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-71 | EM-124 |
EM-363 
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EM-379 | EM-647 |
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EM Short Name
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Community flowering date, Central French Alps | Land-use change and habitat diversity, Europe | InVEST (v1.004) water purification, Indonesia | VELMA soil temperature, Oregon, USA | EcoAIM v.1.0 APG, MD |
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EM Full Name
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Community weighted mean flowering date, Central French Alps | Land-use change effects on habitat diversity, Europe | InVEST (Integrated Valuation of Environmental Services and Tradeoffs v1.004) water purification (nutrient retention), Sumatra, Indonesia | VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | EcoAIM v.1.0, Aberdeen Proving Ground, MD |
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EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | InVEST | US EPA | None |
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EM Source Document ID
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260 | 228 | 309 | 317 | 374 |
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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. | Haines-Young, R., Potschin, M. and Kienast, F. | Bhagabati, N. K., Ricketts, T., Sulistyawan, T. B. S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A., Tallis, H., and Wolney, S. | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Booth, P., Law, S. , Ma, J. Turnley, J., and J.W. Boyd |
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Document Year
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2011 | 2012 | 2014 | 2013 | 2014 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Ecosystem services reinforce Sumatran tiger conservation in land use plans | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | Implementation of EcoAIM - A Multi-Objective Decision Support Tool for Ecosystem Services at Department of Defense Installations |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report |
Software and Access
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EM ID
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EM-71 | EM-124 |
EM-363
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EM-379 | EM-647 |
| 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 | |
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Contact Name
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Sandra Lavorel | Marion Potschin | Nirmal K. Bhagabati | Alex Abdelnour | Pieter Booth |
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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 | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | Exponent Inc., Bellevue WA |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | marion.potschin@nottingham.ac.uk | nirmal.bhagabati@wwfus.org | abdelnouralex@gmail.com | pbooth@ramboll.com |
EM Description
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EM ID
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EM-71 | EM-124 |
EM-363
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EM-379 | EM-647 |
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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." AUTHOR'S DESCRIPTION: "Community-weighted mean date of flowering onset 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: "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 novel aspect of this work is an analysis of whether the historical and the projected land use changes...are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Habitat diversity); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000." AUTHOR'S DESCRIPTION: "The analysis for the regulating service “Habitat diversity” seeks to identify all the areas with potential to support biodiversity…The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers." | 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: "...Here we use simple spatial analyses on readily available datasets to compare the distribution of five ecosystem services with tiger habitat in central Sumatra. We assessed services and habitat in 2008 and the changes in these variables under two future scenarios: a conservation-friendly Green Vision, and a Spatial Plan developed by the Indonesian government..." AUTHOR'S DESCRIPTION: "We used a modeling tool, InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004; Tallis et al., 2010), to map and quantify tiger habitat quality and five ecosystem services. InVEST maps ecosystem services and the quality of species habitat as production functions of LULC using simple biophysical models. Models were parameterized using data from regional agencies, literature surveys, global databases, site visits and prior field experience (Table 1)... Our nutrient retention model estimates nitrogen and phosphorus loading (kg y^-1), leading causes of water pollution from fertilizer application and other activities, using the export coefficient approach of Reckhow et al. (1980). The model routes nutrient runoff from each land parcel downslope along the flow path, with some of the nutrient that originated upstream being retained by the parcel according to its retention efficiency. For assessing variation within the same LULC map (2008 and each scenario), we compared sediment and nutrient retention across the landscape. However, for assessing change to scenarios, we compared sediment and nutrient export between the relevant LULC maps, as the change in export (rather than in retention) better reflects the change in service experienced downstream. ...Although InVEST reports ecosystem services in biophysical units, its simple models are best suited to understanding broad patterns of spatial variation (Tallis and Polasky, 2011), rather than for precise quantification. Additionally, we lacked field measurements against which to calibrate our outputs. Therefore, we focused on relative spatial distribution across the landscape, and relative change to scenarios." | 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: "This report describes the demonstration of the EcoAIM decision support framework and GIS-based tool. EcoAIM identifies and quantifies the ecosystem services provided by the natural resources at the Aberdeen Proving Ground (APG). A structured stakeholder process determined the mission and non-mission priorities at the site, elicited the natural resource management decision process, identified the stakeholders and their roles, and determine the ecosystem services of priority that impact missions and vice versa. The EcoAIM tool was customized to quantify in a geospatial context, five ecosystem services – vista aesthetics, landscape aesthetics, recreational opportunities, habitat provisioning for biodiversity and nutrient sequestration. The demonstration included a Baseline conditions quantification of ecosystem services and the effects of a land use change in the Enhanced Use Lease parcel in cantonment area (Scenario 1). Biodiversity results ranged widely and average scores decreased by 10% after Scenario 1. Landscape aesthetics scores increased by 10% after Scenario 1. Final scores did not change for recreation or nutrient sequestration because scores were outside the boundaries of the baseline condition. User feedback after the demonstration indicated positive reviews of EcoAIM as being useful and usable for land use decisions and particularly for use as a communication tool. " |
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Specific Policy or Decision Context Cited
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None identified | None identified | This analysis provided input to government-led spatial planning and strategic environmental assessments in the study area. This region contains some of the last remaining forest habitat of the critically endangered Sumatran tiger, Panthera tigris sumatrae. | None identified | None reported |
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Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | No additional description provided | Six watersheds in central Sumatra covering portions of Riau, Jambi and West Sumatra provinces. The Barisan mountain range comprises the western edge of the watersheds, while peat swamps predominate in the east. | 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. | Chesapeake bay coastal plain, elev. 60ft. |
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EM Scenario Drivers
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No scenarios presented | Recent historical land use change from 1990-2000 | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | No scenarios presented | N/A |
EM Relationship to Other EMs or Applications
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EM ID
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EM-71 | EM-124 |
EM-363
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EM-379 | EM-647 |
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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 | Method + Application | Method + Application |
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New or Pre-existing EM?
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New or revised model | New or revised model | Application of existing 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-71 | EM-124 |
EM-363
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EM-379 | EM-647 |
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Document ID for related EM
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Doc-260 | Doc-269 | Doc-238 | Doc-239 | Doc-240 | Doc-241 | Doc-242 | Doc-228 | Doc-338 | Doc-205 | Doc-13 | Doc-317 | None |
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EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-122 | EM-123 | EM-125 | EM-162 | EM-164 | EM-165 | EM-166 | EM-170 | EM-171 | EM-99 | EM-119 | EM-120 | EM-121 | EM-438 | EM-112 | EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-71 | EM-124 |
EM-363
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EM-379 | EM-647 |
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EM Temporal Extent
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2007-2008 | 1990-2000 | 2008-2020 | 1969-2008 | 2014 |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | future time | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable | discrete | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Day | Not applicable |
EM Spatial Extent
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EM ID
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EM-71 | EM-124 |
EM-363
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EM-379 | EM-647 |
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Bounding Type
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Physiographic or Ecological | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Geopolitical |
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Spatial Extent Name
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Central French Alps | The EU-25 plus Switzerland and Norway | central Sumatra | H. J. Andrews LTER WS10 | Aberdeen Proving Ground |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 10-100 ha | 100-1000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-71 | EM-124 |
EM-363
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EM-379 | EM-647 |
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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) ?Comment:500m x 500m is also used for some computations. The evaluation does include some riparian buffers which are linear features along streams. |
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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 |
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Spatial Grain Size
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20 m x 20 m | 1 km x 1 km | 30 m x 30 m | 30 m x 30 m surface pixel and 2-m depth soil column | 100m x 100m |
EM Structure and Computation Approach
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EM ID
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EM-71 | EM-124 |
EM-363
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EM-379 | EM-647 |
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EM Computational Approach
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Analytic | Logic- or rule-based | Analytic | Numeric | Numeric |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-71 | EM-124 |
EM-363
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EM-379 | EM-647 |
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Model Calibration Reported?
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No | No | No | No |
No ?Comment:Nutrient sequestion submodel ( EPA's P8 model has been long used) |
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Model Goodness of Fit Reported?
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Yes | No | No | No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None | None | None |
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Model Operational Validation Reported?
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No | No | No | No | No |
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Model Uncertainty Analysis Reported?
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No | No | No | No | No |
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Model Sensitivity Analysis Reported?
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No | No | No | No |
Unclear ?Comment:Just cannot tell, but no mention of sensitivity was made. |
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Model Sensitivity Analysis Include Interactions?
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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-71 | EM-124 |
EM-363
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EM-379 | EM-647 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-71 | EM-124 |
EM-363
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EM-379 | EM-647 |
| None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-71 | EM-124 |
EM-363
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EM-379 | EM-647 |
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Centroid Latitude
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45.05 | 50.53 | 0 | 44.25 | 39.46 |
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Centroid Longitude
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6.4 | 7.6 | 102 | -122.33 | 76.12 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Provided | Estimated | Provided | Provided | Estimated |
Environments and Scales Modeled
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EM ID
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EM-71 | EM-124 |
EM-363
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EM-379 | EM-647 |
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Forests | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Forests | Created Greenspace | Grasslands | Scrubland/Shrubland |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows. | Not applicable | 104 land use land cover classes | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | Coastal Plain |
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EM Ecological Scale
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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 is finer than that of 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-71 | EM-124 |
EM-363
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EM-379 | EM-647 |
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EM Organismal Scale
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Community | Not applicable | Community | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-71 | EM-124 |
EM-363
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EM-379 | EM-647 |
| None Available | None Available | 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-71 | EM-124 |
EM-363
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EM-379 | EM-647 |
| None |
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None |
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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>
fegs1Help
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-71 | EM-124 |
EM-363
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EM-379 | EM-647 |
| None | None | None | None |
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