EcoService Models Library (ESML)
Home
Search EMs
My
EMs 
Learn about
ESML
View ESML Data Maploading
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.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
|
EM ID
em.detail.idHelp
?
|
EM-103 |
EM-660 
|
EM-788
|
EM-846 | EM-981 |
|
EM Short Name
em.detail.shortNameHelp
?
|
Birds in estuary habitats, Yaquina Estuary, WA, USA | RUM: Valuing fishing quality, Michigan, USA | Wild bees over 26 yrs of restored prairie, IL, USA | Indigo bunting abund, Piedmont region, USA | Atlantis ecosystem biology submodel |
|
EM Full Name
em.detail.fullNameHelp
?
|
Bird use of estuarine habitats, Yaquina Estuary, WA, USA | 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 | Indigo bunting abundance, Piedmont ecoregion, USA | Calibrating process-based marine ecosystem models: An example case using Atlantis |
|
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
US EPA | None | None | None | None |
|
EM Source Document ID
|
275 |
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 | 405 | 459 |
|
Document Author
em.detail.documentAuthorHelp
?
|
Frazier, M. R., Lamberson, J. O. and Nelson, W. G. | 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 | Riffel, S., Scognamillo, D., and L. W. Burger | Pethybridge, H. R., Weijerman, M., Perrymann, H., Audzijonyte, A., Porobic, J., McGregor, V., … & Fulton, E. |
|
Document Year
em.detail.documentYearHelp
?
|
2014 | 2014 | 2017 | 2008 | 2019 |
|
Document Title
em.detail.sourceIdHelp
?
|
Intertidal habitat utilization patterns of birds in a Northeast Pacific estuary | Valuing recreational fishing quality at rivers and streams | Wild bee community change over a 26-year chronosequence of restored tallgrass prairie | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Calibrating process-based marine ecosystem models: An example case using Atlantis |
|
Document Status
em.detail.statusCategoryHelp
?
|
Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
|
Comments on Status
em.detail.commentsOnStatusHelp
?
|
Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
Software and Access
|
EM ID
em.detail.idHelp
?
|
EM-103 |
EM-660
|
EM-788
|
EM-846 | EM-981 |
| Not applicable | Not applicable | Not applicable | Not applicable | https://noaa-fisheries-integrated-toolbox.github.io/Atlantis | |
|
Contact Name
em.detail.contactNameHelp
?
|
M. R. Frazier ?Comment:Present address: M. R. Frazier National Center for Ecological Analysis and Synthesis, 735 State St. Suite 300, Santa Barbara, CA 93101, USA |
Richard Melstrom | Sean R. Griffin | Sam Riffell | Heidi R. Pethybridge |
|
Contact Address
|
Western Ecology Division, Office of Research and Development, U.S. Environmental Protection Agency, Pacific coastal Ecology Branch, 2111 SE marine Science Drive, Newport, OR 97365 | 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. | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | CSIRO Oceans and Atmosphere, GPO Box 1538, Hobart, Tasmania, 7000, Australia |
|
Contact Email
|
frazier@nceas.ucsb.edu | melstrom@okstate.edu | srgriffin108@gmail.com | sriffell@cfr.msstate.edu | Heidi.Pethybridge@csiro.au |
EM Description
|
EM ID
em.detail.idHelp
?
|
EM-103 |
EM-660
|
EM-788
|
EM-846 | EM-981 |
|
Summary Description
em.detail.summaryDescriptionHelp
?
|
AUTHOR'S DESCRIPTION: "To describe bird utilization patterns of intertidal habitats within Yaquina estuary, Oregon, we conducted censuses to obtain bird species and abundance data for the five dominant estuarine intertidal habitats: Zostera marina (eelgrass), Upogebia (mud shrimp)/ mudflat, Neotrypaea (ghost shrimp)/sandflat, Zostera japonica (Japanese eelgrass), and low marsh. EPFs were developed for the following metrics of bird use: standardized species richness; Shannon diversity; and density for the following four groups: all birds, all birds excluding gulls, waterfowl (ducks and geese), and shorebirds." | 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." | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds." | Calibration of complex, process-based ecosystem models is a timely task with modellers challenged by many parameters, multiple outputs of interest and often a scarcity of empirical data. Incorrect calibration can lead to unrealistic ecological and socio-economic predictions with the modeller’s experience and available knowledge of the modelled system largely determining the success of model calibration. Here we provide an overview of best practices when calibrating an Atlantis marine ecosystem model, a widely adopted framework that includes the parameters and processes comprised in many different ecosystem models. We highlight the importance of understanding the model structure and data sources of the modelled system. We then focus on several model outputs (biomass trajectories, age distributions, condition at age, realised diet proportions, and spatial maps) and describe diagnostic routines that can assist modellers to identify likely erroneous parameter values. We detail strategies to fine tune values of four groups of core parameters: growth, predator-prey interactions, recruitment and mortality. Additionally, we provide a pedigree routine to evaluate the uncertainty of an Atlantis ecosystem model based on data sources used. Describing best and current practices will better equip future modellers of complex, processed-based ecosystem models to provide a more reliable means of explaining and predicting the dynamics of marine ecosystems. Moreover, it promotes greater transparency between modellers and end-users, including resource managers. |
|
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
None identified | None identified | None identified | None reported | N/A |
|
Biophysical Context
|
Estuarine intertidal, eelgrass, mudflat, sandflat and low marsh | 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. | Conservation Reserve Program lands left to go fallow | Marine ecosystem |
|
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
No scenarios presented | targeted sport fish biomass | No scenarios presented | N/A | No scenarios presented |
EM Relationship to Other EMs or Applications
|
EM ID
em.detail.idHelp
?
|
EM-103 |
EM-660
|
EM-788
|
EM-846 | EM-981 |
|
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only |
|
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
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
em.detail.relatedEmHelp
?
|
EM ID
em.detail.idHelp
?
|
EM-103 |
EM-660
|
EM-788
|
EM-846 | EM-981 |
|
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
None | None | None | Doc-405 | Doc-456 |
|
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
None | None | None | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-843 | EM-844 | EM-845 | EM-847 | EM-978 | EM-983 | EM-985 | EM-990 | EM-991 |
EM Modeling Approach
EM Relationship to Time
|
EM ID
em.detail.idHelp
?
|
EM-103 |
EM-660
|
EM-788
|
EM-846 | EM-981 |
|
EM Temporal Extent
em.detail.tempExtentHelp
?
|
December 2007 - November 2008 | 2008-2010 | 1988-2014 | 2008 | Not applicable |
|
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-stationary | time-stationary | time-stationary | time-stationary | time-dependent |
|
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
|
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | continuous |
|
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
|
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
|
EM ID
em.detail.idHelp
?
|
EM-103 |
EM-660
|
EM-788
|
EM-846 | EM-981 |
|
Bounding Type
em.detail.boundingTypeHelp
?
|
Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Not applicable |
|
Spatial Extent Name
em.detail.extentNameHelp
?
|
Yaquina Estuary (intertidal), Oregon, USA | HUCS in Michigan | Nachusa Grasslands | Piedmont Ecoregion | Not applicable |
|
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
1-10 km^2 | 100,000-1,000,000 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | Not applicable |
Spatial Distribution of Computations
|
EM ID
em.detail.idHelp
?
|
EM-103 |
EM-660
|
EM-788
|
EM-846 | EM-981 |
|
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | Not applicable |
|
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
other (habitat type) | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable |
|
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
0.87-104.29 ha | reach in HUC | Area varies by site | Not applicable | Not applicable |
EM Structure and Computation Approach
|
EM ID
em.detail.idHelp
?
|
EM-103 |
EM-660
|
EM-788
|
EM-846 | EM-981 |
|
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Analytic | Numeric | Analytic | Analytic | Analytic |
|
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic | deterministic | deterministic | deterministic |
|
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
|
|
|
|
Model Checking Procedures Used
|
EM ID
em.detail.idHelp
?
|
EM-103 |
EM-660
|
EM-788
|
EM-846 | EM-981 |
|
Model Calibration Reported?
em.detail.calibrationHelp
?
|
Unclear | No | No | Yes | Yes |
|
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
No | Yes | No | No | Not applicable |
|
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
None |
|
None | None | None |
|
Model Operational Validation Reported?
em.detail.validationHelp
?
|
No | No | No | No | Not applicable |
|
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
No | No | No | No | Not applicable |
|
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
No | No | No | Yes | Not applicable |
|
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
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
?
| EM-103 |
EM-660
|
EM-788
|
EM-846 | EM-981 |
|
|
|
|
None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
?
| EM-103 |
EM-660
|
EM-788
|
EM-846 | EM-981 |
|
None | None | None | None |
Centroid Lat/Long (Decimal Degree)
|
EM ID
em.detail.idHelp
?
|
EM-103 |
EM-660
|
EM-788
|
EM-846 | EM-981 |
|
Centroid Latitude
em.detail.ddLatHelp
?
|
44.62 | 45.12 | 41.89 | 36.23 | Not applicable |
|
Centroid Longitude
em.detail.ddLongHelp
?
|
-124.06 | 85.18 | -89.34 | -81.9 | Not applicable |
|
Centroid Datum
em.detail.datumHelp
?
|
None provided | WGS84 | WGS84 | WGS84 | Not applicable |
|
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Provided | Estimated | Provided | Estimated | Not applicable |
Environments and Scales Modeled
|
EM ID
em.detail.idHelp
?
|
EM-103 |
EM-660
|
EM-788
|
EM-846 | EM-981 |
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Near Coastal Marine and Estuarine | Rivers and Streams | Agroecosystems | Grasslands | 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 |
|
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Estuarine intertidal | stream reaches | Restored prairie, prairie remnants, and cropland | grasslands | Multiple |
|
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
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 | Ecological scale corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
em.detail.nameOfOrgsOrGroupsHelp
?
|
EM ID
em.detail.idHelp
?
|
EM-103 |
EM-660
|
EM-788
|
EM-846 | EM-981 |
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Guild or Assemblage | Not applicable | Species | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
?
| EM-103 |
EM-660
|
EM-788
|
EM-846 | EM-981 |
|
|
|
|
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
?
| EM-103 |
EM-660
|
EM-788
|
EM-846 | EM-981 |
|
|
|
|
|
<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
?
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
?
| EM-103 |
EM-660
|
EM-788
|
EM-846 | EM-981 |
|
|
None |
|
|