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-137
EM-154
EM-260
EM-414
EM-845
EM-859

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-137	EM-154	EM-260	EM-414	EM-845	EM-859
EM Short Name em.detail.shortNameHelp ?	i-Tree Hydro v4.0	Mangrove development, Tampa Bay, FL, USA	Coral taxa and land development, St.Croix, VI, USA	SAV occurrence, St. Louis River, MN/WI, USA	Red-winged blackbird abun, Piedmont region, USA	ARIES Outdoor recreation, Santa Fe, NM
EM Full Name em.detail.fullNameHelp ?	i-Tree Hydro v4.0 (default data option)	Mangrove wetland development, Tampa Bay, FL, USA	Coral taxa richness and land development, St.Croix, Virgin Islands, USA	Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA	Red-winged blackbird abundance, Piedmont ecoregion, USA	Artificial intelligence for Ecosystem Services (ARIES): Outdoor recreation, Santa Fe, New Mexico
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	i-Tree \| USDA Forest Service	US EPA	US EPA	US EPA	None	None
EM Source Document ID	198	97	96	330	405	411
Document Author em.detail.documentAuthorHelp ?	USDA Forest Service	Osland, M. J., Spivak, A. C., Nestlerode, J. A., Lessmann, J. M., Almario, A. E., Heitmuller, P. T., Russell, M. J., Krauss, K. W., Alvarez, F., Dantin, D. D., Harvey, J. E., From, A. S., Cormier, N. and Stagg, C.L.	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	Riffel, S., Scognamillo, D., and L. W. Burger	Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa.
Document Year em.detail.documentYearHelp ?	Not Reported	2012	2011	2013	2008	2018
Document Title em.detail.sourceIdHelp ?	i-Tree Hydro User's Manual v. 4.0	Ecosystem development after mangrove wetland creation: plant–soil change across a 20-year chronosequence	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	Effects of the Conservation Reserve Program on northern bobwhite and grassland birds	Towards globally customizable ecosystem service models
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 ?	Webpage	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-137	EM-154	EM-260	EM-414	EM-845	EM-859
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	http://www.itreetools.org	Not applicable	Not applicable	Not applicable	Not applicable	https://integratedmodelling.org/hub/#/register ? Comment: Need to set up an account first and then can access the main integrated modelling hub page:
Contact Name em.detail.contactNameHelp ?	Not applicable	Michael Osland	Leah Oliver	Ted R. Angradi	Sam Riffell	Javier Martinez-Lopez
Contact Address	Not applicable	U.S. Environmental Protection Agency, Gulf Ecology Division, gulf Breeze, FL 32561	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	Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA	BC3-Basque Centre for Climate Change, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain
Contact Email	Not applicable	mosland@usgs.gov	leah.oliver@epa.gov	angradi.theodore@epa.gov	sriffell@cfr.msstate.edu	javier.martinez@bc3research.org

EM Description

EM ID em.detail.idHelp ?	EM-137	EM-154	EM-260	EM-414	EM-845	EM-859
Summary Description em.detail.summaryDescriptionHelp ?	ABSTRACT: "i-Tree Hydro is the first urban hydrology model that is specifically designed to model vegetation effects and to be calibrated against measured stream flow data. It is designed to model the effects of changes in urban tree cover and impervious surfaces on hourly stream flows and water quality at the watershed level." AUTHOR'S DESCRIPTION: "The purpose of i-Tree Hydro is to simulate hourly changes in stream flow (and water quality) given changes in tree and impervious cover in the watershed. The following is an overview of the process: 1) Determine your watershed of analysis and stream gauge station. i-Tree Hydro works on a watershed basis with the watershed determined as the total drainage area upstream from a measured stream gauge. Stream gauge availability varies. 2) Download national digital elevation data. Once the area and location of the watershed are known, digital elevation data are downloaded from the USGS for an area that encompasses the entire watershed. ArcGIS software is then used to create a digital elevation map and to determine the exact boundary for the watershed upstream from the gauge station location. 3) Determine cover attributes of the watershed and gather other required data. i-Tree Canopy and other sources can be used to determine the tree cover, shrub cover, impervious surface and other cover types. Information about other aspects of the watershed such as proportion of evergreen trees and shrubs, leaf area index, and a variety of hydrologic parameters must be collected. 4) Get started with Hydro. Once these input data are ready, they are loaded into Hydro to begin analysis. 5) Calibrate the model. The Hydro model contains an auto-calibration routine that tries to find the best fit between the stream flow predicted by the model and the stream flow measured at the stream gauge station given the various inputs. The model can also be manually calibrated to improve the fit by changing the parameters as needed. 6) Model new scenarios: Once the model is properly calibrated, tree and impervious cover parameters can be changed to illustrate the impact on stream flow and water quality."	ABSTRACT: "Mangrove wetland restoration and creation effortsare increasingly proposed as mechanisms to compensate for mangrove wetland losses. However, ecosystem development and functional equivalence in restored and created mangrove wetlands are poorly understood. We compared a 20-year chronosequence of created tidal wetland sites in Tampa Bay, Florida (USA) to natural reference mangrove wetlands. Across the chronosequence, our sites represent the succession from salt marsh to mangrove forest communities. Our results identify important soil and plant structural differences between the created and natural reference wetland sites; however, they also depict a positive developmental trajectory for the created wetland sites that reflects tightly coupled plant-soil development. Because upland soils and/or dredge spoils were used to create the new mangrove habitats, the soils at younger created sites and at lower depths (10–30 cm) had higher bulk densities, higher sand content, lower soil organic matter (SOM), lower total carbon (TC), and lower total nitrogen (TN) than did natural reference wetland soils. However, in the upper soil layer (0–10 cm), SOM, TC, and TN increased with created wetland site age simultaneously with mangrove forest growth. The rate of created wetland soil C accumulation was comparable to literature values for natural mangrove wetlands. Notably, the time to equivalence for the upper soil layer of created mangrove wetlands appears to be faster than for many other wetland ecosystem types. Collectively, our findings characterize the rate and trajectory of above- and below-ground changes associated with ecosystem development in created mangrove wetlands; this is valuable information for environmental managers planning to sustain existing mangrove wetlands or mitigate for mangrove wetland losses."	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:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds."	ABSTRACT: "Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The Artificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multicriteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed. "
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	Not applicable	Not applicable	None identified	None reported	None identified
Biophysical Context	No additional description provided	mangrove forest,Salt marsh, estuary, sea level,	nearshore; <1.5 km offshore; <12 m depth	submerged aquatic vegetation	Conservation Reserve Program lands left to go fallow	Watersheds surrounding Santa Fe and Albuquerque, New Mexico
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	Not applicable	Not applicable	No scenarios presented	N/A	N/A

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-137	EM-154	EM-260	EM-414	EM-845	EM-859
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method Only	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application
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	Application of existing model

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

EM ID em.detail.idHelp ?	EM-137	EM-154	EM-260	EM-414	EM-845	EM-859
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-190 \| Doc-223	None	None	None	Doc-405	Doc-411
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-109 \| EM-142 \| EM-51	None	None	None	EM-831 \| EM-838 \| EM-839 \| EM-840 \| EM-841 \| EM-842 \| EM-843 \| EM-844 \| EM-846 \| EM-847	EM-855 \| EM-856 \| EM-858

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-137	EM-154	EM-260	EM-414	EM-845	EM-859
EM Temporal Extent em.detail.tempExtentHelp ?	Not applicable	1990-2010	2006-2007	2010 - 2012	2008	1981-2015
EM Time Dependence em.detail.timeDependencyHelp ?	time-dependent	time-dependent	time-stationary	time-stationary	time-stationary	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	future time	Not applicable	Not applicable	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	discrete	continuous	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	1	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Hour	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-137	EM-154	EM-260	EM-414	EM-845	EM-859
Bounding Type em.detail.boundingTypeHelp ?	Not applicable	Physiographic or Ecological	Physiographic or Ecological	Physiographic or ecological	Physiographic or ecological	Watershed/Catchment/HUC
Spatial Extent Name em.detail.extentNameHelp ?	Not applicable	Tampa Bay	St.Croix, U.S. Virgin Islands	St. Louis River Estuary	Piedmont Ecoregion	Santa Fe Fireshed
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	Not applicable	100-1000 km^2	10-100 km^2	10-100 km^2	100,000-1,000,000 km^2	100-1000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-137	EM-154	EM-260	EM-414	EM-845	EM-859
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 lumped (in all cases)	spatially distributed (in at least some 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	Not applicable	area, for pixel or radial feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	30 x 30 m	m^2	Not applicable	0.07 m^2 to 0.70 m^2	Not applicable	30 m

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-137	EM-154	EM-260	EM-414	EM-845	EM-859
EM Computational Approach em.detail.emComputationalApproachHelp ?	Numeric	Analytic	Analytic	Analytic	Analytic	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-137	EM-154	EM-260	EM-414	EM-845	EM-859
Model Calibration Reported? em.detail.calibrationHelp ?	Not applicable	No	Yes	Yes	Yes	Unclear
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Not applicable	No	Yes	Yes	No	No
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 ?	Not applicable	No	No	Yes	No	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	Not applicable	Yes	Yes	No	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	Not applicable	Yes	No	No	Yes	No
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	No	Not applicable	Not applicable	Unclear	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])

EM-137	EM-154	EM-260	EM-414	EM-845	EM-859
None	North America United States Florida	None	North America United States Minnesota Wisconsin	North America United States North Carolina Georgia Alabama Maryland New Jersey South Carolina Pennsylvania	North America United States New Mexico

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

EM-137	EM-154	EM-260	EM-414	EM-845	EM-859
None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Floridian ? Comment: Realm: Tropical Atlantic Region: West Tropical Atlantic Province: Tropical Northwestern Atlantic Ecoregion: Floridian	Tropical Atlantic West Tropical Atlantic Tropical Southwestern Atlantic	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-137	EM-154	EM-260	EM-414	EM-845	EM-859
Centroid Latitude em.detail.ddLatHelp ?	-9999	27.8	17.75	46.72	36.23	35.86
Centroid Longitude em.detail.ddLongHelp ?	-9999	-82.4	-64.75	-96.13	-81.9	-105.76
Centroid Datum em.detail.datumHelp ?	Not applicable	WGS84	NAD83	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Not applicable	Estimated	Estimated	Estimated	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-137	EM-154	EM-260	EM-414	EM-845	EM-859
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Rivers and Streams \| Ground Water \| Created Greenspace	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Grasslands	Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	Urban watersheds	Created Mangrove wetlands	stony coral reef	Freshwater estuarine system	grasslands	watersheds
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-137	EM-154	EM-260	EM-414	EM-845	EM-859
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Not applicable	Guild or Assemblage	Not applicable	Species	Not applicable

Taxonomic level and name of organisms or groups identified

EM-137	EM-154	EM-260	EM-414	EM-845	EM-859
None Available	Species Laguncularia racemosa Avicennia germinans Rhizophora mangle Spartina alterniflora	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	Species Redwinged blackbird	None Available

EnviroAtlas URL

EM-137	EM-154	EM-260	EM-414	EM-845	EM-859
GAP Ecological Systems, National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Percent Impervious Area, Water supply from NID reservoirs (million gallons)	None Available	None Available	Average Annual Precipitation	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions	Dasymetric Allocation of Population, Ecosystem Markets: Imperiled Species and Habitats

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-137	EM-154	EM-260	EM-414	EM-845	EM-859
[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection Hydrological cycle and water flow maintenance	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal	[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 Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Non-ecosystem] Cultural settings dependent on abiotic structures Physical and intellectual interactions with land-/seascapes [physical settings] By physical and experiential interactions or intellectual and representational interactions Not applicable

<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-137	EM-154	EM-260	EM-414	EM-845	EM-859
Rivers and Streams (111) Composite (8) Regulation of extreme events Flow Indicator Urban/Suburban (27) Composite (8) Regulation of extreme events Flow Indicator	Near Coastal Marine/Estuarine (113) Flora (5) Trees Stock Indicator Seaweed Stock Indicator Composite (8) Regulation of extreme events Quality Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Quality Indicator	None	Grasslands (23) Fauna (4) Birds Stock Indicator	None

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)

Comment:

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)