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-92 | EM-121 | EM-194 |
EM-369 
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EM-485
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EM-842 |
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EM Short Name
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Runoff potential of pesticides, Europe | Landscape importance for recreation, Europe | Coral and land development, St.Croix, VI, USA | Envision, Puget Sound, WA, USA | Yasso07 v1.0.1, Switzerland, site level | Horned lark abundance, Piedmont region, USA |
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EM Full Name
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Runoff potential of pesticides, Europe | Landscape importance for recreation, Europe | Coral colony density and land development, St.Croix, Virgin Islands, USA | Envision, Puget Sound, WA, USA | Yasso07 v1.0.1 forest litter decomposition, Switzerland, site level | Horned lark abundance, Piedmont ecoregion, USA |
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EM Source or Collection
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None | EU Biodiversity Action 5 | US EPA | Envision | None | None |
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EM Source Document ID
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254 | 228 | 96 |
313 ?Comment:Doc 314 is a secondary source. It is a webpage guide intended to provide support for developing an application using ENVISION. |
343 | 405 |
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Document Author
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Schriever, C. A., and Liess, M. | Haines-Young, R., Potschin, M. and Kienast, F. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Bolte, J. and Vache, K. | Didion, M., B. Frey, N. Rogiers, and E. Thurig | Riffel, S., Scognamillo, D., and L. W. Burger |
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Document Year
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2007 | 2012 | 2011 | 2010 | 2014 | 2008 |
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Document Title
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Mapping ecological risk of agricultural pesticide runoff | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Envisioning Puget Sound Alternative Futures: PSNERP Final Report | Validating tree litter decomposition in the Yasso07 carbon model | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Documentation is 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 report | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-92 | EM-121 | EM-194 |
EM-369
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EM-485
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EM-842 |
| Not applicable | Not applicable | Not applicable | http://envision.bioe.orst.edu | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | Not applicable | |
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Contact Name
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Carola Alexandra Schriever | Marion Potschin | Leah Oliver |
John Bolte ?Comment:Phone# 541-737-2041 |
Markus Didion | Sam Riffell |
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Contact Address
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Helmholtz Centre for Environmental Research - UFZ, Department of System Ecotoxicology, Permoserstrasse 15, 04318 Leipzig, Germany | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | National Health and Environmental Research Effects Laboratory | Oregon State University, Dept. of Biological & Ecological Engineering, 116C Gilmore Hall, Corvallis, OR 97333 | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA |
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Contact Email
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carola.schriever@ufz.de | marion.potschin@nottingham.ac.uk | leah.oliver@epa.gov | boltej@engr.orst.edu | markus.didion@wsl.ch | sriffell@cfr.msstate.edu |
EM Description
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EM ID
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EM-92 | EM-121 | EM-194 |
EM-369
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EM-485
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EM-842 |
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Summary Description
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ABSTRACT: "The approach is based on the runoff potential (RP) of stream sites, by a spatially explicit calculation based on pesticide use, precipitation, topography, land use and soil characteristics in the near-stream environment. The underlying simplified model complies with the limited availability and resolution of data at larger scales." AUTHOR'S DESCRIPTION: "The RP is based on a mathematical model that describes runoff losses of a compound with generalized properties and which was developed from a proposal by the Organisation for Economic Co-operation and Development (OECD) for estimating dissolved runoff inputs of a pesticide into surface waters (OECD, 1998)...The runoff model underlying RP calculates the dissolved amount of a generic substance that was applied in the near environment of a stream site and that is expected to reach the stream site during one rainfall event. The dissolved amount results from a single application in the near-stream environment (i.e., a two-sided 100-m stream corridor extending for 1500 m upstream of the site) and is the amount of applied substance in the designated corridor reduced due to the influence of the site-specific key environmental factors precipitation, soil characteristics, topography, and plant interception." | 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 … “Recreation” ... The potential to deliver services is assumed to be influenced by land-use ... and bioclimatic and landscape properties such as mountainous terrain, adjacency to coastal and wetland ecosystems, as well as adjacency to landscape protection zones." AUTHOR'S DESCRIPTION: "Recreation… is broadly defined as all areas where landscape properties are favourable for active recreation purposes." | AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | SUMMARY: "...the Puget Sound Nearshore Ecosystem Restoration Project, completed an analysis of alternative future regional trajectories of landscape change for the Puget Sound region. This effort developed three scenarios of change: 1) Status Quo, reflecting a continuation of current trends in the region, 2) Managed Growth, reflecting the adoption of an aggressive set of land use management policies focusing on protecting and restoring ecosystem function and concentrating growth within Urban Growth Areas (UGA) and near regional growth centers, and 3) Unmanaged Growth, reflecting a relaxation of land use restrictions with limited protection of ecosystem functions. Analyses assumed a fixed population growth rate across all three scenarios, defined by the Washington Office of Financial Management county level growth estimates. Scenarios were generated using a spatially- and temporally-explicit alternative futures analysis model, Envision, previously developed by Oregon State University researchers. The model accepts as input a vector-based representation of the landscape and associated datasets describing relevant landscape characteristics, descriptors of various processes influencing landscape change, and a set of policies, or decision alternatives, which reflect scenario-specific land management alternatives. The model generates 1) a set of spatial coverages (maps) reflecting scenario outcomes of a variety of landscape variables, most notably land use/land cover, shoreline modifications, and population projections, and 2) a set of summary statistics describing landscape change variables summarized across spatial reporting units. Analyses were run on each of such sub-basins in the Puget Sound, and aggregated to providing Sound-wide results. This information is being used by PSNERP to project future impairment of ecosystem functions, goods, and services. The Puget Sound Nearshore Ecosystem project data also provide inputs to calculate aspects of future nearshore process degradation. Impairment and degradation are primary factors being used to define future conditions for the PSNERP General Investigation Study." AUTHOR'S DESCRIPTION: "In this report, we document the application of an alternative futures analysis framework that incorporates these capabilities to the analysis of alternative future trajectories in the Puget Sound region. This framework, Envision (Bolte et al, 2007; Hulse et al. 2008) is a spatially and temporally explicit, standards-based, open source toolset specifically designed to facilitate alternative futures analyses. It employs a multiagent-based modeling approach that contains a robust capability for defining alternative management strategies and scenarios, incorporating a variety of landscape change processes, and creating maps of alternative landscape trajectories, expressed though a variety of metrics defined in an application-specific way." ABOUT ENVISION (ENVISION WEBSITE): "Central to Envision, and conceived at the s | 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... (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests; and (iii) evaluate the suitability of Yasso07 for regional and national scale applications 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)." "The decomposition of below- and aboveground litter was studied over 10 years on five forest sites in Switzerland…" "At the time of this study, three parameter sets have been developed and published:... (3): Rantakari et al., 2012 (henceforth P12)… For the development of P12, Rantakari et al. (2012) obtained a subset of the previously used data which was restricted to European sites." "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 lit-ter (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 r | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds." |
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Specific Policy or Decision Context Cited
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European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | None identified | Not applicable | None identified | None identified | None reported |
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Biophysical Context
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Not applicable | No additional description provided | nearshore; <1.5 km offshore; <12 m depth | No additional description provided | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | Conservation Reserve Program lands left to go fallow |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | Not applicable | Alternative future land management strategies (status quo, managed growth, unmanaged growth) | No scenarios presented | N/A |
EM Relationship to Other EMs or Applications
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EM ID
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EM-92 | EM-121 | EM-194 |
EM-369
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EM-485
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EM-842 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs |
Method + Application (multiple runs exist) View EM Runs ?Comment:Model runs are for different sites (Beatenberg, Vordemwald, Bettlachstock, Schanis, and Novaggio) differentiated by climate and forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). |
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 | 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-92 | EM-121 | EM-194 |
EM-369
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EM-485
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EM-842 |
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Document ID for related EM
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Doc-255 | Doc-256 | Doc-257 | Doc-231 | Doc-228 | None |
Doc-314 | Doc-47 ?Comment:Doc 314 is a secondary source. It is a webpage guide intended to provide support for developing an application using ENVISION. |
Doc-342 | Doc-343 | Doc-405 |
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EM ID for related EM
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None | EM-99 | EM-119 | EM-120 | EM-162 | EM-164 | EM-165 | EM-122 | EM-123 | EM-124 | EM-125 | EM-170 | EM-171 | None | EM-12 | EM-333 | EM-466 | EM-467 | EM-469 | EM-480 | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-843 | EM-844 | EM-845 | EM-846 | EM-847 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-92 | EM-121 | EM-194 |
EM-369
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EM-485
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EM-842 |
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EM Temporal Extent
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2000 | 2000 | 2006-2007 | 2000-2060 | 2000-2010 | 2008 |
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EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | future time | future time | Not applicable |
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EM Time Continuity
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discrete | Not applicable | Not applicable | discrete | discrete | Not applicable |
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EM Temporal Grain Size Value
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1 | Not applicable | Not applicable | 1 | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Day | Not applicable | Not applicable | Year | Year | Not applicable |
EM Spatial Extent
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EM ID
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EM-92 | EM-121 | EM-194 |
EM-369
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EM-485
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EM-842 |
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Bounding Type
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Geopolitical | Geopolitical | Physiographic or Ecological | Watershed/Catchment/HUC | Geopolitical | Physiographic or ecological |
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Spatial Extent Name
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EU-15 | The EU-25 plus Switzerland and Norway | St. Croix, U.S. Virgin Islands | Puget Sound watershed | Switzerland | Piedmont Ecoregion |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | >1,000,000 km^2 | 10-100 km^2 | 10,000-100,000 km^2 | 10,000-100,000 km^2 | 100,000-1,000,000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-92 | EM-121 | EM-194 |
EM-369
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EM-485
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EM-842 |
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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 lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all 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 | Not applicable | Irregular | Not applicable | Not applicable |
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Spatial Grain Size
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10 km x 10 km | 1 km x 1 km | Not applicable | Varies | Not applicable | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-92 | EM-121 | EM-194 |
EM-369
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EM-485
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EM-842 |
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EM Computational Approach
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Analytic | Logic- or rule-based | Analytic | Numeric | Numeric | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | stochastic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-92 | EM-121 | EM-194 |
EM-369
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EM-485
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EM-842 |
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Model Calibration Reported?
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No | No | Yes | Unclear | No | Yes |
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Model Goodness of Fit Reported?
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No | No | Yes | Not applicable | No | No |
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Goodness of Fit (metric| value | unit)
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None | None |
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None | None | None |
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Model Operational Validation Reported?
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No | Yes | No | Not applicable | Yes | No |
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Model Uncertainty Analysis Reported?
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Yes | No | Yes | Not applicable | Yes | No |
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Model Sensitivity Analysis Reported?
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Yes | No | No | Not applicable | No | Yes |
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Model Sensitivity Analysis Include Interactions?
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No | 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-92 | EM-121 | EM-194 |
EM-369
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EM-485
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EM-842 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-92 | EM-121 | EM-194 |
EM-369
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EM-485
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EM-842 |
| None | None |
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None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-92 | EM-121 | EM-194 |
EM-369
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EM-485
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EM-842 |
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Centroid Latitude
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50.01 | 50.53 | 17.75 | 47.58 | 46.82 | 36.23 |
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Centroid Longitude
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4.67 | 7.6 | -64.75 | -122.32 | 8.23 | -81.9 |
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Centroid Datum
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WGS84 | WGS84 | NAD83 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-92 | EM-121 | EM-194 |
EM-369
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EM-485
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EM-842 |
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EM Environmental Sub-Class
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Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Forests | Grasslands |
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Specific Environment Type
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Arable lands in near-stream environments | Not applicable | stony coral reef | Pacific NW US region, coastal to montane, urban to rural | forests | grasslands |
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EM Ecological Scale
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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 | 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 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-92 | EM-121 | EM-194 |
EM-369
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EM-485
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EM-842 |
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EM Organismal Scale
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Not applicable | Not applicable | Guild or Assemblage | Not applicable | Community | Species |
Taxonomic level and name of organisms or groups identified
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| EM-92 | EM-121 | EM-194 |
EM-369
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EM-485
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EM-842 |
| None Available | None Available |
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None Available | None Available |
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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-92 | EM-121 | EM-194 |
EM-369
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EM-485
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EM-842 |
| 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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-92 | EM-121 | EM-194 |
EM-369
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EM-485
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EM-842 |
| None |
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None | None |
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