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-126
EM-185
EM-434
EM-456

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-126	EM-185	EM-434	EM-456
EM Short Name em.detail.shortNameHelp ?	Annual profit from agriculture, South Australia	Blue crabs and SAV, Chesapeake Bay, USA	Land capability classification	Reef dive site favorability, St. Croix, USVI
EM Full Name em.detail.fullNameHelp ?	Annual profit from agriculture, South Australia	Blue crabs and submerged aquatic vegetation interaction, Chesapeake Bay, USA	Land capability classification	Dive site favorability (reef), St. Croix, USVI
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	None	None	None	US EPA
EM Source Document ID	243	292 ? Comment: Conference paper	340	335
Document Author em.detail.documentAuthorHelp ?	Crossman, N. D., Bryan, B. A., and Summers, D. M.	Mykoniatis, N. and Ready, R.	United States Department of Agriculture - Natural Resources Conservation Service	Yee, S. H., Dittmar, J. A., and L. M. Oliver
Document Year em.detail.documentYearHelp ?	2011	2013	2013	2014
Document Title em.detail.sourceIdHelp ?	Carbon payments and low-cost conservation	Evaluating habitat-fishery interactions: The case of submerged aquatic vegetation and blue crab fishery in the Chesapeake Bay	National Soil Survey Handbook - Part 622 - Interpretative Groups	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Not formally documented	Peer reviewed and published	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Conference proceedings	Published report	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-126	EM-185	EM-434	EM-456
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Neville D. Crossman	Nikolaos Mykoniatis	United States Department of Agriculture	Susan H. Yee
Contact Address	CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia	Department of Agricultural Economics, Sociology and Education The Pennsylvania State University	Not reported	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA
Contact Email	neville.crossman@csiro.au	Not reported	http://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/contactus/	yee.susan@epa.gov

EM Description

EM ID em.detail.idHelp ?	EM-126	EM-185	EM-434	EM-456
Summary Description em.detail.summaryDescriptionHelp ?	ABSTRACT: "A price on carbon is expected to generate demand for carbon offset schemes. This demand could drive investment in tree-based monocultures that provide higher carbon yields than diverse plantings of native tree and shrub species, which sequester less carbon but provide greater variation in vegetation structure and composition. Economic instruments such as species conservation banking, the creation and trading of credits that represent biological-diversity values on private land, could close the financial gap between monocultures and more diverse plantings by providing payments to individuals who plant diverse species in locations that contribute to conservation and restoration goals. We studied a highly modified agricultural system in southern Australia that is typical of many temperate agriculture zones globally (i.e., has a high proportion of endangered species, high levels of habitat fragmentation, and presence of non-native species). We quantified the economic returns from agriculture and from carbon plantings." AUTHOR'S DESCRIPTION: "The economic returns of carbon plantings are highly variable and depend primarily on carbon yield and price and opportunity costs (Newell & Stavins 2000; Richards & Stokes 2004; Torres et al. 2010). In this context, opportunity cost is usually expressed as the profit from agricultural production…We based our calculations of agricultural profit on Bryan et al. (2009), who calculated profit at full equity (i.e., economic return to land, capital, and management, exclusive of financial debt). We calculated an annual profit at full equity (PFEc) layer for each commodity (c) in the set of agricultural commodities (C), where C is wheat, field peas, beef cattle, or sheep."	ABSTRACT: "This paper investigates habitat-fisheries interaction between two important resources in the Chesapeake Bay: blue crabs and Submerged Aquatic Vegetation (SAV). A habitat can be essential to a species (the species is driven to extinction without it), facultative (more habitat means more of the species, but species can exist at some level without any of the habitat) or irrelevant (more habitat is not associated with more of the species). An empirical bioeconomic model that nests the essential-habitat model into its facultative-habitat counterpart is estimated. Two alternative approaches are used to test whether SAV matters for the crab stock. Our results indicate that, if we do not have perfect information on habitat-fisheries linkages, the right approach would be to run the more general facultative-habitat model instead of the essential- habitat one."	AUTHOR'S DESCRIPTION: "Definition. Land capability classification is a system of grouping soils primarily on the basis of their capability to produce common cultivated crops and pasture plants without deteriorating over a long period of time." "Class I (1) soils have slight limitations that restrict their use. Class II (2) soils have moderate limitations that reduce the choice of plants or require moderate conservation practices. Class III (3) soils have severe limitations that reduce the choice of plants or require special conservation practices, or both. Class IV (4) soils have very severe limitations that restrict the choice of plants or require very careful management, or both. Class V (5) soils have little or no hazard of erosion but have other limitations, impractical to remove, that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VI (6) soils have severe limitations that make them generally unsuited to cultivation and that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VII (7) soils have very severe limitations that make them unsuited to cultivation and that restrict their use mainly to rangeland, forestland, or wildlife habitat. Class VIII (8) soils and miscellaneous areas have limitations that preclude their use for commercial plant production and limit their use mainly to recreation, wildlife habitat, water supply, or esthetic purposes." [More information can be found at: http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_054226#ex2]	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...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…In lieu of surveys of diver opinion, recreational opportunities can also be estimated by actual field data of coral condition at preferred dive sites. A few studies have directly examined links between coral condition and production of recreational opportunities through field monitoring in an attempt to validate perceptions of recreational quality (Pendleton, 1994; Williams and Polunin, 2002; Leeworthy et al., 2004; Leujakand Ormond, 2007; Uyarraetal., 2009). Uyarraetal. (2009) used surveys to determine reef attributes related to diver perceptions of most and least favorite dive sites. Field data was used to narrow down the suite of potential preferred attributes to those that reflected actual site condition. We combined these attributes to form an index of dive site favorability: Dive site favorability = ΣipiRi where pi is the proportion of respondents indicating each attribute i that affected dive enjoyment positively. Ri is the mean relative magnitude of measured variables used to quantify each descriptive attribute i, including ‘fish abundance’ (pi=0.803), quantified by number of fish schools and fish species richness, and
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	Not applicable	None provided	None identified
Biophysical Context	Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm.	Submerged Aquatic Vegetation (SAV), eelgrass	No additional description provided	No additional description provided
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	Essential or Facultative habitat	No scenarios presented	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-126	EM-185	EM-434	EM-456
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method Only	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	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-126	EM-185	EM-434	EM-456
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-244	Doc-227	None	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-106	None	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-126	EM-185	EM-434	EM-456
EM Temporal Extent em.detail.tempExtentHelp ?	2002-2008	1993-2011	Not applicable	2006-2007, 2010
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-dependent	Not applicable	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	past time	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	discrete	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	1	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Year	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-126	EM-185	EM-434	EM-456
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Physiographic or ecological	Not applicable	Physiographic or ecological
Spatial Extent Name em.detail.extentNameHelp ?	Agricultural districts of the state of South Australia	Chesapeake Bay	Not applicable	Coastal zone surrounding St. Croix
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	100,000-1,000,000 km^2	10,000-100,000 km^2	Not applicable	100-1000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-126	EM-185	EM-434	EM-456
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases)	spatially lumped (in all cases)	Not applicable	spatially distributed (in at least some cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	Not applicable	Not applicable	area, for pixel or radial feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	1 ha	Not applicable	Not applicable	10 m x 10 m

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-126	EM-185	EM-434	EM-456
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Not applicable	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	None	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-126	EM-185	EM-434	EM-456
Model Calibration Reported? em.detail.calibrationHelp ?	No	Yes	Not applicable	Yes
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Yes	Not applicable	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	Yes	No	Yes
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	Yes	Not applicable	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	Yes	Not applicable	No
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Yes	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-126	EM-185	EM-434	EM-456
Oceania Australia	North America United States Maryland Virgina	None	None

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

EM-126	EM-185	EM-434	EM-456
None	Temperate North Atlantic Northwest Atlantic Cold Temperate Northwest Atlantic Virginian	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-126	EM-185	EM-434	EM-456
Centroid Latitude em.detail.ddLatHelp ?	-34.9	36.99	Not applicable	17.73
Centroid Longitude em.detail.ddLongHelp ?	138.7	-75.95	Not applicable	-64.77
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	Not applicable	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Not applicable	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-126	EM-185	EM-434	EM-456
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems	None	Terrestrial Environment (sub-classes not fully specified)	Near Coastal Marine and Estuarine
Specific Environment Type em.detail.specificEnvTypeHelp ?	Agricultural land for annual crops, annual legumes, and grazing of sheep and cows	Yes	None identified	Coral reefs
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is finer than that of the Environmental Sub-class	Yes	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-126	EM-185	EM-434	EM-456
EM Organismal Scale em.detail.orgScaleHelp ?	Guild or Assemblage	Yes	Not applicable	Guild or Assemblage

Taxonomic level and name of organisms or groups identified

EM-126	EM-185	EM-434	EM-456
Genus Triticum Species Ovis aries Bos primigenius Pisum sativum	None Available	None Available	None Available

EnviroAtlas URL

EM-126	EM-185	EM-434	EM-456
GAP Ecological Systems	None Available	Average Annual Precipitation	None Available

EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system

CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)

EM-126	EM-185	EM-434	EM-456
[Ecosystem] Provisioning Nutrition Biomass Reared animals and their outputs Cultivated crops	None	[Ecosystem] Provisioning Nutrition Biomass Reared animals and their outputs Cultivated crops	[Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Experiential use of plants, animals and land-/seascapes in different environmental settings

<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-126	EM-185	EM-434	EM-456
None	None	Agroecosystems (22) Soil (2) Specific types of soil Quality Indicator	Near Coastal Marine/Estuarine (113) Composite (8) Presence of environmental class/subclass Quality Indicator