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-327 | EM-379 | EM-629 | EM-837 |
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EM Short Name
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ARIES sediment regulation, Puget Sound Region, USA | VELMA soil temperature, Oregon, USA | SolVES, Pike & San Isabel NF, WY | Bird species diversity on restored landfills, UK |
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EM Full Name
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ARIES (Artificial Intelligence for Ecosystem Services) Sediment Regulation for Reservoirs, Puget Sound Region, Washington, USA | VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | SolVES, Social Values for Ecosystem Services, Pike and San Isabel National Forest, CO | Bird species diversity on restored landfills compared to paired reference sites, East Midlands, UK |
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EM Source or Collection
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ARIES | US EPA | None | None |
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EM Source Document ID
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302 | 317 | 369 | 406 |
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Document Author
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Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | Rahman, M. L., S. Tarrant, D. McCollin, and J. Ollerton |
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Document Year
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2014 | 2013 | 2014 | 2011 |
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Document Title
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From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | The conservation value of restored landfill sites in the East Midlands, UK for supporting bird communities in the East Midlands, UK for supporting bird communities |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-327 | EM-379 | EM-629 | EM-837 |
| http://aries.integratedmodelling.org/ | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable | Not applicable | |
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Contact Name
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Ken Bagstad | Alex Abdelnour | Benson Sherrouse | Lutfor Rahman |
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Contact Address
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Geosciences and Environmental Change Science Center, US Geological Survey | Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | Landscape and Biodiversity Research Group, School of Science and Technology, The University of Northampton, Avenue Campus, Northampton NN2 6JD, UK |
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Contact Email
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kjbagstad@usgs.gov | abdelnouralex@gmail.com | bcsherrouse@usgs.gov | lutfor.rahman@northampton.ac.uk |
EM Description
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EM ID
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EM-327 | EM-379 | EM-629 | EM-837 |
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Summary Description
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ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "We mapped sediment regulation as the location of sediment sinks (depositional areas in floodplains), which can absorb sediment transported by hydrologic flows from upstream sources (erosionprone areas) prior to reaching users. In this case the benefit of avoided sedimentation is provided to 29 major reservoirs. Avoided sedimentation helps maintain the ability of reservoirs to provide benefits including hydroelectric power generation, flood control, recreation, and water supply to beneficiaries through the region. Avoided reservoir sedimentation likely helps to protect each of these benefits in different ways, i.e., increased turbidity or the loss of reservoir storage capacity may have a greater impact on some provision of some benefit types than others. For our purposes we ended the modeling and mapping exercise at the reservoirs. Reservoir sedimentation reduces their storage capacity, typically decreasing their ability to provide these benefits without costly dredging. We thus used a probabilistic Bayesian model of soil erosion incorporating vegetation, soils, and rainfall influences and calibrated using regional data from coarser scale and/or RUSLE derived erosion models (Bagstad et al. 2011). We probabilistically modeled sediment deposition in floodplains using data for floodplain vegetation, floodplain width, and stream gradient, which can influence rates of deposition. We calculated the ratio of actual to theoretical sediment regulation using the aggregated sink values upstream of reservoirs in the Puget Sound region, divided by aggregated theoretical sink values for the entire landscape." | 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: " "Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders.With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems. | ABSTRACT: "There has been a rapid decline of grassland bird species in the UK over the last four decades. In order to stem declines in biodiversity such as this, mitigation in the form of newly created habitat and restoration of degraded habitats is advocated in the UK biodiversity action plan. One potential restored habitat that could support a number of bird species is re-created grassland on restored landfill sites. However, this potential largely remains unexplored. In this study, birds were counted using point sampling on nine restored landfill sites in the East Midlands region of the UK during 2007 and 2008. The effects of restoration were investigated by examining bird species composition, richness, and abundance in relation to habitat and landscape structure on the landfill sites in comparison to paired reference sites of existing wildlife value. Twelve bird species were found in total and species richness and abundance on restored landfill sites was found to be higher than that of reference sites. Restored landfill sites support both common grassland bird species and also UK Red List bird species such as skylark Alauda arvensis, grey partridge Perdix perdix, lapwing Vanellus vanellus, tree sparrow, Passer montanus, and starling Sturnus vulgaris. Size of the site, percentage of bare soil and amount of adjacent hedgerow were found to be the most influential habitat quality factors for the distribution of most bird species. Presence of open habitat and crop land in the surrounding landscape were also found to have an effect on bird species composition. Management of restored landfill sites should be targeted towards UK Red List bird species since such sites could potentially play a significant role in biodiversity action planning." AUTHOR'S DESCRIPTION: "Mean number of birds from multiple visits were used for data analysis. To analyse the data generalized linear models (GLMs) were constructed to compare local habitat and landscape parameters affecting different species, and to establish which habitat and landscape characteristics explained significant changes in the frequency of occurrence for each species. To ensure analyses focused on resident species, habitat associations were modelled for those seven bird species which were recorded at least three times in the surveys. The analysis was carried out with the software R (R Development Core Team 2003). Nonsignificant predictors (independent variables) were removed in a stepwise manner (least significant factor first). For distribution pattern of bird species, data were initially analysed using detrended correspondence analysis. Redundancy analysis (RDA) was performed on the same data using CANOCO for Windows version 4.0 (ter Braak and Smilauer 2002)." |
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Specific Policy or Decision Context Cited
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None identified | None identified | None | None identified |
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Biophysical Context
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No additional description provided | 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. | Rocky mountain conifer forests | The study area covered mainly Northamptonshire and parts of Bedfordshire, Buckinghamshire and Warwickshire, ranging from 51o58’44.74” N to 52o26’42.18” N and 0o27’49.94” W to 1o19’57.67” W. This region has countryside of low, undulating hills separated by valleys and lies entirely within the great belt of scarplands formed by rocks of Jurassic age which stretch across England from Yorkshire to Dorset (Beaver 1943; Sutherland 1995; Wilson 1995). |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | N/A | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-327 | EM-379 | EM-629 | EM-837 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method Only |
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New or Pre-existing EM?
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New or revised 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-327 | EM-379 | EM-629 | EM-837 |
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Document ID for related EM
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Doc-303 | Doc-305 | Doc-13 | Doc-317 | Doc-369 | Doc-406 |
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EM ID for related EM
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None | EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | EM-626 | EM-628 | EM-836 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-327 | EM-379 | EM-629 | EM-837 |
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EM Temporal Extent
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1971-2005 | 1969-2008 | 2004-2008 | Not applicable |
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EM Time Dependence
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time-stationary | time-dependent | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | future time | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | discrete | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Day | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-327 | EM-379 | EM-629 | EM-837 |
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Bounding Type
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Physiographic or ecological | Watershed/Catchment/HUC | Geopolitical | Not applicable |
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Spatial Extent Name
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Puget Sound Region | H. J. Andrews LTER WS10 | National Park | Not applicable |
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Spatial Extent Area (Magnitude)
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10,000-100,000 km^2 | 10-100 ha | 1000-10,000 km^2. | Not applicable |
Spatial Distribution of Computations
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EM ID
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EM-327 | EM-379 | EM-629 | EM-837 |
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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) ?Comment:See below, grain includes vertical, subsurface dimension. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
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area, for pixel or radial feature | volume, for 3-D feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) |
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Spatial Grain Size
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200m x 200m | 30 m x 30 m surface pixel and 2-m depth soil column | 30m2 | multiple unrelated sites |
EM Structure and Computation Approach
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EM ID
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EM-327 | EM-379 | EM-629 | EM-837 |
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EM Computational Approach
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Analytic | Numeric | Numeric | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-327 | EM-379 | EM-629 | EM-837 |
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Model Calibration Reported?
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Yes | No | No | Not applicable |
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Model Goodness of Fit Reported?
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No | No | Yes | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None |
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None |
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Model Operational Validation Reported?
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No | No | No | Not applicable |
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Model Uncertainty Analysis Reported?
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No | No | No | Not applicable |
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Model Sensitivity Analysis Reported?
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No | No | No | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| EM-327 | EM-379 | EM-629 | EM-837 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-327 | EM-379 | EM-629 | EM-837 |
| None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-327 | EM-379 | EM-629 | EM-837 |
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Centroid Latitude
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48 | 44.25 | 38.7 | Not applicable |
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Centroid Longitude
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-123 | -122.33 | 105.89 | Not applicable |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | Not applicable |
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Centroid Coordinates Status
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Estimated | Provided | Estimated | Not applicable |
Environments and Scales Modeled
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EM ID
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EM-327 | EM-379 | EM-629 | EM-837 |
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EM Environmental Sub-Class
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Rivers and Streams | Lakes and Ponds | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Forests | Forests | Created Greenspace | Grasslands |
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Specific Environment Type
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Terrestrial environment surrounding a large estuary | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | Montain forest | restored landfills and conserved grasslands |
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EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of 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-327 | EM-379 | EM-629 | EM-837 |
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EM Organismal Scale
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Not applicable | Not applicable | Not applicable | Individual or population, within a species |
Taxonomic level and name of organisms or groups identified
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| EM-327 | EM-379 | EM-629 | EM-837 |
| None Available | None Available | None Available |
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EnviroAtlas URL
| EM-327 | EM-379 | EM-629 | EM-837 |
| GAP Ecological Systems, Average Annual Precipitation, Waterbody area | Average Annual Precipitation | GAP Ecological Systems, Enabling Conditions | None Available |
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
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| EM-327 | EM-379 | EM-629 | EM-837 |
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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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-327 | EM-379 | EM-629 | EM-837 |
| None | None |
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None |