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-84 | EM-92 | EM-449 |
EM-709 
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EM-992
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EM Short Name
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ACRU, South Africa | Runoff potential of pesticides, Europe | Decrease in erosion (shoreline), St. Croix, USVI | Pollinators on landfill sites, United Kingdom | DAISY model, Taastrup, Copenhagen |
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EM Full Name
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ACRU (Agricultural Catchments Research Unit), South Africa | Runoff potential of pesticides, Europe | Decrease in erosion (shoreline) by reef, St. Croix, USVI | Pollinating insects on landfill sites, East Midlands, United Kingdon | 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 | US EPA | None | None |
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EM Source Document ID
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271 | 254 | 335 | 389 | 464 |
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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. | Schriever, C. A., and Liess, M. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin | Ghaley, B. B., & Porter, J. R. |
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Document Year
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2008 | 2007 | 2014 | 2013 | 2014 |
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Document Title
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Mapping ecosystem services for planning and management | Mapping ecological risk of agricultural pesticide runoff | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects | 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 | 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 journal manuscript |
Software and Access
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EM ID
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EM-84 | EM-92 | EM-449 |
EM-709
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EM-992
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| Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | |
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Contact Name
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Roland E Schulze | Carola Alexandra Schriever | Susan H. Yee | Sam Tarrant | Bhim Bahadur Ghaley |
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Contact Address
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School of Bioresources Engineering and Environmental Hydrology, University of Natal, South Africa | Helmholtz Centre for Environmental Research - UFZ, Department of System Ecotoxicology, Permoserstrasse 15, 04318 Leipzig, Germany | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K. | 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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schulzeR@nu.ac.za | carola.schriever@ufz.de | yee.susan@epa.gov | sam.tarrant@rspb.org.uk | bbg@life.ku.dk |
EM Description
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EM ID
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EM-84 | EM-92 | EM-449 |
EM-709
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EM-992
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Summary Description
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AUTHOR'S DESCRIPTION (Doc ID 272): "ACRU is a daily timestep, physical conceptual and multipurpose model structured to simulate impacts of land cover/ use change. The model can output, inter alia, components of runoff, irrigation supply and demand, reservoir water budgets as well as sediment and crop yields." AUTHOR'S DESCRIPTION (Doc ID 271): "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…The total benefit to people of water supply is a function of both the quantity and quality with the ecosystem playing a key role in the latter. However, due to the lack of suitable national scale data on water quality for quantifying the service, runoff was used as an estimate of the benefit where runoff is the total water yield from a watershed including surface and subsurface flow. This assumes that runoff is positively correlated with quality, which is the case in South Africa (Allanson et al., 1990)…In South Africa, water resources are mapped in water management areas called catchments (vs. watersheds) where a catchment is defined as the area of land that is drained by a single river system, including its tributaries (DWAF, 2004). There are 1946 quaternary (4th order) catchments in South Africa, the smallest is 4800 ha and the average size is 65,000 ha. Schulze (1997) modelled annual runoff for each quaternary catchment. During modelling of runoff, he used rainfall data collected over a period of more than 30 years, as well as data on other climatic factors, soil characteristics and grassland as the land cover. In this study, median annual simulated runoff was used as a measure of surface water supply. The volume of runoff per quaternary catchment was calculated for surface water supply. The range (areas with runoff of 30 million m^3 or more) and hotspots (areas with runoff of 70 million m^3 or more) were defined using a combination of statistics and expert inputs due to a lack of published thresholds in the literature." | 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: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef." | ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level…In the first year of study, plants in flower and flower visitors were surveyed using the same transects as for the floral resources surveys. The transect was left undisturbed for 20 minutes following the initial plant survey to allow the flower visitors to return. Each transect was surveyed at a rate of approximately 3m/minute for 30 minutes. All insects observed to touch the sexual parts of flowers were either captured using a butterfly net and transferred into individually labeled specimen jars, or directly captured into the jars. After the survey was completed, those insects that could be identified in the field were recorded and released. The flower-visitor surveys were conducted in the morning, within 1 hour of midday, and in the afternoon to sample those insects active at different times. Insects that could not be identified in the field were collected as voucher specimens for later identification. Identifications were verified using reference collections and by taxon specialists. Relatively low capture rates in the first year led to methods being altered in the second year when surveying followed a spiral pattern from a randomly determined point on the sites, at a standard pace of 10 m/minute for 30 minutes, following Nielsen and Bascompte (2007) and Kalikhman (2007). Given a 2-m wide transect, an area of approximately 600m2 was sampled in each | 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 | European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | 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. | Not applicable | No additional description provided | No additional description provided | Agro-ecosystem test farm, Copenhagen, Denmark. |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | A soil organic matter value of 1.3% was used for this model run |
EM Relationship to Other EMs or Applications
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EM ID
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EM-84 | EM-92 | EM-449 |
EM-709
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EM-992
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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 |
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New or Pre-existing EM?
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Application of existing model | New or revised model | Application of existing model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-84 | EM-92 | EM-449 |
EM-709
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EM-992
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Document ID for related EM
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Doc-272 ?Comment:Doc ID 272 was also used as a source document for this EM |
Doc-255 | Doc-256 | Doc-257 | Doc-335 | Doc-389 | None |
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EM ID for related EM
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None | None | EM-447 | EM-448 | EM-697 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-84 | EM-92 | EM-449 |
EM-709
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EM-992
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EM Temporal Extent
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1950-1993 | 2000 | 2006-2007, 2010 | 2007-2008 | 2003-2013 |
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EM Time Dependence
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time-dependent | time-dependent | time-stationary | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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future time | future time | Not applicable | Not applicable | past time |
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EM Time Continuity
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discrete | discrete | Not applicable | Not applicable | continuous |
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EM Temporal Grain Size Value
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1 | 1 | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Day | Day | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-84 | EM-92 | EM-449 |
EM-709
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EM-992
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Bounding Type
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Geopolitical | Geopolitical | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological |
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Spatial Extent Name
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South Africa | EU-15 | Coastal zone surrounding St. Croix | East Midlands | Taastrup experimental farm |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 1-10 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-84 | EM-92 | EM-449 |
EM-709
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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) | 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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other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | 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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Distributed by catchments with average size of 65,000 ha | 10 km x 10 km | 10 m x 10 m | multiple unrelated locations | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-84 | EM-92 | EM-449 |
EM-709
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EM-992
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EM Computational Approach
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Numeric | Analytic | Analytic | Analytic | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-84 | EM-92 | EM-449 |
EM-709
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EM-992
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Model Calibration Reported?
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No | No | Yes | Not applicable | Unclear |
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Model Goodness of Fit Reported?
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No | No | No | Not applicable | Unclear |
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Goodness of Fit (metric| value | unit)
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None | None | None | None | None |
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Model Operational Validation Reported?
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No | No | Yes | Not applicable | Yes |
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Model Uncertainty Analysis Reported?
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No | Yes | No | Not applicable | Unclear |
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Model Sensitivity Analysis Reported?
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No | Yes | No | Not applicable | Unclear |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | No | 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-84 | EM-92 | EM-449 |
EM-709
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EM-992
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-84 | EM-92 | EM-449 |
EM-709
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EM-992
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| None | None |
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None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-84 | EM-92 | EM-449 |
EM-709
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EM-992
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Centroid Latitude
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-30 | 50.01 | 17.73 | 52.22 | 55.4 |
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Centroid Longitude
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25 | 4.67 | -64.77 | -0.91 | 12.18 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | None provided |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated | Estimated | Provided |
Environments and Scales Modeled
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EM ID
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EM-84 | EM-92 | EM-449 |
EM-709
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EM-992
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EM Environmental Sub-Class
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Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Near Coastal Marine and Estuarine | Created Greenspace | Grasslands | Agroecosystems |
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Specific Environment Type
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Not reported | Arable lands in near-stream environments | Coral reefs | restored landfills and grasslands | Agroecosystems |
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EM Ecological Scale
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Ecological scale is coarser 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-84 | EM-92 | EM-449 |
EM-709
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EM-992
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EM Organismal Scale
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Not applicable | Not applicable | Not applicable | Individual or population, within a species |
Guild or Assemblage ?Comment:Microbrial biomass is lumped together, but specific crops are presented. |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-84 | EM-92 | EM-449 |
EM-709
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EM-992
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| None Available | 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-84 | EM-92 | EM-449 |
EM-709
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EM-992
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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>
fegs1Help
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
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| EM-84 | EM-92 | EM-449 |
EM-709
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EM-992
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None |
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None |
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