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-65
EM-71
EM-195
EM-414
EM-735
EM-743

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-195	EM-414	EM-735	EM-743
EM Short Name em.detail.shortNameHelp ?	Green biomass production, Central French Alps	Community flowering date, Central French Alps	C Sequestration and De-N, Tampa Bay, FL, USA	SAV occurrence, St. Louis River, MN/WI, USA	C sequestration in grassland restoration, England	WESP: Irrigation water, ID, USA
EM Full Name em.detail.fullNameHelp ?	Green biomass production, Central French Alps	Community weighted mean flowering date, Central French Alps	Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA	Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA	Carbon sequestration in grassland diversity restoration, England	WESP: Irrigation return water treatment, Idaho, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	EU Biodiversity Action 5	US EPA	US EPA	None	None
EM Source Document ID	260	260	186	330	396	393 ? Comment: Additional data came from electronic appendix provided by author Chris Murphy.
Document Author em.detail.documentAuthorHelp ?	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Russell, M. and Greening, H.	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	Murphy, C. and T. Weekley
Document Year em.detail.documentYearHelp ?	2011	2011	2013	2013	2011	2012
Document Title em.detail.sourceIdHelp ?	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Estimating benefits in a recovering estuary: Tampa Bay, Florida	Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary	Additional carbon sequestration benefits of grassland diversity restoration	Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context.
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 report

Software and Access

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-195	EM-414	EM-735	EM-743
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Sandra Lavorel	M. Russell	Ted R. Angradi	Gerlinde B. De Deyn	Chris Murphy
Contact Address	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	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. 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	Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID
Contact Email	sandra.lavorel@ujf-grenoble.fr	sandra.lavorel@ujf-grenoble.fr	Russell.Marc@epamail.epa.gov	angradi.theodore@epa.gov	g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com	chris.murphy@idfg.idaho.gov

EM Description

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-195	EM-414	EM-735	EM-743
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. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)."	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: “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…"	A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	Restoration of seagrass	None identified	None identified	None identified
Biophysical Context	Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes	Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes	Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove	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.	restored, enhanced and created wetlands
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	Habitat loss or restoration in Tampa Bay Estuary	No scenarios presented	Additional benefits due to biodiversity restoration practices	Sites, function or habitat focus

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-195	EM-414	EM-735	EM-743
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application (multiple runs exist) View EM Runs
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-65	EM-71	EM-195	EM-414	EM-735	EM-743
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260	Doc-260 \| Doc-269	None	None	None	Doc-390
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-71 \| EM-79 \| EM-80 \| EM-81 \| EM-82 \| EM-83	EM-65 \| EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-79 \| EM-80 \| EM-81 \| EM-82 \| EM-83	None	None	None	EM-718 \| EM-734 \| EM-760 \| EM-761 \| EM-763 \| EM-764 \| EM-766 \| EM-767 \| EM-768

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-195	EM-414	EM-735	EM-743
EM Temporal Extent em.detail.tempExtentHelp ?	2007-2009	2007-2008	1982-2010	2010 - 2012	1990-2007	2010-2012
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	past time
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-195	EM-414	EM-735	EM-743
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Physiographic or Ecological	Physiographic or Ecological	Physiographic or ecological	Other	Multiple unrelated locations (e.g., meta-analysis)
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	Central French Alps	Tampa Bay Estuary	St. Louis River Estuary	Colt Park meadows, Ingleborough National Nature Reserve, northern England	Wetlands in idaho
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	10-100 km^2	1000-10,000 km^2.	10-100 km^2	<1 ha	100,000-1,000,000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-195	EM-414	EM-735	EM-743
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 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	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	20 m x 20 m	1 ha	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-65	EM-71	EM-195	EM-414	EM-735	EM-743
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	Analytic	Numeric
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-65	EM-71	EM-195	EM-414	EM-735	EM-743
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	Yes	Yes	Not applicable	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	Yes	No	Yes	Not applicable	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	Adjusted R \| 69.9 \| Not applicable	R squared \| 45.2 \| Not applicable	None	p-value \| <0.001 \| unitless Nagelkerke r2 \| 0.30-0.62 \| unitless	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	No	No	Yes	No	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	No
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-65	EM-71	EM-195	EM-414	EM-735	EM-743
Europe France	Europe France	None	North America United States Minnesota Wisconsin	Europe United Kingdom	North America United States Idaho

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

EM-65	EM-71	EM-195	EM-414	EM-735	EM-743
None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Floridian	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-195	EM-414	EM-735	EM-743
Centroid Latitude em.detail.ddLatHelp ?	45.05	45.05	27.95	46.72	54.2	44.06
Centroid Longitude em.detail.ddLongHelp ?	6.4	6.4	-82.47	-96.13	-2.35	-114.69
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Provided	Estimated	Estimated	Provided	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-195	EM-414	EM-735	EM-743
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Agroecosystems \| Grasslands	Near Coastal Marine and Estuarine	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Agroecosystems \| Grasslands	Inland Wetlands
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows	Subalpine terraces, grasslands, and meadows.	Subtropical Estuary	Freshwater estuarine system	fertilized grassland (historically hayed)	created, restored and enhanced wetlands
EM Ecological Scale em.detail.ecoScaleHelp ?	Not applicable	Not applicable	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 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-65	EM-71	EM-195	EM-414	EM-735	EM-743
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Community	Not applicable	Not applicable	Community	Not applicable

Taxonomic level and name of organisms or groups identified

EM-65	EM-71	EM-195	EM-414	EM-735	EM-743
None Available	None Available	None Available	None Available	None Available	None Available

EnviroAtlas URL

EM-65	EM-71	EM-195	EM-414	EM-735	EM-743
GAP Ecological Systems	None Available	Carbon Storage by Tree Biomass	Average Annual Precipitation	None Available	Total Annual Reduced Nitrogen Deposition, Carbon Storage by Tree Biomass

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-65	EM-71	EM-195	EM-414	EM-735	EM-743
None	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] 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 Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Water conditions Chemical condition of freshwaters Mediation of flows Liquid flows Flood protection Hydrological cycle and water flow maintenance Mass flows Mass stabilisation and control of erosion rates Mediation of waste, toxics and other nuisances Mediation by biota Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals

<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-65	EM-71	EM-195	EM-414	EM-735	EM-743
Grasslands (23) Flora (5) Green biomass Stock Indicator Agroecosystems (22) Flora (5) Green biomass Stock Indicator	None	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Quality Indicator	None	None	None