EcoService Models Library (ESML)
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Compare EMs
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.
EM Identity and Description
EM ID
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EM-86 |
EM-333 ![]() |
EM-630 | EM-938 |
EM Short Name
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Area and hotspots of soil retention, South Africa | Evoland v3.5 (unbounded growth), Eugene, OR, USA | WaterWorld v2, Santa Basin, Peru | OpenNSPECT v. 1.2 |
EM Full Name
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Area and hotspots of soil retention, South Africa | Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA | WaterWorld v2, Santa Basin, Peru | OpenNSPECT v. 1.2 |
EM Source or Collection
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None | Envision | None | None |
EM Source Document ID
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271 |
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
368 | 431 |
Document Author
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Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Van Soesbergen, A. and M. Mulligan | Eslinger, David L., H. Jamieson Carter, Matt Pendleton, Shan Burkhalter, Margaret Allen |
Document Year
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2008 | 2008 | 2018 | 2012 |
Document Title
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Mapping ecosystem services for planning and management | Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | “OpenNSPECT: The Open-source Nonpoint Source Pollution and Erosion Comparison Tool.” NOAA Office for Coastal Management, Charleston, South Carolina. Accessed (11/2022) at https://coast.noaa.gov/digitalcoast/tools/opennspect.html |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Webpage |
EM ID
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EM-86 |
EM-333 ![]() |
EM-630 | EM-938 |
Not applicable | http://evoland.bioe.orst.edu/ | www.policysupport.org/waterworld | https://coast.noaa.gov/digitalcoast/tools/opennspect.html | |
Contact Name
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Benis Egoh | Michael R. Guzy | Arnout van Soesbergen | Not reported |
Contact Address
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Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | Oregon State University, Dept. of Biological and Ecological Engineering | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | NOAA Coastal Services Center, 2234 South Hobson Avenue Charleston, South Carolina 29405-2413 |
Contact Email
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Not reported | Not reported | arnout.van_soesbergen@kcl.ac.uk | Not reported |
EM ID
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EM-86 |
EM-333 ![]() |
EM-630 | EM-938 |
Summary Description
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AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil retention was modelled as a function of vegetation or litter cover and soil erosion potential. Schoeman et al. (2002) modelled soil erosion potential and derived eight erosion classes, ranging from low to severe erosion potential for South Africa. The vegetation cover was mapped by ranking vegetation types using expert knowledge of their ability to curb erosion. We used Schulze (2004) index of litter cover which estimates the soil surface covered by litter based on observations in a range of grasslands, woodlands and natural forests. According to Quinton et al. (1997) and Fowler and Rockstrom (2001) soil erosion is slightly reduced with about 30%, significantly reduced with about 70% vegetation cover. The range of soil retention was mapped by selecting all areas that had vegetation or litter cover of more than 30% for both the expert classified vegetation types and litter accumulation index within areas with moderate to severe erosion potential. The hotspot was mapped as areas with severe erosion potential and vegetation/litter cover of at least 70% where maintaining the cover is essential to prevent erosion. An assumption was made that the potential for this service is relatively low in areas with little natural vegetation or litter cover." | **Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | ABSTRACT: "Water resources in the Santa basin in the Peruvian Andes are increasingly under pressure from climate change and population increases. Impacts of temperature-driven glacier retreat on stream flow are better studied than those from precipitation changes, yet present and future water resources are mostly dependent on precipitation which is more difficult to predict with climate models. This study combines a broad range of projections from climate models with a hydrological model (WaterWorld), showing a general trend towards an increase in water availability due to precipitation increases over the basin. However, high uncertainties in these projections necessitate the need for basin-wide policies aimed at increased adaptability." AUTHOR'S DESCRIPTION: "WaterWorld is a fully distributed, process-based hydrological model that utilises remotely sensed and globally available datasets to support hydrological analysis and decision-making at national and local scales globally, with a particular focus on un-gauged and/or data-poor environments, which makes it highly suited to this study. The model (version 2) currently runs on either 10 degree tiles, large river basins or countries at 1-km2 resolution or 1 degree tiles at 1-ha resolution utilising different datasets. It simulates a hydrological baseline as a mean for the period 1950-2000 and can be used to calculate the hydrological impact of scenarios of climate change, land use change, land management options, impacts of extractives (oil & gas and mining) and impacts of changes in population and demography as well as combinations of these. The model is ‘self parameterising’ (Mulligan, 2013a) in the sense that all data required for model application anywhere in the world is provided with the model, removing a key barrier to model application. However, if users have better data than those provided, it is possible to upload these to WaterWorld as GIS files and use them instead. Results can be viewed visually within the web browser or downloaded as GIS maps. The model’s equations and processes are described in more detail in Mulligan and Burke (2005) and Mulligan (2013b). The model parameters are not routinely calibrated to observed flows as it is designed for hydrological scenario analysis in which the physical basis of its parameters must be retained and the model is also often used in un-gauged basins. Calibration is inappropriate under these circumstances (Sivapalan et al., 2003). The freely available nature of the model means that anyone can apply it and replicate the results shown here. WaterWorld’s (V2) snow and ice module is capable of simulating the processes of melt water production, snow fall and snow pack, making this version highly suited to the current application. The model component is based on a full energy-balance for snow accumulation and melting based on Walter et al., (2005) with input data provided globally by the SimTerra database (Mulligan, 2011) upon which the model r | "This open-source version of the Nonpoint Source Pollution and Erosion Comparison Tool is used to investigate potential water quality impacts from climate change and development to other land uses. The downloadable tool is designed to be broadly applicable for coastal and noncoastal areas alike. Tool functions simulate erosion, pollution, and the accumulation from overland flow. OpenNSPECT uses spatial elevation data to calculate flow direction and flow accumulation throughout a watershed. To do this, land cover, precipitation, and soils data are processed to estimate runoff volume at both the local and watershed levels. Coefficients representing the contribution of each land cover class to the expected pollutant load are also applied to land cover data to approximate total pollutant loads. These coefficients are taken from published sources or can be derived from local water quality studies. The output layers display estimates of runoff volume, pollutant loads, pollutant concentration, and total sediment yield. Requires MapWindow GIS v.4.8.8 (open source software)" |
Specific Policy or Decision Context Cited
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None identified | Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None identified | None identified |
Biophysical Context
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Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | No additional description provided | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | No additional description provided |
EM Scenario Drivers
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No scenarios presented | Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves. | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | No scenarios presented |
EM ID
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EM-86 |
EM-333 ![]() |
EM-630 | EM-938 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) | Method Only |
New or Pre-existing EM?
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New or revised model | New or revised model | Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-86 |
EM-333 ![]() |
EM-630 | EM-938 |
Document ID for related EM
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Doc-271 ?Comment:Document 273 used for source information on soil erosion potential variable |
Doc-183 | Doc-47 | Doc-313 | Doc-314 ?Comment:Doc 183 is a secondary source for the Evoland model. |
None | None |
EM ID for related EM
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EM-85 | EM-87 | EM-88 | EM-12 | EM-369 | None | EM-940 |
EM Modeling Approach
EM ID
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EM-86 |
EM-333 ![]() |
EM-630 | EM-938 |
EM Temporal Extent
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Not reported | 1990-2050 | 1950-2071 | Not applicable |
EM Time Dependence
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time-stationary | time-dependent | time-dependent | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | future time | both | Not applicable |
EM Time Continuity
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Not applicable | discrete | discrete | Not applicable |
EM Temporal Grain Size Value
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Not applicable | 2 | 1 | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Year | Month | Not applicable |
EM ID
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EM-86 |
EM-333 ![]() |
EM-630 | EM-938 |
Bounding Type
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Geopolitical | Geopolitical | Watershed/Catchment/HUC | Not applicable |
Spatial Extent Name
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South Africa | Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Santa Basin | Not applicable |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 10-100 km^2 | 10,000-100,000 km^2 | Not applicable |
EM ID
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EM-86 |
EM-333 ![]() |
EM-630 | EM-938 |
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) |
Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature |
Spatial Grain Size
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Distributed across catchments with average size of 65,000 ha | varies | 1 km2 | 30 m |
EM ID
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EM-86 |
EM-333 ![]() |
EM-630 | EM-938 |
EM Computational Approach
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Analytic | Numeric | * | Analytic |
EM Determinism
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deterministic | stochastic | deterministic | deterministic |
Statistical Estimation of EM
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None |
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EM ID
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EM-86 |
EM-333 ![]() |
EM-630 | EM-938 |
Model Calibration Reported?
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No | Unclear | No | Not applicable |
Model Goodness of Fit Reported?
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No | No | No | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None | None | None |
Model Operational Validation Reported?
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No | No | Yes | Not applicable |
Model Uncertainty Analysis Reported?
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No | No | No | Not applicable |
Model Sensitivity Analysis Reported?
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No | No | No | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-86 |
EM-333 ![]() |
EM-630 | EM-938 |
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None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-86 |
EM-333 ![]() |
EM-630 | EM-938 |
None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-86 |
EM-333 ![]() |
EM-630 | EM-938 |
Centroid Latitude
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-30 | 44.11 | -9.05 | Not applicable |
Centroid Longitude
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25 | -123.09 | -77.81 | Not applicable |
Centroid Datum
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WGS84 | WGS84 | WGS84 | Not applicable |
Centroid Coordinates Status
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Estimated | Estimated | Estimated | Not applicable |
EM ID
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EM-86 |
EM-333 ![]() |
EM-630 | EM-938 |
EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Forests | Agroecosystems | Created Greenspace | None | Aquatic Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Not reported | Agricultural-urban interface at river junction | tropical, coastal to montane | Coastal and non-coastal |
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 | Other or unclear (comment) | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-86 |
EM-333 ![]() |
EM-630 | EM-938 |
EM Organismal Scale
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Not applicable | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-86 |
EM-333 ![]() |
EM-630 | EM-938 |
None Available |
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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-86 |
EM-333 ![]() |
EM-630 | EM-938 |
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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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-86 |
EM-333 ![]() |
EM-630 | EM-938 |
None |
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None | None |