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-656
EM-735
EM-964
EM-998

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-195	EM-260	EM-656	EM-735	EM-964	EM-998
EM Short Name em.detail.shortNameHelp ?	C Sequestration and De-N, Tampa Bay, FL, USA	Coral taxa and land development, St.Croix, VI, USA	P8 UCM	C sequestration in grassland restoration, England	EcoSim II - method	CAESAR landscape evolution model
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	P8 Urban Catchment model method	Carbon sequestration in grassland diversity restoration, England	EcoSim II - method	Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	US EPA	US EPA	None	None	None	None
EM Source Document ID	186	96	377 ? Comment: Published to the web. Previously versions prepared for EPA.	396	448	468
Document Author em.detail.documentAuthorHelp ?	Russell, M. and Greening, H.	Oliver, L. M., Lehrter, J. C. and Fisher, W. S.	Walker, W. Jr., and J.D. Walker	De Deyn, G. B., R. S. Shiel, N. J. Ostle, N. P. McNamara, S. Oakley, I. Young, C. Freeman, N. Fenner, H. Quirk, and R. D. Bardgett	Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell	Van De Wiel, M. J., Coulthard, T. J., Macklin, M. G., & Lewin, J.
Document Year em.detail.documentYearHelp ?	2013	2011	2015	2011	2000	2007
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	P8 Urban Catchment Model Version 3.5	Additional carbon sequestration benefits of grassland diversity restoration	Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II	Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Peer reviewed and published	Not peer reviewed but is published (explain in Comment)	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 journal manuscript	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-195	EM-260	EM-656	EM-735	EM-964	EM-998
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	http://www.wwwalker.net/p8/v35/webhelp/splash.htm	Not applicable	https://ecopath.org/downloads/	http://www.coulthard.org.uk/
Contact Name em.detail.contactNameHelp ?	M. Russell	Leah Oliver	William Walker Jr., PhD	Gerlinde B. De Deyn	Carl Walters	Marco J. Van De Wiel
Contact Address	US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA	National Health and Environmental Research Effects Laboratory	Concord, Massachusetts	Dept. of Terrestrial Ecology, Netherlands Institute of Ecology, P O Box 40, 6666 ZG Heteren, The Netherlands	Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4	Department of Geography, University of Western Ontario, London, Ontario, Canada
Contact Email	Russell.Marc@epamail.epa.gov	leah.oliver@epa.gov	bill@wwwalker.net	g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com	c.walters@oceans.ubc.ca	mvandew3@uwo.ca

EM Description

EM ID em.detail.idHelp ?	EM-195	EM-260	EM-656	EM-735	EM-964	EM-998
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)	Author description: " P8 simulates the generation and transport of stormwater runoff pollutants in urban watersheds. Continuous water-balance and mass-balance calculations are performed on a user-defined drainage system consisting of the following elements: - Watersheds (<= 250 nonpoint source areas) - Devices (<=75 runoff storage/treatment areas or BMP's) - Particles (<= 5 fractions with different settling velocities) - Water Quality Components (<= 10 associated with particles) Simulations are driven by hourly precipitation and daily air temperature time series. Runoff contributions from snowmelt are also simulated. 'P8' abbreviates "Program for Predicting Polluting Particle Passage Thru Pits, Puddles, and Ponds", which more or less captures the basic features and functions of the model. It has been developed for use by engineers and planners in designing and evaluating runoff treatment schemes for existing or proposed urban developments. Design objectives are typically expressed in terms of percentage reduction in suspended solids or other water quality component. Despite its limitations, P8 has been used by state and local regulatory agencies as a consistent framework for evaluating proposed developments. Depending on applications, other models could be either too simple (easily used, but ignoring important factors) or too complex (requiring considerable site-specific data and/or user expertise). P8 attempts to strike a balance to between those extremes. Predicted water quality components include total suspended solids (sum of the individual particle fractions), total phosphorus, total Kjeldahl nitrogen, copper, lead, zinc, and total hydrocarbons. Simulated BMP types include detention ponds (wet, dry, extended), infiltration basins, swales, buffer strips, or other devices with user-specified stage/discharge curves and infiltration rates. A simple water budget algorithm can be used to estimate groundwater storage and stream base flow in watershed-scale applications. Initial calibrations were based upon runoff quality and particle settling velocity data collected under the EPA's Nationwide Urban Runoff Program (Athayede et al., 1983). Calibrations to impervious area runoff parameters for Wisconsin watersheds have been subsequently developed. Inputs are structured in terms which should be familiar to planners and engineers involved in hydrologic evaluation. Several tabular and graphic output formats are provided. "	ABSTRACT: "A major aim of European agri-environment policy is the management of grassland for botanical diversity conservation and restoration, together with the delivery of ecosystem services including soil carbon (C) sequestration. To test whether management for biodiversity restoration has additional benefits for soil C sequestration, we investigated C and nitrogen (N) accumulation rates in soil and C and N pools in vegetation in a long-term field experiment (16 years) in which fertilizer application and plant seeding were manipulated. In addition, the abundance of the legume Trifolium pratense was manipulated for the last 2 years. To unravel the mechanisms underlying changes in soil C and N pools, we also tested for effects of diversity restoration management on soil structure, ecosystem respiration and soil enzyme activities…" AUTHOR'S DESCRIPTION: "Measurements were made on 36 plots of 3 x 3 m comprising two management treatments (and their controls) in a long-term multifactorial grassland restoration experiment which have successfully increased plant species diversity, namely the cessation of NPK fertilizer application and the addition of seed mixtures…"	ABSTRACT: " EcoSim II uses results from the Ecopath procedure for trophic mass-balance analysis to define biomass dynamics models for predicting temporal change in exploited ecosystems. Key populations can be repre- sented in further detail by using delay-difference models to account for both biomass and numbers dynamics. A major problem revealed by linking the population and biomass dynamics models is in representation of population responses to changes in food supply; simple proportional growth and reproductive responses lead to unrealistic predic- tions of changes in mean body size with changes in fishing mortality. EcoSim II allows users to specify life history mechanisms to avoid such unrealistic predictions: animals may translate changes in feed- ing rate into changes in reproductive rather than growth rates, or they may translate changes in food availability into changes in foraging time that in turn affects predation risk. These options, along with model relationships for limits on prey availabil- ity caused by predation avoidance tactics, tend to cause strong compensatory responses in modeled populations. It is likely that such compensatory responses are responsible for our inability to find obvious correlations between interacting trophic components in fisheries time-series data. But Eco- sim II does not just predict strong compensatory responses: it also suggests that large piscivores may be vulnerable to delayed recruitment collapses caused by increases in prey species that are in turn competitors/predators of juvenile piscivores "	We introduce a new computational model designed to simulate and investigate reach-scale alluvial dynamics within a landscape evolution model. The model is based on the cellular automaton concept, whereby the continued iteration of a series of local process ‘rules’ governs the behaviour of the entire system. The model is a modified version of the CAESAR landscape evolution model, which applies a suite of physically based rules to simulate the entrainment, transport and deposition of sediments. The CAESAR model has been altered to improve the representation of hydraulic and geomorphic processes in an alluvial environment. In-channel and overbank flow, sediment entrainment and deposition, suspended load and bed load transport, lateral erosion and bank failure have all been represented as local cellular automaton rules. Although these rules are relatively simple and straightforward, their combined and repeatedly iterated effect is such that complex, non-linear geomorphological response can be simulated within the model. Examples of such larger-scale, emergent responses include channel incision and aggradation, terrace formation, channel migration and river meandering, formation of meander cutoffs, and transitions between braided and single-thread channel patterns. In the current study, the model is illustrated on a reach of the River Teifi, near Lampeter, Wales, UK.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	Restoration of seagrass	Not applicable	None identified	None identified	None	None identified
Biophysical Context	Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove	nearshore; <1.5 km offshore; <12 m depth	Urban setting	Lolium perenne-Cynosorus cristatus grassland; The soil is a shallow brown-earth (average depth 28 cm) over limestone of moderate-high residual fertility.	None, Ocean ecosystems	River Teifi, Lampeter, Wales
EM Scenario Drivers em.detail.scenarioDriverHelp ?	Habitat loss or restoration in Tampa Bay Estuary	Not applicable	N/A	Additional benefits due to biodiversity restoration practices	N/A	Varying flow velocities and durations

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-195	EM-260	EM-656	EM-735	EM-964	EM-998
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method Only	Method + Application (multiple runs exist) View EM Runs	Method Only	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	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-656	EM-735	EM-964	EM-998
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	None	None	None	None	None	Doc-467
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	None	None	None	EM-1055	EM-997

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-195	EM-260	EM-656	EM-735	EM-964	EM-998
EM Temporal Extent em.detail.tempExtentHelp ?	1982-2010	2006-2007	Not applicable	1990-2007	Not applicable	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-dependent	time-stationary	time-dependent	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	both	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	discrete	Not applicable	discrete ? Comment: Modeller dependent	continuous
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	1	Not applicable	1	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Hour	Not applicable	Day	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-195	EM-260	EM-656	EM-735	EM-964	EM-998
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Physiographic or Ecological	Not applicable	Other	Other	Watershed/Catchment/HUC
Spatial Extent Name em.detail.extentNameHelp ?	Tampa Bay Estuary	St.Croix, U.S. Virgin Islands	Not applicable	Colt Park meadows, Ingleborough National Nature Reserve, northern England	Not applicable	River Teifi
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	1000-10,000 km^2.	10-100 km^2	Not applicable	<1 ha	Not applicable	1000-10,000 km^2.

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-195	EM-260	EM-656	EM-735	EM-964	EM-998
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially lumped (in all cases)	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially lumped (in all cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	Not applicable	Not applicable	area, for pixel or radial feature	Not applicable	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	1 ha	Not applicable	Not applicable	3 m x 3 m	Not applicable	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-195	EM-260	EM-656	EM-735	EM-964	EM-998
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Numeric	Analytic	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	stochastic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-195	EM-260	EM-656	EM-735	EM-964	EM-998
Model Calibration Reported? em.detail.calibrationHelp ?	Yes	Yes	Yes	Not applicable	No	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Yes	Not applicable	Not applicable	No	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	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	Not applicable	No	Not applicable	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	Yes	Not applicable	No	Not applicable	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	Not applicable	No	Not applicable	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	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-195	EM-260	EM-656	EM-735	EM-964	EM-998
None	None	None	Europe United Kingdom	None	Europe United Kingdom

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

EM-195	EM-260	EM-656	EM-735	EM-964	EM-998
Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Floridian	Tropical Atlantic West Tropical Atlantic Tropical Southwestern Atlantic	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-195	EM-260	EM-656	EM-735	EM-964	EM-998
Centroid Latitude em.detail.ddLatHelp ?	27.95	17.75	Not applicable	54.2	Not applicable	52.04
Centroid Longitude em.detail.ddLongHelp ?	-82.47	-64.75	Not applicable	-2.35	Not applicable	-4.39
Centroid Datum em.detail.datumHelp ?	WGS84	NAD83	Not applicable	WGS84	Not applicable	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Not applicable	Provided	Not applicable	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-195	EM-260	EM-656	EM-735	EM-964	EM-998
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine	Terrestrial Environment (sub-classes not fully specified)	Agroecosystems \| Grasslands	Open Ocean and Seas	Rivers and Streams
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subtropical Estuary	stony coral reef	Urban catchments	fertilized grassland (historically hayed)	Pelagic	River
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 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-656	EM-735	EM-964	EM-998
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Guild or Assemblage	Not applicable	Community	Other (Comment) ? Comment: Varied levels of taxonomic order	Not applicable

Taxonomic level and name of organisms or groups identified

EM-195	EM-260	EM-656	EM-735	EM-964	EM-998
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	None Available	None Available	other Whiting shrimp Whales Seals Cod Phytoplankton Mackerel Benthos Zooplankton Anchovy	None Available

EnviroAtlas URL

EM-195	EM-260	EM-656	EM-735	EM-964	EM-998
Carbon Storage by Tree Biomass	None Available	Average Annual Precipitation, Total Annual Reduced Nitrogen Deposition, The Watershed Boundary Dataset (WBD), Percent Impervious Area, Water supply from NID reservoirs (million gallons)	None Available	Big game hunting recreation demand	Average Annual Precipitation

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-656	EM-735	EM-964	EM-998
[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 Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	[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 Mediation of flows Liquid flows Hydrological cycle and water flow maintenance Mass flows Mass stabilisation and control of erosion rates

<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-656	EM-735	EM-964	EM-998
Near Coastal Marine/Estuarine (113) Water (3) Liquid water Quality Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Quality Indicator	None	None	Open Oceans and Seas (114) Fauna (4) Fish Flow Indicator	Rivers and Streams (111) Water (3) Liquid water Flow Indicator Soil (2) Specific types of soil Flow Indicator

EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

EM Variables by Category

All EM Descriptors

EM Descriptors by Modeling Concepts

Comparing:

EM Identity and Description

Comment:

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

EM Modeling Approach

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)