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-86 | EM-132 |
EM-333 
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EM-414 |
EM-718
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EM-875 |
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EM Short Name
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Area and hotspots of soil retention, South Africa | Flood regulation capacity, Etropole, Bulgaria | Evoland v3.5 (unbounded growth), Eugene, OR, USA | SAV occurrence, St. Louis River, MN/WI, USA | WESP: Riparian & stream habitat, ID, USA | Valuing environmental ed., New York, New York |
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EM Full Name
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Area and hotspots of soil retention, South Africa | Flood regulation capacity of landscapes, Municipality of Etropole, Bulgaria | Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA | Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA | WESP: Riparian and stream habitat focus projects, ID, USA | Valuing environmental education, Hudson River Park, New York, New York |
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EM Source or Collection
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None | EU Biodiversity Action 5 | Envision | US EPA | None | None |
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EM Source Document ID
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271 | 248 |
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
330 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
416 |
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Document Author
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Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Nedkov, S., Burkhard, B. | Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke | Murphy, C. and T. Weekley | Hutcheson, W. Hoagland, P., and D. Jin |
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Document Year
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2008 | 2012 | 2008 | 2013 | 2012 | 2018 |
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Document Title
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Mapping ecosystem services for planning and management | Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria | Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Valuing environmental education as a cultural ecosystem service at Hudson River Park |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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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 | Published journal manuscript |
Software and Access
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EM ID
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EM-86 | EM-132 |
EM-333
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EM-414 |
EM-718
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EM-875 |
| Not applicable | Not applicable | http://evoland.bioe.orst.edu/ | Not applicable | Not applicable | Not applicable | |
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Contact Name
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Benis Egoh | Stoyan Nedkov | Michael R. Guzy | Ted R. Angradi | Chris Murphy | Walter Hutcheson |
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Contact Address
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Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | National Institute of Geophysics, Geodesy and Geography, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, bl.3, 1113 Sofia, Bulgaria | Oregon State University, Dept. of Biological and Ecological Engineering | U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | New York University, United States |
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Contact Email
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Not reported | snedkov@abv.bg | Not reported | angradi.theodore@epa.gov | chris.murphy@idfg.idaho.gov | wwh235@nyu.edu |
EM Description
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EM ID
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EM-86 | EM-132 |
EM-333
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EM-414 |
EM-718
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EM-875 |
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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." | ABSTRACT: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. In this study, the capacities of different ecosystems to regulate floods were assessed through investigations of water retention functions of the vegetation and soil cover. Based on spatial land cover units originating from CORINE and further data sets, these regulating ecosystem services were quantified and mapped. Resulting maps show the ecosystems’ flood regulating service capacities in the case study area of the Malki Iskar river basin above the town of Etropole in the northern part of Bulgaria...The resulting map of flood regulation supply capacities shows that the Etropole municipality’s area has relatively high capacities for flood regulation. Areas of high and very high relevant capacities cover about 34% of the study area." AUTHOR'S DESCRIPTION: "The capacities of the identified spatial units were assessed on a relative scale ranging from 0 to 5 (after Burkhard et al., 2009). A 0-value indicates that there is no relevant capacity to supply flood regulating services and a 5-value indicates the highest relevant capacity for the supply of these services in the case study region. Values of 2, 3 and 4 represent respective intermediate supply capacities. Of course it depends on the observer’s estimation and knowledge which function–service relations in general are supposed to be relevant. But, this scale offers an alternative relative evaluation scheme, avoiding the presentation of monetary or normative value-transfer results. The 0–5 capacity values’ classifications for the different land cover types were based on the spatial analyses of different biogeophysical and land use data combined with hydrological modeling as described before…The supply capacities of the land cover classes and soil types in the study area were assigned to every unit in their databases. GIS map layers, containing information about the capacity to supply flood regulation for every polygon, were created. The map of supply capacities of flood regulating ecosystem services was elaborated by overlaying the GIS map layers of the land cover and the soils’ capacities." | **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: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake." | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | ABSTRACT: " The Hudson River and its estuary is once again an ecologically, economically, and culturally functional component of New York City’s natural environment. The estuary’s cultural significance may derive largely from environmental education, including marine science programs for the public. These programs are understood as ‘‘cultural” ecosystem services but are rarely evaluated in economic terms. We estimated the economic value of the Hudson River Park’s environmental education programs. We compiled data on visits by schools and summer camps from 32 New York City school districts to the Park during the years 2014 and 2015. A ‘‘travel cost” approach was adapted from the field of environmental economics to estimate the value of education in this context. A small—but conservative—estimate of the Park’s annual education program benefits ranged between $7500 and 25,500, implying an average capitalized value on the order of $0.6 million. Importantly, organizations in districts with high proportions of minority students or English language learners were found to be more likely to participate in the Park’s programs. The results provide an optimistic view of the benefits of environmental education focused on urban estuaries, through which a growing understanding of ecological systems could lead to future environmental improvements. " |
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Specific Policy or Decision Context Cited
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None identified | 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 | None identified |
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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. | Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak. | No additional description provided | submerged aquatic vegetation | restored, enhanced and created wetlands | N/A |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves. | No scenarios presented | Sites, function or habitat focus | N?A |
EM Relationship to Other EMs or Applications
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EM ID
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EM-86 | EM-132 |
EM-333
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EM-414 |
EM-718
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EM-875 |
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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 (multiple runs exist) View EM Runs | Method + Application |
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New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | 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
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EM ID
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EM-86 | EM-132 |
EM-333
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EM-414 |
EM-718
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EM-875 |
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Document ID for related EM
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Doc-271 ?Comment:Document 273 used for source information on soil erosion potential variable |
Doc-248 | Doc-251 | Doc-252 |
Doc-183 | Doc-47 | Doc-313 | Doc-314 ?Comment:Doc 183 is a secondary source for the Evoland model. |
None | Doc-390 | None |
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EM ID for related EM
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EM-85 | EM-87 | EM-88 | EM-130 | EM-133 | EM-12 | EM-369 | None | EM-706 | EM-729 | EM-730 | EM-734 | EM-743 | EM-749 | EM-750 | EM-756 | EM-757 | EM-758 | EM-759 | EM-760 | EM-761 | EM-763 | EM-764 | EM-766 | EM-767 | EM-732 | EM-737 | EM-738 | EM-739 | EM-741 | EM-742 | EM-751 | EM-768 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-86 | EM-132 |
EM-333
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EM-414 |
EM-718
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EM-875 |
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EM Temporal Extent
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Not reported | Not reported | 1990-2050 | 2010 - 2012 | 2010-2011 | 2015 |
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EM Time Dependence
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time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | future time | Not applicable | past time | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | 2 | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-86 | EM-132 |
EM-333
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EM-414 |
EM-718
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EM-875 |
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Bounding Type
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Geopolitical | Geopolitical | Geopolitical | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Geopolitical |
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Spatial Extent Name
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South Africa | Municipality of Etropole | Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | St. Louis River Estuary | Wetlands in idaho | Hudson River Park |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100-1000 km^2 | 10-100 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | 10-100 ha |
Spatial Distribution of Computations
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EM ID
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EM-86 | EM-132 |
EM-333
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EM-414 |
EM-718
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EM-875 |
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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:BH: Each individual transect?s data was parceled into location reports, and that each report?s ?quadrat? area was dependent upon the angle of the hydroacoustic sampling beam. The spatial grain is 0.07 m^2, 0.20 m^2 and 0.70 m^2 for depths of 1 meter, 2 meters and 3 meters, respectively. |
spatially lumped (in all cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable |
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Spatial Grain Size
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Distributed across catchments with average size of 65,000 ha | Distributed by land cover and soil type polygons | varies | 0.07 m^2 to 0.70 m^2 | Not applicable | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-86 | EM-132 |
EM-333
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EM-414 |
EM-718
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EM-875 |
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EM Computational Approach
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Analytic | Analytic | Numeric | Analytic | Numeric | Numeric |
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EM Determinism
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deterministic | deterministic | stochastic | 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-86 | EM-132 |
EM-333
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EM-414 |
EM-718
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EM-875 |
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Model Calibration Reported?
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No | No | Unclear | Yes | No | No |
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Model Goodness of Fit Reported?
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No | No | No | Yes | No | No |
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Goodness of Fit (metric| value | unit)
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None | None | None |
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None | None |
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Model Operational Validation Reported?
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No | No | No | Yes | No | No |
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Model Uncertainty Analysis Reported?
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No | No | No | No | No | No |
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Model Sensitivity Analysis Reported?
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No | No | No | No | No | No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| EM-86 | EM-132 |
EM-333
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EM-414 |
EM-718
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EM-875 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-86 | EM-132 |
EM-333
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EM-414 |
EM-718
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EM-875 |
| None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-86 | EM-132 |
EM-333
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EM-414 |
EM-718
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EM-875 |
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Centroid Latitude
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-30 | 42.8 | 44.11 | 46.72 | 44.06 | 40.73 |
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Centroid Longitude
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25 | 24 | -123.09 | -96.13 | -114.69 | -74.01 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | 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-86 | EM-132 |
EM-333
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EM-414 |
EM-718
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EM-875 |
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EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Lakes and Ponds | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Inland Wetlands | Created Greenspace |
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Specific Environment Type
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Not reported | Mountainous flood-prone region | Agricultural-urban interface at river junction | Freshwater estuarine system | created, restored and enhanced wetlands | Park |
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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 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 |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-86 | EM-132 |
EM-333
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EM-414 |
EM-718
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EM-875 |
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EM Organismal Scale
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-86 | EM-132 |
EM-333
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EM-414 |
EM-718
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EM-875 |
| None Available | None Available |
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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-86 | EM-132 |
EM-333
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EM-414 |
EM-718
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EM-875 |
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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-86 | EM-132 |
EM-333
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EM-414 |
EM-718
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EM-875 |
| None |
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None | None |
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