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-193
EM-414
EM-415
EM-657

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-193	EM-414	EM-415	EM-657
EM Short Name em.detail.shortNameHelp ?	Cultural ecosystem services, Bilbao, Spain	SAV occurrence, St. Louis River, MN/WI, USA	Esocid spawning, St. Louis River, MN/WI, USA	REQI (River Ecosystem Quality Index), Italy
EM Full Name em.detail.fullNameHelp ?	Cultural ecosystem services, Bilbao, Spain	Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA	Esocid spawning, St. Louis River estuary, MN & WI, USA	REQI (River Ecosystem Quality Index), Marecchia River, Italy
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	None ? Comment: EU Mapping Studies	US EPA	US EPA	None
EM Source Document ID	191	330	332	378
Document Author em.detail.documentAuthorHelp ?	Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H.	Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke	Ted R. Angradi, David W. Bolgrien, Jonathon J. Launspach, Brent J. Bellinger, Matthew A. Starry, Joel C. Hoffman, Mike E. Sierszen, Anett S. Trebitz, and Tom P. Hollenhorst	Santolini, R, E. Morri, G. Pasini, G. Giovagnoli, C. Morolli, and G. Salmoiraghi
Document Year em.detail.documentYearHelp ?	2013	2013	2016	2014
Document Title em.detail.sourceIdHelp ?	Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning	Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary	Mapping ecosystem service indicators of a Great Lakes estuarine Area of Concern	Assessing the quality of riparian areas: the case of River Ecosystem Quality Index applied to the Marecchia river (Italy)
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-193	EM-414	EM-415	EM-657
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Izaskun Casado-Arzuaga	Ted R. Angradi	Ted R. Angradi	Elisa Morri
Contact Address	Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain	U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA	United States Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboraty, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804 USA	Dept. of Earth, Life, and Environmental Sciences, Urbino university, via ca le suore, campus scientifico Enrico Mattei, Urbino 61029 Italy
Contact Email	izaskun.casado@ehu.es	angradi.theodore@epa.gov	angradi.theodore@epa.gov	elisa.morri@uniurb.it

EM Description

EM ID em.detail.idHelp ?	EM-193	EM-414	EM-415	EM-657
Summary Description em.detail.summaryDescriptionHelp ?	ABSTRACT "This paper presents a method to quantify cultural ecosystem services (ES) and their spatial distribution in the landscape based on ecological structure and social evaluation approaches. The method aims to provide quantified assessments of ES to support land use planning decisions. A GIS-based approach was used to estimate and map the provision of recreation and aesthetic services supplied by ecosystems in a peri-urban area located in the Basque Country, northern Spain. Data of two different public participation processes (frequency of visits to 25 different sites within the study area and aesthetic value of different landscape units) were used to validate the maps. Three maps were obtained as results: a map showing the provision of recreation services, an aesthetic value map and a map of the correspondences and differences between both services. The data obtained in the participation processes were found useful for the validation of the maps. A weak spatial correlation was found between aesthetic quality and recreation provision services, with an overlap of the highest values for both services only in 7.2 % of the area. A consultation with decision-makers indicated that the results were considered useful to identify areas that can be targeted for improvement of landscape and recreation management."	ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake."	ABSTRACT: "Estuaries provide multiple ecosystem services from which humans benefit…We described an approach, with examples, for assessing how local-scale actions affect the extent and distribution of coastal ecosystem services, using the St. Louis River estuary (SLRE) of western Lake Superior as a case study. We based our approach on simple models applied to spatially explicity biophysical data that allows us to map the providing area of ecosystem services at high resolution (10-m^2 pixel) across aquatic and riparian habitats…Aspects of our approach can be adapted by communities for use in support of local decision-making." AUTHOR'S DESCRIPTION: "We derived the decision criteria used to map the IEGS habitat proxy of esocid spawning from habitat suitability information for two species that have similar but not identical spawning habitat and behavior."	ABSTRACT: "Riparian areas support a set of river functions and of ecosystem services (ESs). Their role is essential in reducing negative human impacts on river functionality. These aspects could be contained in the River Basin Management Plan, which is the tool for managing and planning freshwater ecosystems in a river basin. In this paper, a new index was developed, namely the River Ecosystem Quality Index (REQI). It is composed of five ecological indices, which assess the quality of riparian areas, and it was first applied to the Marecchia river (central Italy). The REQI was also compared with the Italian River Functionality Index (IFF) and the ESs measured as the capacity of land cover in providing human benefits. Data have shown a decrease in the quality of riparian areas, from the upper to lower part of river, with 53% of all subareas showing medium-quality values…" AUTHOR'S DESCRIPTION: "The evaluation of the quality of the riparian areas is based on the analysis of two fundamental elements of riparian areas: vegetation (characteristics and distribution) and wild birds, measured with standardized methodology and used as indicators of environmental quality and changes...To represent the REQI, each of the five indicators was initially scored with its own range (Figure 3(a)—(e)). Then, all results were redistributed in ranges from 1 to 5, where 5 is the best condition of all indices. Redistributed results were finally summed."
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	Land management, ecosystem management, response to EU 2020 Biodiversity Strategy	None identified	Federal delisting of an area of concern (AOC)	None identified
Biophysical Context	Northern Spain; Bizkaia region	submerged aquatic vegetation	No additional description provided	No additional description provided
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	The effect of habitat restoration on esocid spawning area was simulated by varying biophysical changes.	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-193	EM-414	EM-415	EM-657
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	New or revised model	New or revised model	New or revised model

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

EM ID em.detail.idHelp ?	EM-193	EM-414	EM-415	EM-657
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	None	None	None	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	None	None	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-193	EM-414	EM-415	EM-657
EM Temporal Extent em.detail.tempExtentHelp ?	2000 - 2007	2010 - 2012	2013	1996-2003 ? Comment: All the ecological analyses are based on the production of a 1:10,000 scale map of land cover with detailed classes for the vegetation obtained by overlapping the photogrammetric analysis (AIMA flight 1996) and the 2003 land-use map.
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-193	EM-414	EM-415	EM-657
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or ecological	Watershed/Catchment/HUC	Watershed/Catchment/HUC
Spatial Extent Name em.detail.extentNameHelp ?	Bilbao Metropolitan Greenbelt	St. Louis River Estuary	St. Louis River estuary	Marecchia river catchment
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	100-1000 km^2	10-100 km^2	10-100 km^2	100-1000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-193	EM-414	EM-415	EM-657
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases)	spatially distributed (in at least some cases) ? Comment: BH: Each individual transect?s data was parceled into location reports, and that each report?s ?quadrat? area was dependent upon the angle of the hydroacoustic sampling beam. The spatial grain is 0.07 m^2, 0.20 m^2 and 0.70 m^2 for depths of 1 meter, 2 meters and 3 meters, respectively.	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	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	2 m x 2 m	0.07 m^2 to 0.70 m^2	10 m x 10 m	500 m x 1000 m

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-193	EM-414	EM-415	EM-657
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-193	EM-414	EM-415	EM-657
Model Calibration Reported? em.detail.calibrationHelp ?	No	Yes	No	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Yes	No	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	p-value \| <0.001 \| unitless Nagelkerke r2 \| 0.30-0.62 \| unitless	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	Yes	No	Yes ? Comment: R2 values of the analysis between the REQI, the capacity of land cover to provide ESs, and the Italian River Functionality Quality Index ? IFF.
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	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-193	EM-414	EM-415	EM-657
Europe Spain	North America United States Minnesota Wisconsin	North America United States Minnesota Wisconsin	Europe Italy

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

EM-193	EM-414	EM-415	EM-657
None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-193	EM-414	EM-415	EM-657
Centroid Latitude em.detail.ddLatHelp ?	43.25	46.72	46.74	43.89
Centroid Longitude em.detail.ddLongHelp ?	-2.92	-96.13	-92.14	12.3
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Estimated	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-193	EM-414	EM-415	EM-657
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified) \| Forests \| Agroecosystems \| Created Greenspace \| Grasslands \| Scrubland/Shrubland	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Rivers and Streams \| Inland Wetlands \| Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	none	Freshwater estuarine system	freshwater estuary	Riparian zone along major river
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is finer than that of the Environmental Sub-class	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

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-193	EM-414	EM-415	EM-657
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Not applicable	Species ? Comment: Bird species for faunistic index of conservation.

Taxonomic level and name of organisms or groups identified

EM-193	EM-414	EM-415	EM-657
None Available	None Available	Family Esocids	None Available

EnviroAtlas URL

EM-193	EM-414	EM-415	EM-657
Percent IUCN Status II, Percent GAP Status 1 & 2	Average Annual Precipitation	None Available	Ecosystem Markets: Imperiled Species and Habitats

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-193	EM-414	EM-415	EM-657
[Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Physical use of land-/seascapes in different environmental settings Experiential use of plants, animals and land-/seascapes in different environmental settings Intellectual and representative interactions Aesthetic	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Water conditions Chemical condition of freshwaters

<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-193	EM-414	EM-415	EM-657
Near Coastal Marine/Estuarine (113) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scapes: views, sounds and scents of land, sea, sky or a combination Site Characteristic Indicator Forests (21) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scapes: views, sounds and scents of land, sea, sky or a combination Site Characteristic Indicator Grasslands (23) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scapes: views, sounds and scents of land, sea, sky or a combination Site Characteristic Indicator Scrubland/Shrubland (24) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scapes: views, sounds and scents of land, sea, sky or a combination Site Characteristic Indicator	None	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Flow Indicator Rivers and Streams (111) Fauna (4) Fish Flow Indicator Lakes and Ponds (112) Fauna (4) Fish Flow Indicator Wetlands (12) Fauna (4) Fish Flow Indicator	None

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

Comment:

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])

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

Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

Comment:

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)