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-434 |
EM-660 
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EM-788
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EM-983 | EM-1019 |
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EM Short Name
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Land capability classification | RUM: Valuing fishing quality, Michigan, USA | Wild bees over 26 yrs of restored prairie, IL, USA | Atlantis ecosystem physics submodel | SMOKE emissions model, Asia |
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EM Full Name
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Land capability classification | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA | Atlantis user's guide part I: general overview, physics & ecology | Development of an anthropogenic emissions processing system for Asia using SMOKE |
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EM Source or Collection
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None | None | None | None | None |
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EM Source Document ID
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340 |
382 ?Comment:Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010. |
401 | 461 | 481 |
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Document Author
em.detail.documentAuthorHelp
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United States Department of Agriculture - Natural Resources Conservation Service | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree | Audzijonyte, A., Gorton, R., Kaplan, I., & Fulton, E. A. | Woo, J.H., Choi, K.C., Kim, H.K., Baek, B.H., Jang, M., Eum, J.H., Song, C.H., Ma, Y.I., Sunwoo, Y., Chang, L.S. and Yoo, S.H. |
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Document Year
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2013 | 2014 | 2017 | 2017 | 2012 |
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Document Title
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National Soil Survey Handbook - Part 622 - Interpretative Groups | Valuing recreational fishing quality at rivers and streams | Wild bee community change over a 26-year chronosequence of restored tallgrass prairie | Atlantis user’s guide part I: general overview, physics & ecology | Development of an anthropogenic emissions processing system for Asia using SMOKE |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) | Peer reviewed and published |
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Comments on Status
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Published report | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript |
Software and Access
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EM ID
em.detail.idHelp
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EM-434 |
EM-660
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EM-788
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EM-983 | EM-1019 |
| Not applicable | Not applicable | Not applicable | https://research.csiro.au/atlantis/home/links/ | https://www.cmascenter.org/smoke/ | |
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Contact Name
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United States Department of Agriculture | Richard Melstrom | Sean R. Griffin | Asta Audzijonyte | Jung-Hun Woo |
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Contact Address
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Not reported | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901, U.S.A. | University of Tasmania (Australia); Nature Research Centre (Lithuania) | Department of Advanced Technology Fusion, Room 812, San-Hak Bldg., Konkuk University, Seoul, South Korea |
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Contact Email
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http://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/contactus/ | melstrom@okstate.edu | srgriffin108@gmail.com | Asta.Audzijonyte@utas.edu.au | jwoo@konkuk.ac.kr |
EM Description
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EM ID
em.detail.idHelp
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EM-434 |
EM-660
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EM-788
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EM-983 | EM-1019 |
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Summary Description
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AUTHOR'S DESCRIPTION: "Definition. Land capability classification is a system of grouping soils primarily on the basis of their capability to produce common cultivated crops and pasture plants without deteriorating over a long period of time." "Class I (1) soils have slight limitations that restrict their use. Class II (2) soils have moderate limitations that reduce the choice of plants or require moderate conservation practices. Class III (3) soils have severe limitations that reduce the choice of plants or require special conservation practices, or both. Class IV (4) soils have very severe limitations that restrict the choice of plants or require very careful management, or both. Class V (5) soils have little or no hazard of erosion but have other limitations, impractical to remove, that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VI (6) soils have severe limitations that make them generally unsuited to cultivation and that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VII (7) soils have very severe limitations that make them unsuited to cultivation and that restrict their use mainly to rangeland, forestland, or wildlife habitat. Class VIII (8) soils and miscellaneous areas have limitations that preclude their use for commercial plant production and limit their use mainly to recreation, wildlife habitat, water supply, or esthetic purposes." [More information can be found at: http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_054226#ex2] | ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. " | 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." | Before delving into Atlantis we would like to provide a little bit of background on the modelling framework and this manual. Atlantis is just one of many marine ecosystem models, originally known as BM2 (BoxModel 2) it was christened Atlantis by Villy Christensen in South Africa in 2001. Marine ecosystem models have existed for more than 50 years, though they have only grown in popular use since the advent of (fast) modern computing power. They have grown from a biophysical focus to include more and more of the human dimensions. This is reflected in the structure of this manual, which sequentially works through the physical then biological before getting into the human dimensions. Atlantis was originally developed with an eye to temperate marine ecosystems and fisheries, though it has grown through time. | Air quality modeling is a useful methodology to investigate air quality degradation in various locations and to analyze effectiveness of emission reduction plans. A comprehensive air quality model usually requires a coordinated set of emissions input of all necessary chemical species. We have developed an anthropogenic emissions processing system for Asia in support of air quality modeling and analysis over Asia (named SMOKE-Asia). The SMOKE (Sparse Matrix Operator kernel Emissions) system, which was developed by U.S. EPA and has been maintained by the Carolina Environmental Program (CEP) of the University of North Carolina, was used to develop our emissions processing system. A merged version of INTEX 2006 and TRACE-P 2000 inventories was used as an initial Asian emissions inventory. The IDA (Inventory Data Analyzer) format was used to create SMOKE-ready emissions. Source Classification Codes (SCCs) and country/state/county (FIPS) code, which are the two key data fields of SMOKE IDA data structure, were created for Asia. The 38 SCCs and 2752 FIPS codes were allocated to our SMOKE-ready emissions for more comprehensive processing. US EPA’s MIMS (Multimedia Integrated Modeling System) Spatial Allocator software, along with many global and regional GIS shapes, were used to create spatial allocation profiles for Asia. Temporal allocation and chemical speciation profiles were partly regionalized using Asia-based studies. Initial data production using the developed SMOKE-Asia system was successfully performed. NOx and VOC emissions for the year 2009 were projected to be increased by 50% from those of 1997. The emission hotspots, such as large cities and large point sources, are distinguished in the domain due to spatial allocation. Regional emission peaks were distinguished due to temporally resolved emission information. The PAR (Paraffin carbon bond) and XYL (Xylene and other polyalkyl aromatics) showed the first and second largest emission rate among VOC species. Most of point source emissions are located in layers 3 to 4, which the altitude range reaches 310–550 m AGL. Qualitative inter-comparison between model output and ground/satellite measurement showed good agreements in terms of spatial and temporal patterns. We expect that the result of this study will provide better air quality modeling inputs, which will act as a major step to improve our understanding of Asian air quality. |
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Specific Policy or Decision Context Cited
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None provided | None identified | None identified | None identified | None provided |
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Biophysical Context
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No additional description provided | stream and river reaches of Michigan | 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. | Marine and coastal ecosystems | Asia |
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EM Scenario Drivers
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No scenarios presented | targeted sport fish biomass | No scenarios presented | No scenarios presented | NA |
EM Relationship to Other EMs or Applications
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EM ID
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EM-434 |
EM-660
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EM-788
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EM-983 | EM-1019 |
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Method Only, Application of Method or Model Run
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Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method Only | 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 |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
em.detail.idHelp
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EM-434 |
EM-660
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EM-788
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EM-983 | EM-1019 |
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Document ID for related EM
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None | None | None | Doc-456 | Doc-459 | Doc-478 |
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EM ID for related EM
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None | None | None | EM-981 | EM-978 | EM-985 | EM-990 | EM-991 | EM-1012 | EM-1021 |
EM Modeling Approach
EM Relationship to Time
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EM ID
em.detail.idHelp
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EM-434 |
EM-660
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EM-788
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EM-983 | EM-1019 |
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EM Temporal Extent
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Not applicable | 2008-2010 | 1988-2014 | Not applicable | 1997-2009 |
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EM Time Dependence
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Not applicable | time-stationary | time-stationary | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | past time |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable | continuous | continuous |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-434 |
EM-660
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EM-788
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EM-983 | EM-1019 |
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Bounding Type
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Not applicable | Watershed/Catchment/HUC | Physiographic or ecological | Not applicable | Geopolitical |
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Spatial Extent Name
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Not applicable | HUCS in Michigan | Nachusa Grasslands | Not applicable | Asia |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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Not applicable | 100,000-1,000,000 km^2 | 10-100 km^2 | Not applicable | 1000-10,000 km^2. |
Spatial Distribution of Computations
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EM ID
em.detail.idHelp
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EM-434 |
EM-660
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EM-788
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EM-983 | EM-1019 |
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EM Spatial Distribution
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Not applicable | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | Not applicable | spatially lumped (in all cases) |
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Spatial Grain Type
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Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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Not applicable | reach in HUC | Area varies by site | Not applicable | Not applicable |
EM Structure and Computation Approach
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EM ID
em.detail.idHelp
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EM-434 |
EM-660
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EM-788
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EM-983 | EM-1019 |
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EM Computational Approach
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Not applicable | Numeric | Analytic | Analytic | Numeric |
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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-434 |
EM-660
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EM-788
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EM-983 | EM-1019 |
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Model Calibration Reported?
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Not applicable | No | No | Not applicable | Unclear |
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Model Goodness of Fit Reported?
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Not applicable | Yes | No | Not applicable | Unclear |
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Goodness of Fit (metric| value | unit)
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None |
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None | None | None |
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Model Operational Validation Reported?
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No | No | No | Not applicable | Yes |
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Model Uncertainty Analysis Reported?
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Not applicable | No | No | Not applicable | Unclear |
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Model Sensitivity Analysis Reported?
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Not applicable | No | No | Not applicable | Unclear |
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Model Sensitivity Analysis Include Interactions?
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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])
em.detail.relationToSpaceTerrestrialHelp
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| EM-434 |
EM-660
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EM-788
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EM-983 | EM-1019 |
| None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
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| EM-434 |
EM-660
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EM-788
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EM-983 | EM-1019 |
| None | None | None | None |
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Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-434 |
EM-660
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EM-788
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EM-983 | EM-1019 |
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Centroid Latitude
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Not applicable | 45.12 | 41.89 | Not applicable | 38.63 |
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Centroid Longitude
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Not applicable | 85.18 | -89.34 | Not applicable | 117.79 |
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Centroid Datum
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Not applicable | WGS84 | WGS84 | Not applicable | WGS84 |
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Centroid Coordinates Status
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Not applicable | Estimated | Provided | Not applicable | Estimated |
Environments and Scales Modeled
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EM ID
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EM-434 |
EM-660
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EM-788
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EM-983 | EM-1019 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas | Atmosphere |
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Specific Environment Type
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None identified | stream reaches | Restored prairie, prairie remnants, and cropland | Multiple | Asian atmosphere |
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EM Ecological Scale
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Ecological scale corresponds to 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 | Not applicable |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
em.detail.idHelp
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EM-434 |
EM-660
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EM-788
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EM-983 | EM-1019 |
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EM Organismal Scale
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Not applicable | Not applicable | Species | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-434 |
EM-660
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EM-788
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EM-983 | EM-1019 |
| 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.detail.cicesHelp
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| EM-434 |
EM-660
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EM-788
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EM-983 | EM-1019 |
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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-434 |
EM-660
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EM-788
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EM-983 | EM-1019 |
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None |
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