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-345 
|
EM-660
|
EM-698 | EM-862 |
|
EM Short Name
em.detail.shortNameHelp
?
|
InVEST habitat quality, Puli Township, Taiwan | RUM: Valuing fishing quality, Michigan, USA | Fish species richness, St. Croix, USVI | Recreational fishery index, USA |
|
EM Full Name
em.detail.fullNameHelp
?
|
InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) habitat quality, Puli Township, Taiwan | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | Fish Species Richness, Buck Island, St. Croix , USVI | Recreational fishery index for streams and rivers, USA |
|
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
InVEST | None | None | US EPA |
|
EM Source Document ID
|
308 |
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. |
355 | 414 |
|
Document Author
em.detail.documentAuthorHelp
?
|
Wu, C.-F., Lin, Y.-P., Chiang, L.-C. and Huang, T. | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold |
|
Document Year
em.detail.documentYearHelp
?
|
2014 | 2014 | 2007 | 2021 |
|
Document Title
em.detail.sourceIdHelp
?
|
Assessing highway's impacts on landscape patterns and ecosystem services: A case study in Puli Township, Taiwan | Valuing recreational fishing quality at rivers and streams | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | Correspondence between a recreational fishery index and ecological condition for U.S.A. streams and rivers. |
|
Document Status
em.detail.statusCategoryHelp
?
|
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 |
Software and Access
|
EM ID
em.detail.idHelp
?
|
EM-345
|
EM-660
|
EM-698 | EM-862 |
| https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable | |
|
Contact Name
em.detail.contactNameHelp
?
|
Yu-Pin Lin ?Comment:Tel.: +886 2 3366 3467; fax: +866 2 2368 6980 |
Richard Melstrom | Simon Pittman | Gregg Lomnicky |
|
Contact Address
|
Not reported | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | 1305 East-West Highway, Silver Spring, MD 20910, USA | 200 SW 35th St., Corvallis, OR, 97333 |
|
Contact Email
|
yplin@ntu.edu.tw | melstrom@okstate.edu | simon.pittman@noaa.gov | lomnicky.gregg@epa.gov |
EM Description
|
EM ID
em.detail.idHelp
?
|
EM-345
|
EM-660
|
EM-698 | EM-862 |
|
Summary Description
em.detail.summaryDescriptionHelp
?
|
Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "...To assess the effects of different land-use scenarios under various agricultural and environmental conservation policy regimes, this study applies an integrated approach to analyze the effects of Highway 6 construction on Puli Township...A habitat quality assessment using the InVEST model indicates that the conservation of agricultural and forested lands improves habitat quality and preserves rare habitats…" AUTHOR'S DESCRIPTION: "In total, three land-use planning scenarios were simulated based on government policies in Taiwan’s Hillside Protection Act and Regulations on Non-Urban Land Utilization Control. The baseline planning scenario, Scenario A, allows land-use development with-out land-use controls (Appendix Fig. S2), meaning that land-use changes can occur anywhere. Scenario B is based on the Regulations on Non-Urban Land Utilization Control and the maintenance of agricultural areas, such that land-use changes cannot occur in agricultural areas. Scenario C protects agricultural land, hillsides, and naturally forested areas from development...The biodiversity evaluation module in the InVEST model assessed the degree of change in habitat quality and habitat rarity under three scenarios. In the InVEST model, habitat quality is primarily threatened by four factors: the relative impact of each threat; the relative sensitivity of each habitat type to each threat; the distance between habitats and sources of threats; as well as the relative degree to which land is legally protected..." Use of other models in conjunction with this model: Land use data for future scenarios modeled in InVEST were derived from a linear regression model of land use change, and the CLUE-S (Conversion of Land Use and its Effects at Small regional extent) model for apportioning those changes to the landscape. | 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: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems." | ABSTRACT: [Sport fishing is an important recreational and economic activity, especially in Australia, Europe and North America, and the condition of sport fish populations is a key ecological indicator of water body condition for millions of anglers and the public. Despite its importance as an ecological indicator representing the status of sport fish populations, an index for measuring this ecosystem service has not been quantified by analyzing actual fish taxa, size and abundance data across the U.S.A. Therefore, we used game fish data collected from 1,561 stream and river sites located throughout the conterminous U.S.A. combined with specific fish species and size dollar weights to calculate site-specific recreational fishery index (RFI) scores. We then regressed those scores against 38 potential site-specific environmental predictor variables, as well as site-specific fish assemblage condition (multimetric index; MMI) scores based on entire fish assemblages, to determine the factors most associated with the RFI scores. We found weak correlations between RFI and MMI scores and weak to moderate correlations with environmental variables, which varied in importance with each of 9 ecoregions. We conclude that the RFI is a useful indicator of a stream ecosystem service, which should be of greater interest to the U.S.A. public and traditional fishery management agencies than are MMIs, which tend to be more useful for ecologists, environmentalists and environmental quality agencies.] |
|
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
Environmental effects of Highway 6 construction on Puli Township, Taiwan | None identified | None provided | None identified |
|
Biophysical Context
|
26% of the land area is categorized as plain and the remaining 74% is categorized as hilly with elevations of 380-700 m. Predominant land classes are forested (47.4%), cultivated (31.8%), and built-up (14.5%). Average annual rainfall is 2120 mm, and average annual temperature is 21°C. The soil in the eastern portion of the basin is primarily clay, and primarily loess elsewhere. | stream and river reaches of Michigan | Hard and soft benthic habitat types approximately to the 33m isobath | None |
|
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
Three scenarios; baseline planning (A, without land-use controls), scenario B based on maintenance of agriculture, scenario C protects agriculture, hillsides and naturally forested areas. | targeted sport fish biomass | No scenarios presented | N/A |
EM Relationship to Other EMs or Applications
|
EM ID
em.detail.idHelp
?
|
EM-345
|
EM-660
|
EM-698 | EM-862 |
|
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application |
|
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
Application of existing model | New or revised model | Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
em.detail.relatedEmHelp
?
|
EM ID
em.detail.idHelp
?
|
EM-345
|
EM-660
|
EM-698 | EM-862 |
|
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
Doc-278 | None | Doc-355 | None |
|
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
EM-143 | None | EM-590 | EM-699 | None |
EM Modeling Approach
EM Relationship to Time
|
EM ID
em.detail.idHelp
?
|
EM-345
|
EM-660
|
EM-698 | EM-862 |
|
EM Temporal Extent
em.detail.tempExtentHelp
?
|
2010-2025 | 2008-2010 | 2000-2005 | 2013-2014 |
|
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-stationary | time-stationary | time-stationary | time-dependent |
|
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
Not applicable | Not applicable | Not applicable | past time |
|
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
Not applicable | Not applicable | Not applicable | discrete |
|
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
Not applicable | Not applicable | Not applicable | 1 |
|
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable | Not applicable | Not applicable | Year |
EM Spatial Extent
|
EM ID
em.detail.idHelp
?
|
EM-345
|
EM-660
|
EM-698 | EM-862 |
|
Bounding Type
em.detail.boundingTypeHelp
?
|
Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical |
|
Spatial Extent Name
em.detail.extentNameHelp
?
|
Puli Township, Nantou County | HUCS in Michigan | SW Puerto Rico, | United States |
|
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
100-1000 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | >1,000,000 km^2 |
Spatial Distribution of Computations
|
EM ID
em.detail.idHelp
?
|
EM-345
|
EM-660
|
EM-698 | EM-862 |
|
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 distributed (in at least some cases) |
|
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) |
|
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
40 m x 40 m | reach in HUC | not reported | stream reach (site) |
EM Structure and Computation Approach
|
EM ID
em.detail.idHelp
?
|
EM-345
|
EM-660
|
EM-698 | EM-862 |
|
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Analytic | Numeric | Analytic | Analytic |
|
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic | deterministic | deterministic |
|
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
|
|
|
Model Checking Procedures Used
|
EM ID
em.detail.idHelp
?
|
EM-345
|
EM-660
|
EM-698 | EM-862 |
|
Model Calibration Reported?
em.detail.calibrationHelp
?
|
Unclear | No | No | No |
|
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
Not applicable | Yes | Yes | No |
|
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
None |
|
|
None |
|
Model Operational Validation Reported?
em.detail.validationHelp
?
|
Not applicable | No | Yes | No |
|
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
No | No | No | No |
|
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
No | No | Yes | No |
|
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | Not applicable | No | 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-345
|
EM-660
|
EM-698 | EM-862 |
Comment:Taiwan |
|
None |
|
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
?
|
EM-345
|
EM-660
|
EM-698 | EM-862 |
| None | None |
|
None |
Centroid Lat/Long (Decimal Degree)
|
EM ID
em.detail.idHelp
?
|
EM-345
|
EM-660
|
EM-698 | EM-862 |
|
Centroid Latitude
em.detail.ddLatHelp
?
|
23.98 | 45.12 | 17.79 | 36.21 |
|
Centroid Longitude
em.detail.ddLongHelp
?
|
120.96 | 85.18 | -64.62 | -113.76 |
|
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | WGS84 | WGS84 |
|
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Estimated | Estimated | Estimated | Estimated |
Environments and Scales Modeled
|
EM ID
em.detail.idHelp
?
|
EM-345
|
EM-660
|
EM-698 | EM-862 |
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Rivers and Streams | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Rivers and Streams | Near Coastal Marine and Estuarine | Rivers and Streams |
|
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Predominantly an agricultural area with associated forest land | stream reaches | shallow coral reefs | reach |
|
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of 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-345
|
EM-660
|
EM-698 | EM-862 |
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Community | Not applicable | Guild or Assemblage | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
?
|
EM-345
|
EM-660
|
EM-698 | EM-862 |
| None Available |
|
|
None Available |
EnviroAtlas URL
|
EM-345
|
EM-660
|
EM-698 | EM-862 |
| Percent GAP Status 1 & 2 | The National Hydrography Dataset (NHD), The Watershed Boundary Dataset (WBD), Enabling Conditions, Employment Rate | None Available | None Available |
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-345
|
EM-660
|
EM-698 | EM-862 |
|
|
|
|
<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-345
|
EM-660
|
EM-698 | EM-862 |
|
|
|
|