck pairs per site was estimated for 6 species of ducks: Mallard (Anas platyrhynchos), Blue-winged Teal (Anas discors), Northern Shoveler (Anas clypeata), Gadwall (Anas strepera), Northern Pintail (Anas acuta), and Wood Duck (Aix sponsa), using models developed by Cowardin et al. (1995). Pair abundance was based on wetland class (i.e., temporary, seasonal, semi-permanent, lake, or river), wetland size, and a set of species specific regression coefficients. All CREP wetlands were considered semi-permanent for this analysis; therefore only coefficients associated with the semipermanent wetland pair model were used in calculations. The general equation used to estimate the pairs per wetland was: Pairs = e (a + bx + α) * p where, e = mathematical constant ≈ 2.718, a = species specific regression coefficient a, b = species specific regression coefficient b, x = the natural log of wetland size, α = species specific alpha value, and p = proportion of the basin containing water (assumed to be 0.90 for this analysis)" | 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 | climate change | None identified | None identified |
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Biophysical Context
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No additional description provided | Gulf of Mexico; mangrove-lagoon system | Not applicable | Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter. | No additional description provided | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Prairie pothole region of north-central Iowa | 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 | Optional future scenarios for changed LULC and wood harvest | No scenarios presented | No scenarios presented |
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). |
air temperature, precipitation, Atmospheric CO2 concentrations | No scenarios presented | N/A |
EM Relationship to Other EMs or Applications
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EM ID
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EM-99 | EM-106 | EM-374 | EM-379 | EM-449 |
EM-467
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EM-593
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EM-632
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EM-895 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method Only | Method + Application | Method + Application |
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 (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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New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | Application of existing 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-99 | EM-106 | EM-374 | EM-379 | EM-449 |
EM-467
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EM-593
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EM-632
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EM-895 |
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Document ID for related EM
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Doc-231 | Doc-228 | None | Doc-309 | Doc-13 | Doc-317 | Doc-335 | Doc-342 | Doc-344 | None | None | Doc-422 |
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EM ID for related EM
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EM-119 | EM-120 | EM-121 | EM-162 | EM-164 | EM-165 | EM-122 | EM-123 | EM-124 | EM-125 | EM-166 | EM-170 | EM-171 | EM-185 | EM-319 | EM-349 | EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | EM-447 | EM-448 | EM-466 | EM-469 | EM-480 | EM-485 | EM-598 | EM-705 | EM-703 | EM-702 | EM-701 | EM-700 | EM-886 | EM-888 | EM-889 | EM-890 | EM-891 | EM-893 | EM-894 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-99 | EM-106 | EM-374 | EM-379 | EM-449 |
EM-467
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EM-593
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EM-632
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EM-895 |
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EM Temporal Extent
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2000 | 1980-1990 | Not applicable | 1969-2008 | 2006-2007, 2010 | 1993-2013 | 1961-1990 | 2002-2007 | 2022 |
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EM Time Dependence
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time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | future time | future time | Not applicable | future time | both | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | discrete | discrete | Not applicable | discrete | discrete | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | 1 | 1 | Not applicable | 1 | 1 | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Year | Year | Day | Not applicable | Year | Day | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-99 | EM-106 | EM-374 | EM-379 | EM-449 |
EM-467
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EM-593
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EM-632
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EM-895 |
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Bounding Type
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Geopolitical | Physiographic or Ecological | Not applicable | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical | Point or points | Multiple unrelated locations (e.g., meta-analysis) | Geopolitical |
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Spatial Extent Name
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The EU-25 plus Switzerland and Norway | Laguna de Terminos Mangrove system | Not applicable | H. J. Andrews LTER WS10 | Coastal zone surrounding St. Croix | Switzerland | Oak Park Research centre | CREP (Conservation Reserve Enhancement Program) wetland sites | Great Lakes waterfront |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100-1000 km^2 | Not applicable | 10-100 ha | 100-1000 km^2 | 10,000-100,000 km^2 | 1-10 ha | 1-10 km^2 | 1000-10,000 km^2. |
Spatial Distribution of Computations
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EM ID
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EM-99 | EM-106 | EM-374 | EM-379 | EM-449 |
EM-467
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EM-593
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EM-632
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EM-895 |
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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) ?Comment:See below, grain includes vertical, subsurface dimension. |
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) |
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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 | volume, for 3-D feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable |
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Spatial Grain Size
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1 km x 1 km | 1 km x 1 km | application specific | 30 m x 30 m surface pixel and 2-m depth soil column | 10 m x 10 m | 5 sites | Not applicable | multiple, individual, irregular shaped sites | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-99 | EM-106 | EM-374 | EM-379 | EM-449 |
EM-467
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EM-593
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EM-632
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EM-895 |
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EM Computational Approach
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Logic- or rule-based | Analytic | Analytic | Numeric | Analytic | Numeric | Numeric | Analytic | Numeric |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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Model Checking Procedures Used
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EM ID
em.detail.idHelp
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EM-99 | EM-106 | EM-374 | EM-379 | EM-449 |
EM-467
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EM-593
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EM-632
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EM-895 |
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Model Calibration Reported?
em.detail.calibrationHelp
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No | Yes | Not applicable | No | Yes | No | No | Unclear | No |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | Yes | Not applicable | No | No | No |
Yes ?Comment:for N2O fluxes |
No | No |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None |
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None | None | None | None |
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None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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Yes | No | Not applicable | No | Yes | Yes | Yes | Unclear | No |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | Yes | Not applicable | No | No | No | No | No | No |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | Yes | Not applicable | No | No | No | No | No | Yes |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Unclear | Not applicable | 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])
em.detail.relationToSpaceTerrestrialHelp
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| EM-99 | EM-106 | EM-374 | EM-379 | EM-449 |
EM-467
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EM-593
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EM-632
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EM-895 |
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None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
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| EM-99 | EM-106 | EM-374 | EM-379 | EM-449 |
EM-467
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EM-593
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EM-632
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EM-895 |
| None |
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None | None |
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None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-99 | EM-106 | EM-374 | EM-379 | EM-449 |
EM-467
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EM-593
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EM-632
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EM-895 |
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Centroid Latitude
em.detail.ddLatHelp
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50.53 | 18.61 | -9999 | 44.25 | 17.73 | 46.82 | 52.86 | 42.62 | 42.26 |
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Centroid Longitude
em.detail.ddLongHelp
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7.6 | -91.55 | -9999 | -122.33 | -64.77 | 8.23 | 6.54 | -93.84 | -87.84 |
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Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Estimated | Not applicable | Provided | Estimated | Estimated | Provided | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
em.detail.idHelp
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EM-99 | EM-106 | EM-374 | EM-379 | EM-449 |
EM-467
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EM-593
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EM-632
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EM-895 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Not applicable | Forests | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Inland Wetlands | Agroecosystems | Grasslands | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Tundra | Ice and Snow | Atmosphere |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Not applicable | Mangrove | Terrestrial environments, but not specified for methods | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | Coral reefs | forests | farm pasture | Wetlands buffered by grassland set in agricultural land | Lake Michigan & Lake Erie waterfront |
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EM Ecological Scale
em.detail.ecoScaleHelp
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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 | Not applicable | 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 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 |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
em.detail.nameOfOrgsOrGroupsHelp
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EM ID
em.detail.idHelp
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EM-99 | EM-106 | EM-374 | EM-379 | EM-449 |
EM-467
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EM-593
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EM-632
|
EM-895 |
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EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Community | Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-99 | EM-106 | EM-374 | EM-379 | EM-449 |
EM-467
|
EM-593
|
EM-632
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EM-895 |
| None Available |
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None Available | None Available | None Available | None Available | 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)
em.detail.cicesHelp
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| EM-99 | EM-106 | EM-374 | EM-379 | EM-449 |
EM-467
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EM-593
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EM-632
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EM-895 |
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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-99 | EM-106 | EM-374 | EM-379 | EM-449 |
EM-467
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EM-593
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EM-632
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EM-895 |
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None | None |
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None |
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None |