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-224
EM-260
EM-414
EM-735
EM-998

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-195	EM-224	EM-260	EM-414	EM-735	EM-998
EM Short Name em.detail.shortNameHelp ?	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	SAV occurrence, St. Louis River, MN/WI, USA	C sequestration in grassland restoration, England	CAESAR landscape evolution model
EM Full Name em.detail.fullNameHelp ?	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	Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA	Carbon sequestration in grassland diversity restoration, England	Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	US EPA	US EPA	US EPA	US EPA	None	None
EM Source Document ID	186	23 ? Comment: Related document ID 22 is a secondary source providing tree species specific parameters in appendix.	96	330	396	468
Document Author em.detail.documentAuthorHelp ?	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.	Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke	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	Van De Wiel, M. J., Coulthard, T. J., Macklin, M. G., & Lewin, J.
Document Year em.detail.documentYearHelp ?	2013	2007	2011	2013	2011	2007
Document Title em.detail.sourceIdHelp ?	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	Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary	Additional carbon sequestration benefits of grassland diversity restoration	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	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-195	EM-224	EM-260	EM-414	EM-735	EM-998
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	http://www.coulthard.org.uk/
Contact Name em.detail.contactNameHelp ?	M. Russell	Richard T. Busing	Leah Oliver	Ted R. Angradi	Gerlinde B. De Deyn	Marco J. Van De Wiel
Contact Address	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	U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA	Dept. of Terrestrial Ecology, Netherlands Institute of Ecology, P O Box 40, 6666 ZG Heteren, The Netherlands	Department of Geography, University of Western Ontario, London, Ontario, Canada
Contact Email	Russell.Marc@epamail.epa.gov	rtbusing@aol.com	leah.oliver@epa.gov	angradi.theodore@epa.gov	g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com	mvandew3@uwo.ca

EM Description

EM ID em.detail.idHelp ?	EM-195	EM-224	EM-260	EM-414	EM-735	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)	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)	ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake."	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…"	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	None Identified	Not applicable	None identified	None identified	None identified
Biophysical Context	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	submerged aquatic vegetation	Lolium perenne-Cynosorus cristatus grassland; The soil is a shallow brown-earth (average depth 28 cm) over limestone of moderate-high residual fertility.	River Teifi, Lampeter, Wales
EM Scenario Drivers em.detail.scenarioDriverHelp ?	Habitat loss or restoration in Tampa Bay Estuary	Two scenarios modelled, forests with and without fire	Not applicable	No scenarios presented	Additional benefits due to biodiversity restoration practices	Varying flow velocities and durations

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-195	EM-224	EM-260	EM-414	EM-735	EM-998
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	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 + Application	Method + Application (multiple runs exist) View EM Runs	Method Only
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	Application of existing 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-224	EM-260	EM-414	EM-735	EM-998
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	None	Doc-22 \| Doc-23	None	None	None	Doc-467
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-146 \| EM-208 \| EM-186	None	None	None	EM-997

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-195	EM-224	EM-260	EM-414	EM-735	EM-998
EM Temporal Extent em.detail.tempExtentHelp ?	1982-2010	>650 yrs	2006-2007	2010 - 2012	1990-2007	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-dependent	time-stationary	time-stationary	time-stationary	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	past time	Not applicable	Not applicable	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	discrete	Not applicable	Not applicable	Not applicable	continuous
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	1	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Year	Not applicable	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-195	EM-224	EM-260	EM-414	EM-735	EM-998
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Physiographic or ecological	Physiographic or Ecological	Physiographic or ecological	Other	Watershed/Catchment/HUC
Spatial Extent Name em.detail.extentNameHelp ?	Tampa Bay Estuary	West Cascades, Oregon	St.Croix, U.S. Virgin Islands	St. Louis River Estuary	Colt Park meadows, Ingleborough National Nature Reserve, northern England	River Teifi
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	1000-10,000 km^2.	100-1000 km^2	10-100 km^2	10-100 km^2	<1 ha	1000-10,000 km^2.

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-195	EM-224	EM-260	EM-414	EM-735	EM-998
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially distributed (in at least some cases) ? Comment: BH: Each individual transect?s data was parceled into location reports, and that each report?s ?quadrat? area was dependent upon the angle of the hydroacoustic sampling beam. The spatial grain is 0.07 m^2, 0.20 m^2 and 0.70 m^2 for depths of 1 meter, 2 meters and 3 meters, respectively.	spatially distributed (in at least some cases)	spatially lumped (in all cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	area, for pixel or radial feature	Not applicable	area, for pixel or radial feature	area, for pixel or radial feature	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	1 ha	0.08 ha	Not applicable	0.07 m^2 to 0.70 m^2	3 m x 3 m	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-195	EM-224	EM-260	EM-414	EM-735	EM-998
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Numeric	Analytic	Analytic	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	stochastic	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-224	EM-260	EM-414	EM-735	EM-998
Model Calibration Reported? em.detail.calibrationHelp ?	Yes	No	Yes	Yes	Not applicable	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	Yes	Yes	Not applicable	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	R squared (taxa richness) \| 0.74 \| Not applicable p-value \| 0.0020 \| unitless	p-value \| <0.001 \| unitless Nagelkerke r2 \| 0.30-0.62 \| unitless	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	Yes	No	Yes	No	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	Yes	No	No	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	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-224	EM-260	EM-414	EM-735	EM-998
None	North America United States Oregon	None	North America United States Minnesota Wisconsin	Europe United Kingdom	Europe United Kingdom

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

EM-195	EM-224	EM-260	EM-414	EM-735	EM-998
Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Floridian	None	Tropical Atlantic West Tropical Atlantic Tropical Southwestern Atlantic	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-195	EM-224	EM-260	EM-414	EM-735	EM-998
Centroid Latitude em.detail.ddLatHelp ?	27.95	44.24	17.75	46.72	54.2	52.04
Centroid Longitude em.detail.ddLongHelp ?	-82.47	-122.24	-64.75	-96.13	-2.35	-4.39
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	NAD83	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Estimated	Estimated	Provided	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-195	EM-224	EM-260	EM-414	EM-735	EM-998
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Near Coastal Marine and Estuarine	Forests	Near Coastal Marine and Estuarine	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Agroecosystems \| Grasslands	Rivers and Streams
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subtropical Estuary	Primarily conifer forest	stony coral reef	Freshwater estuarine system	fertilized grassland (historically hayed)	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-224	EM-260	EM-414	EM-735	EM-998
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Species	Guild or Assemblage	Not applicable	Community	Not applicable

Taxonomic level and name of organisms or groups identified

EM-195	EM-224	EM-260	EM-414	EM-735	EM-998
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	None Available	None Available

EnviroAtlas URL

EM-195	EM-224	EM-260	EM-414	EM-735	EM-998
Carbon Storage by Tree Biomass	GAP Ecological Systems, Average Annual Precipitation, U.S. EPA (Omernik) ecoregions	None Available	Average Annual Precipitation	None Available	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-224	EM-260	EM-414	EM-735	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	[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 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 Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	[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-224	EM-260	EM-414	EM-735	EM-998
Near Coastal Marine/Estuarine (113) Water (3) Liquid water Quality Indicator	None	Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Quality Indicator	None	None	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:

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

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)