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-195
EM-260
EM-303
EM-414
EM-1006

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-195	EM-260	EM-303	EM-414	EM-1006
EM Short Name em.detail.shortNameHelp ?	C Sequestration and De-N, Tampa Bay, FL, USA	Coral taxa and land development, St.Croix, VI, USA	Biological pest control, Uppland Province, Sweden	SAV occurrence, St. Louis River, MN/WI, USA	Vista land-sea planning submodel
EM Full Name em.detail.fullNameHelp ?	Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA	Coral taxa richness and land development, St.Croix, Virgin Islands, USA	Biological control of agricultural pests by natural predators, Uppland Province, Sweden	Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA	A technical guide to the integrated land-sea planning toolkit
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	US EPA	US EPA	None	US EPA	None
EM Source Document ID	186	96	299	330	473
Document Author em.detail.documentAuthorHelp ?	Russell, M. and Greening, H.	Oliver, L. M., Lehrter, J. C. and Fisher, W. S.	Jonsson, M., Bommarco, R., Ekbom, B., Smith, H.G., Bengtsson, J., Caballero-Lopez, B., Winqvist, C., and Olsson, O.	Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke	Crist, P., Madden, K., Varley, I., Eslinger, D., Walker, D., Anderson, A., Morehead, S. and Dunton, K.,
Document Year em.detail.documentYearHelp ?	2013	2011	2014	2013	2009
Document Title em.detail.sourceIdHelp ?	Estimating benefits in a recovering estuary: Tampa Bay, Florida	Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands	Ecological production functions for biological control services in agricultural landscapes	Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary	Integrated Land-Sea Planning: A Technical Guide to the Integrated Land-Sea Planning Toolkit.
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 report

Software and Access

EM ID em.detail.idHelp ?	EM-195	EM-260	EM-303	EM-414	EM-1006
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	https://repositories.lib.utexas.edu/bitstreams/3dee92a8-9373-4bcc-be25-eda74e81fabf/download
Contact Name em.detail.contactNameHelp ?	M. Russell	Leah Oliver	Mattias Jonsson	Ted R. Angradi	Patrick Crist ? Comment: No contact information provided
Contact Address	US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA	National Health and Environmental Research Effects Laboratory	Department of Ecology, Swedish University of Agricultural Sciences, PO Box 7044, SE-750 07 Uppsala, Sweden	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	None provided
Contact Email	Russell.Marc@epamail.epa.gov	leah.oliver@epa.gov	mattias.jonsson@slu.se	angradi.theodore@epa.gov	patrick@planitfwd.com

EM Description

EM ID em.detail.idHelp ?	EM-195	EM-260	EM-303	EM-414	EM-1006
Summary Description em.detail.summaryDescriptionHelp ?	AUTHOR'S DESCRIPTION: "...we examine the change in the production of ecosystem goods produced as a result of restoration efforts and potential relative cost savings for the Tampa Bay community from seagrass expansion (more than 3,100 ha) and coastal marsh and mangrove restoration (∼600 ha), since 1990… The objectives of this article are to explore the roles that ecological processes and resulting ecosystem goods have in maintaining healthy estuarine systems by (1) quantifying the production of specific ecosystem goods in a subtropical estuarine system and (2) determining potential cost savings of improved water quality and increased habitat in a recovering estuary." (pp. 2)	AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294)	ABSTRACT: "We develop a novel, mechanistic landscape model for biological control of cereal aphids, explicitly accounting for the influence of landscape composition on natural enemies varying in mobility, feeding rates and other life history traits. Finally, we use the model to map biological control services across cereal fields in a Swedish agricultural region with varying landscape complexity. The model predicted that biological control would reduce crop damage by 45–70% and that the biological control effect would be higher in complex landscapes. In a validation with independent data, the model performed well and predicted a significant proportion of biological control variation in cereal fields. However, much variability remains to be explained, and we propose that the model could be improved by refining the mechanistic understanding of predator dynamics and accounting for variation in aphid colonization."	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."	NatureServe Vista is a broad assessment and planning decision support tool focused on conservation of specific mapped features or “conservation elements.” It facilitates capturing spatial and non-spatial information and conservation requirements for elements, defining scenarios of land use, management, conservation, disturbance, etc., and evaluating the impacts of scenarios on the elements. Vista also contains powerful internal tools and interoperability with outside tools to facilitate mitigating site-level conflicts, offsite mitigation, and development of alternative scenarios. The primary objective (though not exclusive application) of the tool is to develop/mitigate alternative scenarios such that they meet explicit conservation goals for the elements. Vista can also support goal seeking for competing land uses while preventing development of scenarios that attempt to meet goals for conflicting things in the same place. The primary role of NatureServe Vista in this toolkit is to evaluate the impacts of land use scenarios on conservation elements in terrestrial, freshwater, and marine ecosystems. It does this through direct evaluation of land use scenarios from CommunityViz (augmented with other use, management, disturbance data) and interoperating with N-SPECT to evaluate water quality impacts on aquatic/marine elements.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	Restoration of seagrass	Not applicable	None identified	None identified	None provided
Biophysical Context	Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove	nearshore; <1.5 km offshore; <12 m depth	Spring-sown cereal croplands, where the bird chearry-oat aphid is a key aphid pest. The aphid colonizes the crop during late May and early June, depending on weather and location. The colonization phase is followed by a brief phase of rapid exponential population growth by wingless aphids, continuing until about the time of crop heading, in late June or early July. After heading, aphid populations usually decline rapidly in the crop due to decreased plant quality and migration to grasslands. The aphids are attacked by a complex of arthropod natural enemies, but parasitism is not important in the region and therefore not modelled here.	submerged aquatic vegetation	Not applicable
EM Scenario Drivers em.detail.scenarioDriverHelp ?	Habitat loss or restoration in Tampa Bay Estuary	Not applicable	No scenarios presented	No scenarios presented	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-195	EM-260	EM-303	EM-414	EM-1006
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	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	New or revised model

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

EM ID em.detail.idHelp ?	EM-195	EM-260	EM-303	EM-414	EM-1006
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	None	None	None	None	Doc-473
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	None	None	None	EM-1003 \| EM-1005 \| EM-1007 \| EM-1008

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-195	EM-260	EM-303	EM-414	EM-1006
EM Temporal Extent em.detail.tempExtentHelp ?	1982-2010	2006-2007	2009	2010 - 2012	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	other or unclear (comment)
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-195	EM-260	EM-303	EM-414	EM-1006
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Physiographic or Ecological	Geopolitical	Physiographic or ecological	Not applicable
Spatial Extent Name em.detail.extentNameHelp ?	Tampa Bay Estuary	St.Croix, U.S. Virgin Islands	Uppland province	St. Louis River Estuary	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	1000-10,000 km^2.	10-100 km^2	10,000-100,000 km^2	10-100 km^2	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-195	EM-260	EM-303	EM-414	EM-1006
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases)	spatially lumped (in all cases)	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.	other or unclear (comment)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	Not applicable	area, for pixel or radial feature	area, for pixel or radial feature	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	1 ha	Not applicable	25 m x 25 m	0.07 m^2 to 0.70 m^2	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-195	EM-260	EM-303	EM-414	EM-1006
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	Analytic
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-195	EM-260	EM-303	EM-414	EM-1006
Model Calibration Reported? em.detail.calibrationHelp ?	Yes	Yes	No	Yes	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Yes	No	Yes	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	R squared (taxa richness) \| 0.74 \| Not applicable p-value \| 0.0020 \| unitless	None	p-value \| <0.001 \| unitless Nagelkerke r2 \| 0.30-0.62 \| unitless	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	Yes	Yes	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	Yes	No	No	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	Yes ? Comment: AUTHOR'S NOTE: "Varying aphid fecundity, overall predator abundances and attack rates affected the biological control effect, but had little influence on the relative differences between landscapes with high and low levels of biological control. The model predictions were more sensitive to changing the predators' landscape relations, but, with few exceptions, did not dramatically alter the overall patterns generated by the model."	No	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	No	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-195	EM-260	EM-303	EM-414	EM-1006
None	None	Europe Sweden	North America United States Minnesota Wisconsin	None

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

EM-195	EM-260	EM-303	EM-414	EM-1006
Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Floridian	Tropical Atlantic West Tropical Atlantic Tropical Southwestern Atlantic	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-195	EM-260	EM-303	EM-414	EM-1006
Centroid Latitude em.detail.ddLatHelp ?	27.95	17.75	59.52	46.72	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	-82.47	-64.75	17.9	-96.13	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	NAD83	WGS84	WGS84	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Estimated	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-195	EM-260	EM-303	EM-414	EM-1006
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine	Agroecosystems \| Grasslands	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Not applicable
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subtropical Estuary	stony coral reef	Spring-sown cereal croplands and surrounding grassland and non-arable land	Freshwater estuarine system	None
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 ID em.detail.idHelp ?	EM-195	EM-260	EM-303	EM-414	EM-1006
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Guild or Assemblage	Individual or population, within a species	Not applicable	Community

Taxonomic level and name of organisms or groups identified

EM-195	EM-260	EM-303	EM-414	EM-1006
None Available	Genus Acropora Porites spp. Diploria spp. Species Mycetophylia lamarckiana Mycetophylia ferox Montastrea cavernosa Dichocoenia stokesii Meandrina meandrites Agaricia tenuifolia Agaricia fragilis Montastrea annularis Agaricia agaricites Dendrogyra cylindricus Millepora complanata Montastrea faveolata Porites astreoides Montastrea franksii	Genus Neuroptera: Chrysopidae Araneae: Linyphiidae Coleoptera: Carabidae Araneae:Lycosidae Coleoptera: Staphylinidae Coleoptera: Coccinellidae Diptera: Syrphidae Species Rhopalosiphum padi L.	None Available	None Available

EnviroAtlas URL

EM-195	EM-260	EM-303	EM-414	EM-1006
Carbon Storage by Tree Biomass	None Available	GAP Ecological Systems	Average Annual Precipitation	Watersheds, 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-195	EM-260	EM-303	EM-414	EM-1006
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations 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	[Non-ecosystem] Regulation & Maintenance by natural physical structures and processes Maintenance of physical, chemical, abiotic conditions By natural chemical and physical processes Not applicable	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Pest and disease control Pest control	[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 Soil formation and composition Decomposition and fixing processes Weathering processes Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Mediation of flows Liquid flows Hydrological cycle and water flow maintenance Mass flows Buffering and attenuation of mass flows Mass stabilisation and control of erosion rates Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems Dilution by atmosphere, freshwater and marine ecosystems

<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-195	EM-260	EM-303	EM-414	EM-1006
Near Coastal Marine/Estuarine (113) Water (3) Liquid water Quality Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Quality Indicator	Grasslands (23) Fauna (4) Pest predators Stock Indicator Agroecosystems (22) Fauna (4) Pest predators Stock Indicator	None	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:

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

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)