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
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EM ID
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EM-127 |
EM-788 |
EM-992 |
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EM Short Name
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Annual profit - carbon plantings, South Australia | Wild bees over 26 yrs of restored prairie, IL, USA | DAISY model, Taastrup, Copenhagen |
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EM Full Name
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Annual profit from carbon plantings, South Australia | Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA | Ecosystem function and service quantification and valuation in a conventional winter wheat production system with DAISY model in Denmark |
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EM Source or Collection
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None | None | None |
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EM Source Document ID
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243 | 401 | 464 |
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Document Author
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Crossman, N. D., Bryan, B. A., and Summers, D. M. | Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree | Ghaley, B. B., & Porter, J. R. |
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Document Year
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2011 | 2017 | 2014 |
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Document Title
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Carbon payments and low-cost conservation | Wild bee community change over a 26-year chronosequence of restored tallgrass prairie | Ecosystem function and service quantification and valuation in a conventional winter wheat production system with DAISY model in Denmark |
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Document Status
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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 |
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EM ID
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EM-127 |
EM-788 |
EM-992 |
| Not applicable | Not applicable | Not applicable | |
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Contact Name
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Neville D. Crossman | Sean R. Griffin | Bhim Bahadur Ghaley |
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Contact Address
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CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia | Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901, U.S.A. | Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen, Højbakkegård Allé 30, DK-2630 Taastrup, Denmark. |
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Contact Email
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neville.crossman@csiro.au | srgriffin108@gmail.com | bbg@life.ku.dk |
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EM ID
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EM-127 |
EM-788 |
EM-992 |
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Summary Description
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ABSTRACT: "A price on carbon is expected to generate demand for carbon offset schemes. This demand could drive investment in tree-based monocultures that provide higher carbon yields than diverse plantings of native tree and shrub species, which sequester less carbon but provide greater variation in vegetation structure and composition. Economic instruments such as species conservation banking, the creation and trading of credits that represent biological-diversity values on private land, could close the financial gap between monocultures and more diverse plantings by providing payments to individuals who plant diverse species in locations that contribute to conservation and restoration goals. We studied a highly modified agricultural system in southern Australia that is typical of many temperate agriculture zones globally (i.e., has a high proportion of endangered species, high levels of habitat fragmentation, and presence of non-native species). We quantified the economic returns...from carbon plantings (monoculture and mixed tree and shrubs) under six carbon-price scenarios." AUTHOR'S DESCRIPTION: "The economic returns of carbon plantings are highly variable and depend primarily on carbon yield and price and opportunity costs (Newell & Stavins 2000; Richards & Stokes 2004; Torres et al. 2010)...The spatial variation in carbon yield and costs, including establishment, maintenance, transaction, and opportunity costs, means that the net economic returns of carbon plantings are also likely to vary spatially." | ABSTRACT: "Restoration efforts often focus on plants, but additionally require the establishment and long-term persistence of diverse groups of nontarget organisms, such as bees, for important ecosystem functions and meeting restoration goals. We investigated long-term patterns in the response of bees to habitat restoration by sampling bee communities along a 26-year chronosequence of restored tallgrass prairie in north-central Illinois, U.S.A. Specifically, we examined how bee communities changed over time since restoration in terms of (1) abundance and richness, (2) community composition, and (3) the two components of beta diversity, one-to-one species replacement, and changes in species richness. Bee abundance and raw richness increased with restoration age from the low level of the pre-restoration (agricultural) sites to the target level of the remnant prairie within the first 2–3 years after restoration, and these high levels were maintained throughout the entire restoration chronosequence. Bee community composition of the youngest restored sites differed from that of prairie remnants, but 5–7 years post-restoration the community composition of restored prairie converged with that of remnants. Landscape context, particularly nearby wooded land, was found to affect abundance, rarefied richness, and community composition. Partitioning overall beta diversity between sites into species replacement and richness effects revealed that the main driver of community change over time was the gradual accumulation of species, rather than one-to-one species replacement. At the spatial and temporal scales we studied, we conclude that prairie restoration efforts targeting plants also successfully restore bee communities." | With inevitable link between ecosystem function (EF), ecosystem services (ES) and agricultural productivity, there is a need for quantification and valuation of EF and ES in agro-ecosystems. Management practices have significant effects on soil organic matter (SOM), affecting productivity, EF and ES provision. The objective was to quantify two EF: soil water storage and nitrogen mineralization and three ES: food and fodder production and carbon sequestration, in a conventional winter wheat production system at 2.6% SOM compared to 50% lower (1.3%) and 50% higher (3.9%) SOM in Denmark by DAISY model. At 2.6% SOM, the food and fodder production was 6.49 and 6.86 t ha−1 year−1 respectively whereas carbon sequestration and soil water storage was 9.73 t ha−1 year−1 and 684 mm ha−1 year−1 respectively and nitrogen mineralisation was 83.58 kg ha−1 year−1. At 2.6% SOM, the two EF and three ES values were US$ 177 and US$ 2542 ha−1 year−1 respectively equivalent to US$ 96 and US$1370 million year−1 respectively in Denmark. The EF and ES quantities and values were positively correlated with SOM content. Hence, the quantification and valuation of EF and ES provides an empirical tool for optimising the EF and ES provision for agricultural productivity. |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified |
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Biophysical Context
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Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm. | The Nachusa Grasslands consists of over 1,900 ha of restored prairie plantings, prairie remnants, and other habitats such as wetlands and oak savanna. The area is generally mesic with an average annual precipitation of 975 mm, and most precipitation occurs during the growing season. | Agro-ecosystem test farm, Copenhagen, Denmark. |
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EM Scenario Drivers
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Carbon prices at $10/t CO2^-e, $15/t CO2^-e, $20/t CO2^-e, $25/t CO2^-e, $30/t CO2^-e, and $40/t CO2^-e | No scenarios presented | A soil organic matter value of 1.3% was used for this model run |
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EM ID
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EM-127 |
EM-788 |
EM-992 |
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Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs ?Comment:Runs are differentiated based on the the expected annual profit from two types of carbon plantings: 1) Tree-based monocultures (i.e., monoculture carbon planting) and 2) Diverse plantings of native tree and shrub species (i.e., ecological carbon planting) |
Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs |
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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 |
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-127 |
EM-788 |
EM-992 |
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Document ID for related EM
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Doc-245 | Doc-246 | Doc-247 | Doc-243 | None | None |
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EM ID for related EM
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EM-128 | EM-141 | None | None |
EM Modeling Approach
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EM ID
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EM-127 |
EM-788 |
EM-992 |
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EM Temporal Extent
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2009-2050 | 1988-2014 | 2003-2013 |
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EM Time Dependence
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time-dependent | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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future time | Not applicable | past time |
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EM Time Continuity
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discrete | Not applicable | continuous |
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EM Temporal Grain Size Value
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1 | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable |
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EM ID
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EM-127 |
EM-788 |
EM-992 |
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Bounding Type
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Physiographic or Ecological | Physiographic or ecological | Physiographic or ecological |
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Spatial Extent Name
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Agricultural districts of the state of South Australia | Nachusa Grasslands | Taastrup experimental farm |
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Spatial Extent Area (Magnitude)
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100,000-1,000,000 km^2 | 10-100 km^2 | 1-10 km^2 |
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EM ID
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EM-127 |
EM-788 |
EM-992 |
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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) | other or unclear (comment) |
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Spatial Grain Type
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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable |
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Spatial Grain Size
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1 ha x 1 ha | Area varies by site | Not applicable |
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EM ID
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EM-127 |
EM-788 |
EM-992 |
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EM Computational Approach
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Analytic | Analytic | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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EM ID
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EM-127 |
EM-788 |
EM-992 |
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Model Calibration Reported?
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No | No | Unclear |
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Model Goodness of Fit Reported?
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No | No | Unclear |
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Goodness of Fit (metric| value | unit)
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None | None | None |
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Model Operational Validation Reported?
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No | No | Yes |
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Model Uncertainty Analysis Reported?
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No | No | Unclear |
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Model Sensitivity Analysis Reported?
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No | No | Unclear |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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EM-127 |
EM-788 |
EM-992 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-127 |
EM-788 |
EM-992 |
| None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-127 |
EM-788 |
EM-992 |
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Centroid Latitude
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-34.9 | 41.89 | 55.4 |
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Centroid Longitude
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138.7 | -89.34 | 12.18 |
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Centroid Datum
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WGS84 | WGS84 | None provided |
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Centroid Coordinates Status
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Estimated | Provided | Provided |
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EM ID
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EM-127 |
EM-788 |
EM-992 |
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EM Environmental Sub-Class
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Agroecosystems | Agroecosystems | Grasslands | Agroecosystems |
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Specific Environment Type
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Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Restored prairie, prairie remnants, and cropland | Agroecosystems |
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EM Ecological Scale
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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 of differentiation of organisms modeled
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EM ID
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EM-127 |
EM-788 |
EM-992 |
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EM Organismal Scale
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Guild or Assemblage | Species |
Guild or Assemblage ?Comment:Microbrial biomass is lumped together, but specific crops are presented. |
Taxonomic level and name of organisms or groups identified
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EM-127 |
EM-788 |
EM-992 |
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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)
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EM-127 |
EM-788 |
EM-992 |
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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)
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EM-127 |
EM-788 |
EM-992 |
| None | None |
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