EcoService Models Library (ESML)

Compare EMs

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.

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Comparing:

EM-104
EM-106
EM-630
EM-845
EM-941

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-104	EM-106	EM-630	EM-845	EM-941
EM Short Name em.detail.shortNameHelp ?	SPARROW, Northeastern USA	Value of Habitat for Shrimp, Campeche, Mexico	WaterWorld v2, Santa Basin, Peru	Red-winged blackbird abun, Piedmont region, USA	ESTIMAP - Pollination potential, Iran
EM Full Name em.detail.fullNameHelp ?	SPARROW (SPAtially Referenced Regressions On Watershed Attributes), Northeastern USA	Value of Habitat for Shrimp, Campeche, Mexico	WaterWorld v2, Santa Basin, Peru	Red-winged blackbird abundance, Piedmont ecoregion, USA	ESTIMAP - Pollination potential, Iran
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	US EPA	None	None	None	None
EM Source Document ID	86	227	368	405	434
Document Author em.detail.documentAuthorHelp ?	Moore, R. B., Johnston, C.M., Smith, R. A. and Milstead, B.	Barbier, E. B., and Strand, I.	Van Soesbergen, A. and M. Mulligan	Riffel, S., Scognamillo, D., and L. W. Burger	Rahimi, E., Barghjelveh, S., and P. Dong
Document Year em.detail.documentYearHelp ?	2011	1998	2018	2008	2020
Document Title em.detail.sourceIdHelp ?	Source and delivery of nutrients to receiving waters in the northeastern and mid-Atlantic regions of the United States	Valuing mangrove-fishery linkages: A case study of Campeche, Mexico	Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru	Effects of the Conservation Reserve Program on northern bobwhite and grassland birds	Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran)
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-104	EM-106	EM-630	EM-845	EM-941
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	www.policysupport.org/waterworld	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Richard Moore	E.B. Barbier	Arnout van Soesbergen	Sam Riffell	Ehsan Rahini
Contact Address	U.S. Environmental Protection Agency, 27 Tarzwell Drive, Narragansett, Rhode Island 02882	Environment Department, University of York, York YO1 5DD, UK	Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK	Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA	Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran
Contact Email	rmoore@usgs.gov	Not reported	arnout.van_soesbergen@kcl.ac.uk	sriffell@cfr.msstate.edu	ehsanrahimi666@gmail.com

EM Description

EM ID em.detail.idHelp ?	EM-104	EM-106	EM-630	EM-845	EM-941
Summary Description em.detail.summaryDescriptionHelp ?	AUTHOR'S DESCRIPTION: "SPAtially Referenced Regressions On Watershed attributes (SPARROW) nutrient models were developed for the Northeastern and Mid-Atlantic (NE US) regions of the United States to represent source conditions for the year 2002. The model developed to examine the source and delivery of nitrogen to the estuaries of nine large rivers along the NE US Seaboard indicated that agricultural sources contribute the largest percentage (37%) of the total nitrogen load delivered to the estuaries"	AUTHOR'S DESCRIPTION: "We assume throughout that shrimp harvesting occurs through open access management that yields production which is exported internationally, and we modify a standard open access fishery model to account explicitly for the effect of the mangrove area on carrying capacity and thus production.We derive the conditions determining the long-run equilibrium of the model, including the comparative static effects of a change in mangrove area, on this equilibrium. Through regressing a relationship between shrimp harvest, effort and mangrove area over time, we estimate parameters based on the combinations of the bioeconomic parameters of the model determining the comparative statics. By incorporating additional economic data, we are able to simulate an estimate of the effect of changes in mangrove area in Laguna de Terminos on the production and value of shrimp harvests in Campeche state." (153)	ABSTRACT: "Water resources in the Santa basin in the Peruvian Andes are increasingly under pressure from climate change and population increases. Impacts of temperature-driven glacier retreat on stream flow are better studied than those from precipitation changes, yet present and future water resources are mostly dependent on precipitation which is more difficult to predict with climate models. This study combines a broad range of projections from climate models with a hydrological model (WaterWorld), showing a general trend towards an increase in water availability due to precipitation increases over the basin. However, high uncertainties in these projections necessitate the need for basin-wide policies aimed at increased adaptability." AUTHOR'S DESCRIPTION: "WaterWorld is a fully distributed, process-based hydrological model that utilises remotely sensed and globally available datasets to support hydrological analysis and decision-making at national and local scales globally, with a particular focus on un-gauged and/or data-poor environments, which makes it highly suited to this study. The model (version 2) currently runs on either 10 degree tiles, large river basins or countries at 1-km2 resolution or 1 degree tiles at 1-ha resolution utilising different datasets. It simulates a hydrological baseline as a mean for the period 1950-2000 and can be used to calculate the hydrological impact of scenarios of climate change, land use change, land management options, impacts of extractives (oil & gas and mining) and impacts of changes in population and demography as well as combinations of these. The model is ‘self parameterising’ (Mulligan, 2013a) in the sense that all data required for model application anywhere in the world is provided with the model, removing a key barrier to model application. However, if users have better data than those provided, it is possible to upload these to WaterWorld as GIS files and use them instead. Results can be viewed visually within the web browser or downloaded as GIS maps. The model’s equations and processes are described in more detail in Mulligan and Burke (2005) and Mulligan (2013b). The model parameters are not routinely calibrated to observed flows as it is designed for hydrological scenario analysis in which the physical basis of its parameters must be retained and the model is also often used in un-gauged basins. Calibration is inappropriate under these circumstances (Sivapalan et al., 2003). The freely available nature of the model means that anyone can apply it and replicate the results shown here. WaterWorld’s (V2) snow and ice module is capable of simulating the processes of melt water production, snow fall and snow pack, making this version highly suited to the current application. The model component is based on a full energy-balance for snow accumulation and melting based on Walter et al., (2005) with input data provided globally by the SimTerra database (Mulligan, 2011) upon which the model r	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."	Abstract: ". ..we used the ESTIMAP model to improve the results of the Lonsdorf model. For this, we included the effects of roads, railways, rivers, wetlands, lakes, altitude, climate, and ecosystem boundaries in the ESTIMAP modeling and compared the results with the Lonsdorf model. The results of the Lonsdorf model showed that the majority of Iran had a very low potential for providing pollination service and only three percent of the northern and western parts of Iran had high potential. However, the results of the ESTIMAP model showed that 16% of Iran had a high potential to provide pollination that covers most of the northern and southern parts of the country. The results of the ESTIMAP model for pollination mapping in Iran showed the Lonsdorf model of estimating pollination service can be improved through considering other relevant factors."
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	water-quality assessment, total maximum daily load(TMDL) determination	None identified	None identified	None reported	None reported
Biophysical Context	Norteneastern region (U.S.); Mid-Atlantic region (U.S.)	Gulf of Mexico; mangrove-lagoon system	Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal.	Conservation Reserve Program lands left to go fallow	None additional
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100	N/A	N/A

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-104	EM-106	EM-630	EM-845	EM-941
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application (multiple runs exist)	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	Application of existing model

Related EMs (for example, other versions or derivations of this EM) described in ESML

EM ID em.detail.idHelp ?	EM-104	EM-106	EM-630	EM-845	EM-941
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	None	None	None	Doc-405	Doc-432
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-185 \| EM-319	None	EM-831 \| EM-838 \| EM-839 \| EM-840 \| EM-841 \| EM-842 \| EM-843 \| EM-844 \| EM-846 \| EM-847	EM-939

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-104	EM-106	EM-630	EM-845	EM-941
EM Temporal Extent em.detail.tempExtentHelp ?	2002 ? Comment: Several nationwide database development and modeling efforts were necessary to create models consistent with 2002 conditions.	1980-1990	1950-2071	2008	2020
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-dependent	time-stationary	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	both	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	discrete	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	1	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Year	Month	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-104	EM-106	EM-630	EM-845	EM-941
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or Ecological	Watershed/Catchment/HUC	Physiographic or ecological	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	NE U.S. Regions	Laguna de Terminos Mangrove system	Santa Basin	Piedmont Ecoregion	Iran
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	100-1000 km^2	10,000-100,000 km^2	100,000-1,000,000 km^2	>1,000,000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-104	EM-106	EM-630	EM-845	EM-941
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)	spatially distributed (in at least some cases) ? Comment: Varies by inputs, but results are for areas of country
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	Not applicable	area, for pixel or radial feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	30 x 30 m	1 km x 1 km	1 km2	Not applicable	ha^2

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-104	EM-106	EM-630	EM-845	EM-941
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	*	Analytic	Numeric
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-104	EM-106	EM-630	EM-845	EM-941
Model Calibration Reported? em.detail.calibrationHelp ?	Yes	Yes	No	Yes	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes ? Comment: R-squared of .97 refers to the modelled loading whereas .83 refers to yield (see table 1, pg 972 for more information)	Yes	No	No	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	R^2 \| 0.97 \| Not applicable Mean Square Error \| 0.12 \| Not applicable Root Mean Square Error \| 0.35 \| Not applicable R-squared \| 0.83 \| Not applicable	Adjusted R^2 \| 0.745 \| Not applicable F-stat \| 30.216 \| Not applicable SE of regression \| 639745.7 \| Not applicable	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	No	Yes	No	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	Unclear	Yes	No	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	Yes	Yes	No	Yes	No
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Unclear	Unclear	Not applicable	Unclear	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])

EM-104	EM-106	EM-630	EM-845	EM-941
North America United States Vermont New York West Virginia Massachusetts Maine Connecticut New Hampshire Delaware Maryland New Jersey Pennsylvania Virgina	North America Mexico	None	North America United States North Carolina Georgia Alabama Maryland New Jersey South Carolina Pennsylvania	Europe ? Comment: Model for Iran - no form preset id for country

Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)

EM-104	EM-106	EM-630	EM-845	EM-941
None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Southern Gulf of Mexico	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-104	EM-106	EM-630	EM-845	EM-941
Centroid Latitude em.detail.ddLatHelp ?	42	18.61	-9.05	36.23	32.29
Centroid Longitude em.detail.ddLongHelp ?	-73	-91.55	-77.81	-81.9	53.68
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-104	EM-106	EM-630	EM-845	EM-941
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Rivers and Streams \| Ground Water \| Terrestrial Environment (sub-classes not fully specified) \| Agroecosystems \| Atmosphere	Near Coastal Marine and Estuarine	None	Grasslands	Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	none	Mangrove	tropical, coastal to montane	grasslands	terrestrial land types
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is coarser than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Other or unclear (comment)	Ecological scale corresponds to 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 ID em.detail.idHelp ?	EM-104	EM-106	EM-630	EM-845	EM-941
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Guild or Assemblage	Not applicable	Species	Not applicable

Taxonomic level and name of organisms or groups identified

EM-104	EM-106	EM-630	EM-845	EM-941
None Available	other Shrimp	None Available	Species Redwinged blackbird	Species Apis mellifera Apis florea

EnviroAtlas URL

EM-104	EM-106	EM-630	EM-845	EM-941
National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, U.S. EPA (Omernik) ecoregions, Total Annual Nitrogen Deposition	GAP Ecological Systems, Big game hunting recreation demand	Average Annual Precipitation	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions	The National Hydrography Dataset (NHD), GAP Ecological Systems, Average Annual Precipitation, Waterbody area

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-104	EM-106	EM-630	EM-845	EM-941
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of freshwaters Mediation of waste, toxics and other nuisances Mediation by biota Bio-remediation by micro-organisms, algae, plants, and animals Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal

<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>

(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)

EM-104	EM-106	EM-630	EM-845	EM-941
Rivers and Streams (111) Water (3) Liquid water Quality Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Shrimp Stock Indicator	None	Grasslands (23) Fauna (4) Birds Stock Indicator	Forests (21) Fauna (4) Wildlife Stock Indicator Scrubland/Shrubland (24) Fauna (4) Wildlife Stock Indicator Urban/Surburban (27) Fauna (4) Wildlife Stock Indicator

EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

EM Variables by Category

All EM Descriptors

EM Descriptors by Modeling Concepts

Comparing:

EM Identity and Description

Related EMs (for example, other versions or derivations of this EM) described in ESML

EM Modeling Approach

Comment:

Comment:

Comment:

EM Locations, Environments, Ecology

Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])

Comment:

Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)

Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

Taxonomic level and name of organisms or groups identified

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)

<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>

(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)