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.

Compare Criteria:

Show Criteria

Hide Criteria

Comparing:

EM-86
EM-459
EM-651
EM-1005

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-86	EM-459	EM-651	EM-1005
EM Short Name em.detail.shortNameHelp ?	Area and hotspots of soil retention, South Africa	Reef density of S. gigas, St. Croix, USVI	Dickcissel density, CREP, Iowa, USA	ComunityViz - land-sea planning submodel
EM Full Name em.detail.fullNameHelp ?	Area and hotspots of soil retention, South Africa	Relative density of Strombus gigas (on reef), St. Croix, USVI	Dickcissel population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	A technical guide to the integrated land-sea planning toolkit
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	None	US EPA	None	None
EM Source Document ID	271	335	372	473
Document Author em.detail.documentAuthorHelp ?	Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S.	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever	Crist, P., Madden, K., Varley, I., Eslinger, D., Walker, D., Anderson, A., Morehead, S. and Dunton, K.,
Document Year em.detail.documentYearHelp ?	2008	2014	2010	2009
Document Title em.detail.sourceIdHelp ?	Mapping ecosystem services for planning and management	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt	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
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published report	Published report

Software and Access

EM ID em.detail.idHelp ?	EM-86	EM-459	EM-651	EM-1005
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	https://repositories.lib.utexas.edu/bitstreams/3dee92a8-9373-4bcc-be25-eda74e81fabf/download
Contact Name em.detail.contactNameHelp ?	Benis Egoh	Susan H. Yee	David Otis	Patrick Crist ? Comment: No contact information provided
Contact Address	Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University	None provided
Contact Email	Not reported	yee.susan@epa.gov	dotis@iastate.edu	patrick@planitfwd.com

EM Description

EM ID em.detail.idHelp ?	EM-86	EM-459	EM-651	EM-1005
Summary Description em.detail.summaryDescriptionHelp ?	AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil retention was modelled as a function of vegetation or litter cover and soil erosion potential. Schoeman et al. (2002) modelled soil erosion potential and derived eight erosion classes, ranging from low to severe erosion potential for South Africa. The vegetation cover was mapped by ranking vegetation types using expert knowledge of their ability to curb erosion. We used Schulze (2004) index of litter cover which estimates the soil surface covered by litter based on observations in a range of grasslands, woodlands and natural forests. According to Quinton et al. (1997) and Fowler and Rockstrom (2001) soil erosion is slightly reduced with about 30%, significantly reduced with about 70% vegetation cover. The range of soil retention was mapped by selecting all areas that had vegetation or litter cover of more than 30% for both the expert classified vegetation types and litter accumulation index within areas with moderate to severe erosion potential. The hotspot was mapped as areas with severe erosion potential and vegetation/litter cover of at least 70% where maintaining the cover is essential to prevent erosion. An assumption was made that the potential for this service is relatively low in areas with little natural vegetation or litter cover."	ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(2) density of the queen conch Strombus gigas"	ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Dickcissel (Spiza americana)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: DICK density = 1-1/1+e^(-6.811334 + 1.889878 * bbspath) * e^(-1.831015 + 0.0312571 * hay400)	CommunityViz® is an advanced yet easy-to-use GIS software extension that is designed to help people visualize, analyze, and communicate about important planning decisions. Widely adopted by land-use planners, it supports informed, collaborative decision-making by illustrating and analyzing alternative planning scenarios. It features flexible and interactive analysis tools, a rich set of presentation tools, and several options for 3D visualization of future places. In the land-sea toolkit, CommunityViz (sometimes referred to as “Cviz”) serves as the platform for creating land use scenarios. It models how urban growth could occur over time as the result of present-day decisions regarding land use and regulation. The resulting future growth conditions are passed to NatureServe Vista (Vista) and NSPECT for impact assessment, and those results can be returned to CommunityViz for display and for guidance in development of revisions to planning scenarios. Throughout the integration process, CommunityViz provides the ability to assess a variety of socio-economic indicators attached to the land-use scenarios.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	None identified	None provided
Biophysical Context	Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential.	No additional description provided	Prairie pothole region of north-central Iowa	Not applicable
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-86	EM-459	EM-651	EM-1005
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method Only
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	Application of existing model	Application of existing model ? Comment: Models developed by Quamen (2007).	New or revised model

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

EM ID em.detail.idHelp ?	EM-86	EM-459	EM-651	EM-1005
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-271 ? Comment: Document 273 used for source information on soil erosion potential variable	None	Doc-372	Doc-473
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-85 \| EM-87 \| EM-88	None	EM-652 \| EM-650 \| EM-649 \| EM-648	EM-1003 \| EM-1006 \| EM-1007 \| EM-1008

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-86	EM-459	EM-651	EM-1005
EM Temporal Extent em.detail.tempExtentHelp ?	Not reported	2006-2007, 2010	1992-2007	Not applicable
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	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	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
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-86	EM-459	EM-651	EM-1005
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or ecological	Multiple unrelated locations (e.g., meta-analysis)	Not applicable
Spatial Extent Name em.detail.extentNameHelp ?	South Africa	Coastal zone surrounding St. Croix	CREP (Conservation Reserve Enhancement Program) wetland sites	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	100-1000 km^2	1-10 km^2	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-86	EM-459	EM-651	EM-1005
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)	other or unclear (comment)
Spatial Grain Type em.detail.spGrainTypeHelp ?	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	Distributed across catchments with average size of 65,000 ha	10 m x 10 m	multiple, individual, irregular shaped sites	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-86	EM-459	EM-651	EM-1005
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-86	EM-459	EM-651	EM-1005
Model Calibration Reported? em.detail.calibrationHelp ?	No	Yes	Unclear	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	No	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	Yes	Unclear	Not applicable
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-86	EM-459	EM-651	EM-1005
Africa South Africa	None	North America United States Iowa	None

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

EM-86	EM-459	EM-651	EM-1005
None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-86	EM-459	EM-651	EM-1005
Centroid Latitude em.detail.ddLatHelp ?	-30	17.73	42.62	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	25	-64.77	-93.84	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-86	EM-459	EM-651	EM-1005
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Terrestrial Environment (sub-classes not fully specified)	Near Coastal Marine and Estuarine	Inland Wetlands \| Agroecosystems \| Grasslands	Not applicable
Specific Environment Type em.detail.specificEnvTypeHelp ?	Not reported	Coral reefs	Grassland buffering inland wetlands set in agricultural land	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

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-86	EM-459	EM-651	EM-1005
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Species	Species	Community

Taxonomic level and name of organisms or groups identified

EM-86	EM-459	EM-651	EM-1005
None Available	Species Panulirus argus	Species Dickcissel (Spiza americana)	None Available

EnviroAtlas URL

EM-86	EM-459	EM-651	EM-1005
None Available	None Available	GAP Ecological Systems	Dasymetric Allocation of Population

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-86	EM-459	EM-651	EM-1005
[Ecosystem] Regulation & Maintenance Mediation of flows Mass flows Mass stabilisation and control of erosion rates	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[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-86	EM-459	EM-651	EM-1005
None	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator	Grasslands (23) Fauna (4) Birds Stock 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:

Comment:

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

Comment:

EM Modeling Approach

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)