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-71
EM-195
EM-224
EM-260
EM-656
EM-964

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-71	EM-195	EM-224	EM-260	EM-656	EM-964
EM Short Name em.detail.shortNameHelp ?	Community flowering date, Central French Alps	C Sequestration and De-N, Tampa Bay, FL, USA	FORCLIM v2.9, West Cascades, OR, USA	Coral taxa and land development, St.Croix, VI, USA	P8 UCM	EcoSim II - method
EM Full Name em.detail.fullNameHelp ?	Community weighted mean flowering date, Central French Alps	Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA	FORCLIM (FORests in a changing CLIMate) v2.9, West Cascades, OR, USA	Coral taxa richness and land development, St.Croix, Virgin Islands, USA	P8 Urban Catchment model method	EcoSim II - method
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	US EPA	US EPA	US EPA	None	None
EM Source Document ID	260	186	23 ? Comment: Related document ID 22 is a secondary source providing tree species specific parameters in appendix.	96	377 ? Comment: Published to the web. Previously versions prepared for EPA.	448
Document Author em.detail.documentAuthorHelp ?	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Russell, M. and Greening, H.	Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A.	Oliver, L. M., Lehrter, J. C. and Fisher, W. S.	Walker, W. Jr., and J.D. Walker	Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell
Document Year em.detail.documentYearHelp ?	2011	2013	2007	2011	2015	2000
Document Title em.detail.sourceIdHelp ?	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Estimating benefits in a recovering estuary: Tampa Bay, Florida	Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model	Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands	P8 Urban Catchment Model Version 3.5	Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Not peer reviewed but is published (explain in Comment)	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published report	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-71	EM-195	EM-224	EM-260	EM-656	EM-964
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	http://www.wwwalker.net/p8/v35/webhelp/splash.htm	https://ecopath.org/downloads/
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	M. Russell	Richard T. Busing	Leah Oliver	William Walker Jr., PhD	Carl Walters
Contact Address	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA	U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA	National Health and Environmental Research Effects Laboratory	Concord, Massachusetts	Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4
Contact Email	sandra.lavorel@ujf-grenoble.fr	Russell.Marc@epamail.epa.gov	rtbusing@aol.com	leah.oliver@epa.gov	bill@wwwalker.net	c.walters@oceans.ubc.ca

EM Description

EM ID em.detail.idHelp ?	EM-71	EM-195	EM-224	EM-260	EM-656	EM-964
Summary Description em.detail.summaryDescriptionHelp ?	ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables."	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)	ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range…The simulation of both stand-replacing and partial-stand disturbances across western Oregon improved agreement between simulated and actual data." AUTHOR'S DESCRIPTION: "An analysis of forest successional dynamics was performed on ecoregions 4a and 4b, which cover the south Santiam watershed area selected for intensive study. In each of these two ecoregions, a set of 20 simulated sites was compared to survey plot data summaries. Survey data were analysed by stand age class and simulations of corresponding ages. The statistical methods described…were applied in comparison of actual with simulated forest composition and total basal area by age class. Separate simulations were run with and without fire."	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: " 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 "
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	Restoration of seagrass	None Identified	Not applicable	None identified	None
Biophysical Context	Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes	Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove	West Cascade lowlands (4a), and west Cascade montane (4b) ecoregions	nearshore; <1.5 km offshore; <12 m depth	Urban setting	None, Ocean ecosystems
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	Habitat loss or restoration in Tampa Bay Estuary	Two scenarios modelled, forests with and without fire	Not applicable	N/A	N/A

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-71	EM-195	EM-224	EM-260	EM-656	EM-964
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs ? Comment: Related document ID 22 is a secondary source providing tree species specific parameters in appendix.	Method + Application	Method Only	Method Only
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	New or revised model	Application of existing 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-71	EM-195	EM-224	EM-260	EM-656	EM-964
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260 \| Doc-269	None	Doc-22 \| Doc-23	None	None	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-65 \| EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-79 \| EM-80 \| EM-81 \| EM-82 \| EM-83	None	EM-146 \| EM-208 \| EM-186	None	None	EM-1055

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-71	EM-195	EM-224	EM-260	EM-656	EM-964
EM Temporal Extent em.detail.tempExtentHelp ?	2007-2008	1982-2010	>650 yrs	2006-2007	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	past time	Not applicable	Not applicable	both
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	discrete	Not applicable	discrete	discrete ? Comment: Modeller dependent
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	1	Not applicable	1	1
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Year	Not applicable	Hour	Day

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-71	EM-195	EM-224	EM-260	EM-656	EM-964
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Physiographic or Ecological	Physiographic or ecological	Physiographic or Ecological	Not applicable	Other
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	Tampa Bay Estuary	West Cascades, Oregon	St.Croix, U.S. Virgin Islands	Not applicable	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	1000-10,000 km^2.	100-1000 km^2	10-100 km^2	Not applicable	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-71	EM-195	EM-224	EM-260	EM-656	EM-964
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)	spatially lumped (in all cases)	spatially lumped (in all cases)	spatially lumped (in all cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	Not applicable	Not applicable	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	1 ha	0.08 ha	Not applicable	Not applicable	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-71	EM-195	EM-224	EM-260	EM-656	EM-964
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Numeric	Analytic	Numeric	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	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-71	EM-195	EM-224	EM-260	EM-656	EM-964
Model Calibration Reported? em.detail.calibrationHelp ?	No	Yes	No	Yes	Yes	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	No	No	Yes	Not applicable	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	R squared \| 45.2 \| Not applicable	None	None	R squared (taxa richness) \| 0.74 \| Not applicable p-value \| 0.0020 \| unitless	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	Yes	No	Not applicable	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	Yes	Not applicable	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	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-71	EM-195	EM-224	EM-260	EM-656	EM-964
Europe France	None	North America United States Oregon	None	None	None

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

EM-71	EM-195	EM-224	EM-260	EM-656	EM-964
None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Floridian	None	Tropical Atlantic West Tropical Atlantic Tropical Southwestern Atlantic	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-71	EM-195	EM-224	EM-260	EM-656	EM-964
Centroid Latitude em.detail.ddLatHelp ?	45.05	27.95	44.24	17.75	Not applicable	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	6.4	-82.47	-122.24	-64.75	Not applicable	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	NAD83	Not applicable	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Estimated	Estimated	Estimated	Not applicable	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-71	EM-195	EM-224	EM-260	EM-656	EM-964
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Near Coastal Marine and Estuarine	Forests	Near Coastal Marine and Estuarine	Terrestrial Environment (sub-classes not fully specified)	Open Ocean and Seas
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows.	Subtropical Estuary	Primarily conifer forest	stony coral reef	Urban catchments	Pelagic
EM Ecological Scale em.detail.ecoScaleHelp ?	Not applicable	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 is finer than that of 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-71	EM-195	EM-224	EM-260	EM-656	EM-964
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Not applicable	Species	Guild or Assemblage	Not applicable	Other (Comment) ? Comment: Varied levels of taxonomic order

Taxonomic level and name of organisms or groups identified

EM-71	EM-195	EM-224	EM-260	EM-656	EM-964
None Available	None Available	Species Pinus ponderosa Abies amabilis Tsuga mertensiana Thuja plicata Picea sitchensis Abies procera Abies grandis Chamaecyparis nootkatensis Tsuga heterophylla Other (trees) Pseudotsuga menziesii	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	other Whiting shrimp Whales Seals Cod Phytoplankton Mackerel Benthos Zooplankton Anchovy

EnviroAtlas URL

EM-71	EM-195	EM-224	EM-260	EM-656	EM-964
None Available	Carbon Storage by Tree Biomass	GAP Ecological Systems, Average Annual Precipitation, U.S. EPA (Omernik) ecoregions	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)	Big game hunting recreation demand

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-71	EM-195	EM-224	EM-260	EM-656	EM-964
None	[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	[Ecosystem] Provisioning Materials Biomass Fibres and other materials from plants, algae and animals for direct use or processing [Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[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] 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

<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-71	EM-195	EM-224	EM-260	EM-656	EM-964
None	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Quality Indicator	None	Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Quality Indicator	None	Open Oceans and Seas (114) Fauna (4) Fish 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:

Comment:

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)