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:

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Comparing:

EM-83
EM-86
EM-109
EM-428
EM-439
EM-466
EM-542
EM-649
EM-655
EM-705
EM-820
EM-841
EM-845
EM-875
EM-971
EM-1011

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID

EM-83

EM-86

EM-109

EM-428

EM-439

EM-466

EM-542

EM-649

EM-655

EM-705

EM-820

EM-841

EM-845

EM-875

EM-971

EM-1011

EM Short Name

Soil carbon and plant traits, Central French Alps

Area and hotspots of soil retention, South Africa

UFORE-Hydro, Baltimore, MD, USA

Retained rainwater, Guánica Bay, Puerto Rico

WaSSI, Conterminous USA

Yasso 15 - soil carbon model

Coastal protection in Belize

Grasshopper Sparrow density, CREP, Iowa, USA

Hunting recreation, Wisconsin, USA

Total duck recruits, CREP wetlands, Iowa, USA

MMI method for aquatic surveys

Brown-headed cowbird abundance, Piedmont, USA

Red-winged blackbird abun, Piedmont region, USA

Valuing environmental ed., New York, New York

Stormwater pollutant loads, Finland

WMOST method

EM Full Name

Soil carbon potential estimated from plant functional traits, Central French Alps

Area and hotspots of soil retention, South Africa

UFORE-Hydro (Urban Forest Effects - Hydrology) v1, Dead Run Catchment, Baltimore, MD ?

Retained rainwater, Guánica Bay, Puerto Rico, USA

Water Supply Stress Index, Conterminous USA

Yasso 15 - soil carbon

Coastal Protection provided by Coral, Seagrasses and Mangroves in Belize:

Grasshopper Sparrow population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA

Hunting recreation, Wisconsin, USA

Total duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA

Multimetric Indice (MMI) method for large scale aquatic surveys

Brown-headed cowbird abundance, Piedmont ecoregion, USA

Red-winged blackbird abundance, Piedmont ecoregion, USA

Valuing environmental education, Hudson River Park, New York, New York

Assessment of stormwater pollutant loads and source area contributions with storm water management model (SWMM)

Watershed Management Optimization Support Tool (WMOST) v1 method

EM Source or Collection

EU Biodiversity Action 5

None

i-Tree | USDA Forest Service

US EPA

USDA Forest Service ?

None

InVEST

None

US EPA

None

US EPA

EM Source Document ID

260

271

190

338

341

342 ?

350

372

376

372 ?

403

405

416

455

477

Document Author

Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.

Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S.

Wang, J., Endreny, T. A. and Nowak, D. J.

Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher

Peter Caldwell, Ge Sun, Steve McNulty, Jennifer Moore Myers, Erika Cohen, Robert Herring, Erik Martinez

Repo, A., Jarvenpaa, M., Kollin, J., Rasinmaki, J. and Liski, J.

Guannel, G., Arkema, K., Ruggiero, P., and G. Verutes

Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever

Qiu, J. and M. G. Turner

Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever

Stoddard, J.L., Herlihy, A.T., Peck, D.V., Hughes, R.M., Whittier, T.R., and E. Tarquinio

Riffel, S., Scognamillo, D., and L. W. Burger

Hutcheson, W. Hoagland, P., and D. Jin

Tuomela, C., N., Sillanpaa, and H. Koivusalo

United States EPA

Document Year

2011

2008

2017

2013

2016

2010

2013

2010

2008

2018

2019

2013

Document Title

Using plant functional traits to understand the landscape distribution of multiple ecosystem services

Mapping ecosystem services for planning and management

Mechanistic simulation of tree effects in an urban water balance model

Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico

WaSSI Ecosystem Services Model

Yasso 15 graphical user-interface manual

The Power of Three: Coral Reefs, Seagrasses and Mangroves Protect Coastal Regions and Increase Their Resilience

Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt

Spatial interactions among ecosystem services in an urbanizing agricultural watershed

Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt

A process for creating multimetric indices for large-scale A process for creating multimetic indices for large-scale aquatic surveys

Effects of the Conservation Reserve Program on northern bobwhite and grassland birds

Valuing environmental education as a cultural ecosystem service at Hudson River Park

Assessment of stormwater pollutant loads and source area contributions with storm water management model (SWMM)

Watershed Management Optimization Support Tool (WMOST) v1 User manual

Document Status

Peer reviewed and published

Not peer reviewed but is published (explain in Comment)

Other or unclear (explain in Comment)

Peer reviewed and published

Comments on Status

Published journal manuscript

While the user guide on which model entry is based has not been peer reviewed, several peer reviewed journal articles describing this USA HUC8 version of WaSSI have been published.

Not applicable

Published journal manuscript

Published report

Published journal manuscript

Published report

Published journal manuscript

Published EPA report

Software and Access

EM ID

EM-83

EM-86

EM-109

EM-428

EM-439

EM-466

EM-542

EM-649

EM-655

EM-705

EM-820

EM-841

EM-845

EM-875

EM-971

EM-1011

EM Software Access info Exit

Not applicable

http://www.itreetools.org/

Not applicable

http://www.wassiweb.sgcp.ncsu.edu/

http://en.ilmatieteenlaitos.fi/yasso-download-and-support ?

Not identified in paper

Not applicable

https://www.epa.gov/water-research/storm-water-management-model-swmm

https://cfpub.epa.gov/si/si_public_record_report.cfm?Lab=NHEERL&dirEntryId=262280

Contact Name

Sandra Lavorel

Benis Egoh

Jun Wang

Susan H. Yee

Ge Sun

Jari Liski

Greg Guannel

David Otis

Monica G. Turner

David Otis

John Stoddard

Sam Riffell

Walter Hutcheson

Camilla Tuomela

Naomi Detenbeck

Contact Address

Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France

Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy

Environmental Resources and Forest Engineering, Colecge of Environmental Science and Forestry, State University of New York, Syracuse, New York 13210

U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA

Eastern Forest Environmental Threat Assessment Center, Southern Research Station, USDA Forest Service, 920 Main Campus Dr. Venture II, Suite 300, Raleigh, NC 27606

Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki

The Nature Conservancy, Coral Gables, FL. USA

U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University

Not reported

U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University

200 SW 35th St., Corvallis, OR 97333

Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA

New York University, United States

Department of Built Environment, Aalto University School of Engineering, P.O. Box 15200, FI- 00076, Aalto, Finland

NHEERL, Atlantic Ecology Division Narragansett, RI 02882

Contact Email

sandra.lavorel@ujf-grenoble.fr

Not reported

yee.susan@epa.gov

gesun@fs.fed.us

jari.liski@ymparisto.fi

greg.guannel@gmail.com

dotis@iastate.edu

turnermg@wisc.edu

dotis@iastate.edu

stoddard.john@epa.gov

sriffell@cfr.msstate.edu

wwh235@nyu.edu

camilla.tuomela@aalto.fi

detenbeck.naomi@epa.gov

EM Description

EM ID

EM-83

EM-86

EM-109

EM-428

EM-439

EM-466

EM-542

EM-649

EM-655

EM-705

EM-820

EM-841

EM-845

EM-875

EM-971

EM-1011

Summary Description

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: "The soil carbon ecosystem service map was a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to the soil carbon ecosystem service are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions."

AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil retention was modelled as a function of vegetation or litter cover and soil erosion potential. Schoeman et al. (2002) modelled soil erosion potential and derived eight erosion classes, ranging from low to severe erosion potential for South Africa. The vegetation cover was mapped by ranking vegetation types using expert knowledge of their ability to curb erosion. We used Schulze (2004) index of litter cover which estimates the soil surface covered by litter based on observations in a range of grasslands, woodlands and natural forests. According to Quinton et al. (1997) and Fowler and Rockstrom (2001) soil erosion is slightly reduced with about 30%, significantly reduced with about 70% vegetation cover. The range of soil retention was mapped by selecting all areas that had vegetation or litter cover of more than 30% for both the expert classified vegetation types and litter accumulation index within areas with moderate to severe erosion potential. The hotspot was mapped as areas with severe erosion potential and vegetation/litter cover of at least 70% where maintaining the cover is essential to prevent erosion. An assumption was made that the potential for this service is relatively low in areas with little natural vegetation or litter cover."

ABSTRACT: "A semidistributed, physical-based Urban Forest Effects – Hydrology (UFORE-Hydro) model was created to simulate and study tree effects on urban hydrology and guide management of urban runoff at the catchment scale. The model simulates hydrological processes of precipitation, interception, evaporation, infiltration, and runoff using data inputs of weather, elevation, and land cover along with nine channel, soil, and vegetation parameters. Weather data are pre-processed by UFORE using Penman-Monteith equations to provide potential evaporation terms for open water and vegetation. Canopy interception algorithms modified established routines to better account for variable density urban trees, short vegetation, and seasonal growth phenology. Actual evaporation algorithms allocate potential energy between leaf surface storage and transpiration from soil storage. Infiltration algorithms use a variable rain rate Green-Ampt formulation and handle both infiltration excess and saturation excess ponding and runoff. Stream discharge is the sum of surface runoff and TOPMODEL- based subsurface flow equations. Automated calibration routines that use observed discharge has been coupled to the model." FURTHER DESCRIPTION: UFORE-Hydro was tested in the urban Dead Run catchment of Baltimore, Maryland, USA.

AUTHOR'S DESCRIPTION: "In total, 19 ecosystem services metrics were identified as relevant to stakeholder objectives in the Guánica Bay watershed identified during the 2013 Public Values Forum (Table 2)...Ecological production functions were applied to translate LULC measures of ecosystem condition to supply of ecosystem services…The volume of retained rainwater per unit area (in^3/in^2) includes both the maximum soil moisture retention and the initial abstraction of water before runoff due to infiltration, evaporation, or interception by vegetation…"

AUTHORS DESCRIPTION: "WaSSI simulates monthly water and carbon dynamics at the Hydrologic Unit Code 8 level in the US. Three modules are integrated within the WaSSI model framework. The water balance module computes ecosystem water use, evapotranspiration and the water yield from each watershed. Water yield is sometimes referred to as runoff and can be thought of as the amount of streamflow at the outlet of each watershed due to hydrologic processes in each watershed in isolation without any flow contribution from upstream watersheds. The ecosystem productivity module simulates carbon gains and losses in each watershed or grid cell as functions of evapotranspiration. The water supply and demand module routes and accumulates the water yield through the river network according to topological relationships between adjacent watersheds, subtracts consumptive water use by humans from river flows, and compares water supply to water demand to compute the water supply stress index, or WaSSI."

AUTHOR'S DESCRIPTION: "The Yasso15 calculates the stock of soil organic carbon, changes in the stock of soil organic carbon and heterotrophic soil respiration. Applications the model include, for example, simulations of land use change, ecosystem management, climate change, greenhouse gas inventories and education. The Yasso15 is a relatively simple soil organic carbon model requiring information only on climate and soil carbon input to operate... In the Yasso15 model litter is divided into five soil organic carbon compound groups (Fig. 1). These groups are compounds hydrolysable in acid (denoted with A), compounds soluble in water (W) or in a non-polar solvent, e.g. ethanol or dichloromethane (E), compounds neither soluble nor hydrolysable (N) and humus (H). The AWEN form the group of labile fractions whereas H fraction contains humus, which is more recalcitrant to decomposition. Decomposition of the fractions results in carbon flux out of soil and carbon fluxes between the compartments (Fig. 1). The basic idea of Yasso15 is that the decomposition of different types of soil carbon input depends on the chemical composition of the input types and climate conditions. The effects of the chemical composition are taken into account by dividing carbon input to soil between the four labile compartments explicitly according to the chemical composition (Fig. 1). Decomposition of woody litter depends additionally on the size of the litter. The effects of climate conditions are modelled by adjusting the decomposition rates of the compartments according to air temperature and precipitation. In the Yasso15 model separate decomposition rates are applied to fast-decomposing A, W and E compartments, more slowly decomposing N and very slowly decomposing humus compartment H. The Yasso is a global-level model meaning that the same parameter values are suitable for all applications for accurate predictions. However, the current GUI version also includes possibility to use earlier parameterizations. The parameter values of Yasso15 are based on measurements related to cycling of organic carbon in soil (Table 1). An extensive set of litter decomposition measurements was fundamental in developing the model (Fig. 2). This data set covered, firstly, most of the global climate conditions in terms of temperature precipitation and seasonality (Fig 3.), secondly, different ecosystem types from forests to grasslands and agricultural fields and, thirdly, a wide range of litter types. In addition, a large set of data giving information on decomposition of woody litter (including branches, stems, trunks, roots with different size classes) was used for fitting. In addition to woody and non-woody litter decomposition measurements, a data set on accumulation of soil carbon on the Finnish coast and a large, global steady state data sets were used in the parameterization of the model. These two data sets contain information on the formation and slow decomposition of humus."

AUTHOR'S DESCRIPTION: "Natural habitats have the ability to protect coastal communities against the impacts of waves and storms, yet it is unclear how different habitats complement each other to reduce those impacts. Here, we investigate the individual and combined coastal protection services supplied by live corals on reefs, seagrass meadows, and mangrove forests during both non-storm and storm conditions, and under present and future sea-level conditions. Using idealized profiles of fringing and barrier reefs, we quantify the services supplied by these habitats using various metrics of inundation and erosion. We find that, together, live corals, seagrasses, and mangroves supply more protection services than any individual habitat or any combination of two habitats. Specifically, we find that, while mangroves are the most effective at protecting the coast under non-storm and storm conditions, live corals and seagrasses also moderate the impact of waves and storms, thereby further reducing the vulnerability of coastal regions. Also, in addition to structural differences, the amount of service supplied by habitats in our analysis is highly dependent on the geomorphic setting, habitat location and forcing conditions: live corals in the fringing reef profile supply more protection services than seagrasses; seagrasses in the barrier reef profile supply more protection services than live corals; and seagrasses, in our simulations, can even compensate for the long-term degradation of the barrier reef. Results of this study demonstrate the importance of taking integrated and place-based approaches when quantifying and managing for the coastal protection services supplied by ecosystems."

ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Grasshopper Sparrow (Ammodramus savannarum)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: GRSP density = e (-2.554612 + 0.0246975 * grass400 – 0.1032461 * trees400)

AUTHOR'S DESCRIPTION (from Supporting Information): "The hunting recreation service was estimated as a function of the extent of wildlife areas open for hunting, the number of game species, proximity to population center, and accessibility. Similar assumptions were made for this assessment: larger areas and places with more game species would support more hunting, areas closer to large population centers would be used more than remote areas, and proximity to major roads would increase access and use of an area. We first obtained the boundary of public wild areas from Wisconsin DNR and calculated the amount of areas for each management unit. The number of game species (Spe) for each area was derived from Dane County Parks Division (70). We used the same population density (Pop) and road buffer layer (Road) described in the previous forest recreation section. The variables Spe, Pop, and Road were weighted to ranges of 0–40, 0–40, and 0–20, respectively, based on the relative importance of each in determining this service. We estimated overall hunting recreation service for each 30-m grid cell with the following equation: HRSi = Ai Σ(Spei + Popi +Roadi), where HRS is hunting recreation score, A is the area of public wild areas open for hunting/fishing, Spe represents the number of game species, Pop stands for the proximity to population centers, and Road is the distance to major roads. To simplify interpretation, we rescaled the original hunting recreation score (ranging from 0 to 28,000) to a range of 0–100, with 0 representing no hunting recreation service and 100 representing highest service.

ABSTRACT: "Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2*R*n where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = H*Z*B/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632).

Abstract: "Differences in sampling and laboratory protocols, differences in techniques used to evaluate metrics, and differing scales of calibration and application prohibit the use of many existing multimetric indices (MMIs) in large-scale bioassessments. We describe an approach to developing MMIs of ecological condition that is applicable to a variety of biological assemblage types and to spatially extensive (regional, national) aquatic resource surveys. The process involves testing the performance characteristics of candidate metrics in several categories that correspond to key dimensions of biotic condition. The performance characteristics include: information content (range), reproducibility, calibration for natural gradients, responsiveness to stressor gradients, and independence from other metrics. The best-performing metric from each category is included in the final MMI. The consistency of the process enables development of separate MMIs in different regions that can be combined in a national assessment and that are more comparable across regions and taxonomic groups than a set of independently developed MMIs would be. Range: Generally eliminate metrics if their range is <4 or if > 1/3 of samples have values = 0. Very few macroinvertebrate metrics are eliminated by this test. It does eliminate a large number of potentially poor metrics for assemblages with fewer taxa (e.g., fish). Reproducibility: We quantify metric reproducibility with a variant of the signal:noise ratio (S/N). S/N is the ratio of the variance among all sites (signal) to the variance of repeated visits to the same site (noise). S/N values 1 indicate that visiting a single site twice yields as much metric variability as visiting 2 different sites. Natural gradient calibration: Focusing solely on reference-site data and to quantify the remaining correspondence between the metric value and the natural gradient. Responsiveness: The ability of a metric to distinguish least-disturbed (reference) from most-disturbed sites. We identify the metrics that have the highest responsiveness (t-scores) within each class and aggregated ecoregion. Redundancy: We often consider metrics as too strongly correlated when their Pearson correlation coefficients at least-disturbed sites are > |0.71| (R2 = 0.5).

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: " The Hudson River and its estuary is once again an ecologically, economically, and culturally functional component of New York City’s natural environment. The estuary’s cultural significance may derive largely from environmental education, including marine science programs for the public. These programs are understood as ‘‘cultural” ecosystem services but are rarely evaluated in economic terms. We estimated the economic value of the Hudson River Park’s environmental education programs. We compiled data on visits by schools and summer camps from 32 New York City school districts to the Park during the years 2014 and 2015. A ‘‘travel cost” approach was adapted from the field of environmental economics to estimate the value of education in this context. A small—but conservative—estimate of the Park’s annual education program benefits ranged between $7500 and 25,500, implying an average capitalized value on the order of $0.6 million. Importantly, organizations in districts with high proportions of minority students or English language learners were found to be more likely to participate in the Park’s programs. The results provide an optimistic view of the benefits of environmental education focused on urban estuaries, through which a growing understanding of ecological systems could lead to future environmental improvements. "

Decentralized urban runoff management requires detailed information about pollutant sources and pathways. However, scarce data of local water quality compel simplified approaches in water quality modelling. This study investigated the use of constant source concentrations in modelling pollutant loads. The source area contributions of total suspended solids, total phosphorus, total nitrogen, lead, copper and zinc were modelled with SWMM based on literature event mean concentrations (EMCs) for different land cover types and on-site rainfall and discharge data for a residential area in southern Finland. The simulated pollutant loads were compared with loads measured at the catchment outlet. Large differences were evident in the modelled catchment-scale and land cover specific loads, depending on the EMC data source. The simulated loads exceeded the measured loads especially during wet conditions, which was explained by the dilution effect of large stormwater volumes on measured EMCs. In addition, the mismatch was explained by the lack of local data for the source area EMCs and by the unaccountability of the mechanisms affecting loads along the pollutant pathways from source areas to sewer outlet. The spatial simulation of stormwater pollutant loads enabled the assessment of source area contributions at the catchment scale, as well as the pollutant pathways and the total diffuse pollution load. For a single pollutant, one or two important pollutant sources contributed the majority of the catchment load, which provides useful information for stormwater management. However, for a group of pollutants, no single land cover type dominated the pollutant loads, reflecting the challenges in decentralized water quality management in the scale of a residential area. Overall, the results emphasize that the widely used stormwater quality modelling with constant EMCs is uncertain even when on-site water quality and rainfall-runoff data from a catchment outlet are available.

ABSTRACT: "The Watershed Management Optimization Support Tool (WMOST) is intended to be used as a screening tool as part of an integrated watershed management process such as that described in EPA’s watershed planning handbook (EPA 2008).1 The objective of WMOST is to serve as a public-domain, efficient, and user-friendly tool for local water resources managers and planners to screen a widerange of potential water resources management options across their watershed or jurisdiction for costeffectiveness as well as environmental and economic sustainability (Zoltay et al 2010). Examples of options that could be evaluated with the tool include projects related to stormwater, water supply, wastewater and water-related resources such as Low-Impact Development (LID) and land conservation. The tool is intended to aid in evaluating the environmental and economic costs, benefits, trade-offs and co-benefits of various management options. In addition, the tool is intended to facilitate the evaluation of low impact development (LID) and green infrastructure as alternative or complementary management options in projects proposed for State Revolving Funds (SRF). WMOST is a screening model that is spatially lumped with a daily or monthly time step. The model considers water flows but does not yet consider water quality. The optimization of management options is solved using linear programming. The target user group for WMOST consists of local water resources managers, including municipal water works superintendents and their consultants. This document includes a user guide and presentation of two case studies as examples of how to apply WMOST. Theoretical documentation is provided in a separate report (EPA/600/R-13/151). "

Specific Policy or Decision Context Cited

None identified

Meeting water demands for agriculture and domestic purposes.

WaSSI can be used to project the regional effects of forest land cover change, climate change, and water withdrawals on river flows, water supply stress, and ecosystem productivity (i.e., carbon sequestration).WaSSI can be used to evaluate trade-offs among management strategies that influence multiple ecosystem services

None identified

Future rock lobster fisheries management

None identified

None reported

None identified

N/A

Not applicable

Biophysical Context

Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes

Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential.

No additional description provided

No additional descriptions provided

Conterminous US

Not applicable

barrier reef and fringing reef in nearshore coastal marine system

Prairie pothole region of north-central Iowa

No additional description provided

Prairie Pothole Region of Iowa

Aquatic systems

Conservation Reserve Program lands left to go fallow

N/A

Urban residential catchment area, Espoo, Finland

None

EM Scenario Drivers

No scenarios presented

Base case; increase pervious area tree cover to 40%; increase impervious area tree cover to 40%; double impervious area to 60%; halve pervious area tree cover to 6%; double pervious area tree cover to 24% and increase pervious area tree cover to 20%. ?

No scenarios presented

No scenarios presented ?

No scenarios presented

Reef type, Sea level increase, storm conditions, seagrass conditions, coral conditions, vegetation types and conditions

No scenarios presented

Not applicable

N/A

N?A

No scenarios presented

None

EM Relationship to Other EMs or Applications

EM ID

EM-83

EM-86

EM-109

EM-428

EM-439

EM-466

EM-542

EM-649

EM-655

EM-705

EM-820

EM-841

EM-845

EM-875

EM-971

EM-1011

Method Only, Application of Method or Model Run

Method + Application

Method + Application (multiple runs exist) View EM Runs

Method + Application

Method Only

Method + Application (multiple runs exist) View EM Runs

Method + Application

Method Only

Method + Application

Method Only

New or Pre-existing EM?

New or revised model

Application of existing model

Application of existing model ?

New or revised model

Application of existing model ?

New or revised model

Application of existing model

New or revised model

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

EM ID

EM-83

EM-86

EM-109

EM-428

EM-439

EM-466

EM-542

EM-649

EM-655

EM-705

EM-820

EM-841

EM-845

EM-875

EM-971

EM-1011

Document ID for related EM

Doc-260

Doc-271 ?

Doc-198

None

Doc-343 | Doc-344

None

Doc-372

None

Doc-372 | Doc-373

None

Doc-405

None

Doc-452

None

EM ID for related EM

EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82

EM-85 | EM-87 | EM-88

EM-137

None

EM-467 | EM-469 | EM-480 | EM-485

None

EM-652 | EM-651 | EM-650 | EM-648

None

EM-632 | EM-700 | EM-701 | EM-702 | EM-703 | EM-704

EM-821

EM-831 | EM-838 | EM-839 | EM-842 | EM-843 | EM-844 | EM-845 | EM-846 | EM-847

EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-843 | EM-844 | EM-846 | EM-847

None

EM-968

None

EM Modeling Approach

EM Relationship to Time

EM ID

EM-83

EM-86

EM-109

EM-428

EM-439

EM-466

EM-542

EM-649

EM-655

EM-705

EM-820

EM-841

EM-845

EM-875

EM-971

EM-1011

EM Temporal Extent

Not reported

2000

2006 - 2012

1961-2009

Not applicable

2005-2013

2002-2007

2000-2006

1987-2007

Not applicable

2008

2015

2005-2006

Not applicable

EM Time Dependence

time-stationary

time-dependent

time-stationary

time-dependent

time-stationary

time-dependent

time-stationary

time-dependent

EM Time Reference (Future/Past)

Not applicable

both

Not applicable

future time

Not applicable

past time

Not applicable ?

EM Time Continuity

Not applicable

discrete

Not applicable

discrete

Not applicable

other or unclear (comment)

discrete

EM Temporal Grain Size Value

Not applicable

EM Temporal Grain Size Unit

Not applicable

Hour

Not applicable

Month

Year

Second

Not applicable

Month

EM Spatial Extent

EM ID

EM-83

EM-86

EM-109

EM-428

EM-439

EM-466

EM-542

EM-649

EM-655

EM-705

EM-820

EM-841

EM-845

EM-875

EM-971

EM-1011

Bounding Type

Physiographic or Ecological

Geopolitical

Watershed/Catchment/HUC

Not applicable

Geopolitical

Multiple unrelated locations (e.g., meta-analysis)

Watershed/Catchment/HUC

Multiple unrelated locations (e.g., meta-analysis)

Not applicable

Physiographic or ecological

Geopolitical

Watershed/Catchment/HUC

Not applicable

Spatial Extent Name

Central French Alps

South Africa

Dead Run Catchement, Baltimore, MD

Guanica Bay watershed

All 8-digit hydrologic unit codes (HUC-8) in the conterminous USA

Not applicable

Coast of Belize

CREP (Conservation Reserve Enhancement Program) wetland sites

Yahara Watershed, Wisconsin

CREP (Conservation Reserve Enhancement Program

Not applicable

Piedmont Ecoregion

Hudson River Park

Vallikallio

Not applicable

Spatial Extent Area (Magnitude)

10-100 km^2

>1,000,000 km^2

10-100 km^2

1000-10,000 km^2.

>1,000,000 km^2

Not applicable

100-1000 km^2

1-10 km^2

1000-10,000 km^2.

10,000-100,000 km^2

Not applicable

100,000-1,000,000 km^2

10-100 ha

1-10 km^2

Not applicable

Spatial Distribution of Computations

EM ID

EM-83

EM-86

EM-109

EM-428

EM-439

EM-466

EM-542

EM-649

EM-655

EM-705

EM-820

EM-841

EM-845

EM-875

EM-971

EM-1011

EM Spatial Distribution

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)

Not applicable

spatially lumped (in all cases)

Spatial Grain Type

area, for pixel or radial feature

other (specify), for irregular (e.g., stream reach, lake basin)

area, for pixel or radial feature

other (specify), for irregular (e.g., stream reach, lake basin)

Not applicable

length, for linear feature (e.g., stream mile)

other (specify), for irregular (e.g., stream reach, lake basin)

area, for pixel or radial feature

other (specify), for irregular (e.g., stream reach, lake basin)

Not applicable

Spatial Grain Size

20 m x 20 m

Distributed across catchments with average size of 65,000 ha

irregular topographically delineated similar units

30 m x 30 m

Computations are at the 8-digit HUC scale. MostHUC-8 watersheds are within a range of 800-8000 km^2 (500-5000 mi^2) in size.

Not applicable

1 meter

multiple, individual, irregular shaped sites

30m x 30m

multiple, individual, irregular sites

Not applicable

EM Structure and Computation Approach

EM ID

EM-83

EM-86

EM-109

EM-428

EM-439

EM-466

EM-542

EM-649

EM-655

EM-705

EM-820

EM-841

EM-845

EM-875

EM-971

EM-1011

EM Computational Approach

Analytic

Numeric

Analytic

Numeric

Analytic

Numeric

Analytic

Numeric

EM Determinism

deterministic

stochastic

deterministic

Statistical Estimation of EM

Conventional (frequentist) statistics

None

Conventional (frequentist) statistics

None

None

Bayesian statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

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-83

EM-86

EM-109

EM-428

EM-439

EM-466

EM-542

EM-649

EM-655

EM-705

EM-820

EM-841

EM-845

EM-875

EM-971

EM-1011

Model Calibration Reported?

Yes

Not applicable

Unclear

Not applicable

Yes

Not applicable

Model Goodness of Fit Reported?

Yes

Not applicable

Goodness of Fit (metric| value | unit)

None

Peak Weighted Nash-Sutcliffe Criteria | 0.85 | Not applicable
Base Flow Weighted Criteria | 0.60 | Not applicable
Balanced Peak and Base Flow Criteria | 0.72 | Not applicable

None

Model Operational Validation Reported?

Yes

Not applicable

No ?

Unclear

Not applicable

Yes

Not applicable

Model Uncertainty Analysis Reported?

Unclear

Not applicable

Unclear

Not applicable

Model Sensitivity Analysis Reported?

Not applicable

Yes

Unclear

Not applicable

Model Sensitivity Analysis Include Interactions?

Not applicable

Yes

Unclear

Not applicable

EM Locations, Environments, Ecology

Location of EM Application

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

EM-83

EM-86

EM-109

EM-428

EM-439

EM-466

EM-542

EM-649

EM-655

EM-705

EM-820

EM-841

EM-845

EM-875

EM-971

EM-1011

Europe
- France

Africa
- South Africa

North America
- United States
  - Maryland

North America
- U.S. Caribbean Territories

North America
- United States

None

North America
- Belize

North America
- United States
  - Iowa

North America
- United States
  - Wisconsin

North America
- United States
  - Iowa

North America
- United States

North America
- United States
  - North Carolina
  - Georgia
  - Alabama
  - Maryland
  - New Jersey
  - South Carolina
  - Pennsylvania

North America
- United States
  - North Carolina
  - Georgia
  - Alabama
  - Maryland
  - New Jersey
  - South Carolina
  - Pennsylvania

North America
- United States
  - New York

Europe
- Finland

None

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

EM-83

EM-86

EM-109

EM-428

EM-439

EM-466

EM-542

EM-649

EM-655

EM-705

EM-820

EM-841

EM-845

EM-875

EM-971

EM-1011

None

Tropical Atlantic
- West Tropical Atlantic
  - Tropical Northwestern Atlantic
    - Western Caribbean

None

Centroid Lat/Long (Decimal Degree)

EM ID

EM-83

EM-86

EM-109

EM-428

EM-439

EM-466

EM-542

EM-649

EM-655

EM-705

EM-820

EM-841

EM-845

EM-875

EM-971

EM-1011

Centroid Latitude

45.05

-30

39.31

17.96

39.83

Not applicable

18.63

42.62

43.1

42.62

Not applicable

36.23

40.73

60.23

Not applicable

Centroid Longitude

6.4

-76.74

-67.02

-98.58

Not applicable

-88.22

-93.84

-89.4

-93.84

Not applicable

-81.9

-74.01

24.82

Not applicable

Centroid Datum

WGS84

Not applicable

WGS84

Not applicable

WGS84

Not applicable

Centroid Coordinates Status

Provided

Estimated

Provided

Estimated

Not applicable

Estimated

Provided

Estimated

Not applicable

Estimated

Not applicable

Environments and Scales Modeled

EM ID

EM-83

EM-86

EM-109

EM-428

EM-439

EM-466

EM-542

EM-649

EM-655

EM-705

EM-820

EM-841

EM-845

EM-875

EM-971

EM-1011

EM Environmental Sub-Class

Agroecosystems | Grasslands

Terrestrial Environment (sub-classes not fully specified)

Rivers and Streams | Ground Water | Created Greenspace

Lakes and Ponds ?

Forests | Grasslands | Scrubland/Shrubland | Tundra

Near Coastal Marine and Estuarine

Inland Wetlands | Agroecosystems | Grasslands

Inland Wetlands | Agroecosystems | Grasslands

Aquatic Environment (sub-classes not fully specified)

Grasslands

Created Greenspace

Terrestrial Environment (sub-classes not fully specified)

Specific Environment Type

Subalpine terraces, grasslands, and meadows.

Not reported

Urban watershed

13 LULC were used

Not applicable

coral reefs

Grassland buffering inland wetlands set in agricultural land

Mixed environment watershed of prairie converted to predominantly agriculture and urban landscape

Wetlands buffered by grassland within agroecosystems

Multiple

grasslands

Park

Urben residential stormwater catchment

watershed

EM Ecological Scale

Ecological scale is coarser than that of the Environmental Sub-class

Ecological scale is finer than that of the Environmental Sub-class

Ecological scale is coarser 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

Other or unclear (comment) ?

Ecological scale corresponds to the Environmental Sub-class

Ecological scale is finer than that of the Environmental Sub-class

Other or unclear (comment)

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-83

EM-86

EM-109

EM-428

EM-439

EM-466

EM-542

EM-649

EM-655

EM-705

EM-820

EM-841

EM-845

EM-875

EM-971

EM-1011

EM Organismal Scale

Community

Not applicable

Community

Not applicable

Species

Guild or Assemblage

Species

Not applicable

Guild or Assemblage

Species

Not applicable

Taxonomic level and name of organisms or groups identified

EM-83

EM-86

EM-109

EM-428

EM-439

EM-466

EM-542

EM-649

EM-655

EM-705

EM-820

EM-841

EM-845

EM-875

EM-971

EM-1011

None Available

Species
- Grasshopper Sparrow (Ammodramus savannarum)

None Available

Species
- Northern Pintail (Anas acuta)
- Mallard (Anas platyrhynchos)
- Blue-winged teal (Anas discors)
- Northern Shoveler (Anas clypeata)
- Gadwall (Anas strepera)

None Available

Species
- Brown headed cowbird

Species
- Redwinged blackbird

None Available

EnviroAtlas URL

EM-83

EM-86

EM-109

EM-428

EM-439

EM-466

EM-542

EM-649

EM-655

EM-705

EM-820

EM-841

EM-845

EM-875

EM-971

EM-1011

None Available

GAP Ecological Systems, National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Percent Impervious Area, Water supply from NID reservoirs (million gallons)

The Watershed Boundary Dataset (WBD)

Dasymetric Allocation of Population, The National Hydrography Dataset (NHD), Average Annual Precipitation, Wetlands and Streams, Agricultural water use (million gallons/day), The Watershed Boundary Dataset (WBD), Percent Impervious Area, Carbon Storage by Tree Biomass

Average Annual Precipitation, Carbon storage by tree biomass (kg/m2), Carbon Storage by Tree Biomass

GAP Ecological Systems, National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation

GAP Ecological Systems

Dasymetric Allocation of Population

Acres of Land Enrolled in the Conservation Reserve Program (CRP)

None Available

GAP Ecological Systems, U.S. EPA (Omernik) ecoregions

Enabling Conditions

Average Annual Precipitation, Total Annual Nitrogen Deposition

National Hydrography Dataset Plus (NHD PlusV2), Wetlands and Streams, Agricultural water use (million gallons/day)

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-83

EM-86

EM-109

EM-428

EM-439

EM-466

EM-542

EM-649

EM-655

EM-705

EM-820

EM-841

EM-845

EM-875

EM-971

EM-1011

[Ecosystem] Regulation & Maintenance
- Mediation of waste, toxics and other nuisances
  - Mediation by ecosystems
    - Filtration/sequestration/storage/accumulation by ecosystems

[Ecosystem] Regulation & Maintenance
- Mediation of flows
  - Mass flows
    - Mass stabilisation and control of erosion rates

[Ecosystem] Regulation & Maintenance
- Mediation of flows
  - Liquid flows
    - Flood protection
    - Hydrological cycle and water flow maintenance

[Ecosystem] Provisioning
- Materials
  - Water
    - Surface water for non-drinking purposes

[Ecosystem] Provisioning
- Nutrition
  - Water
    - Ground water for drinking

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Soil formation and composition
    - Decomposition and fixing processes
  - Atmospheric composition and climate regulation
    - Micro and regional climate regulation

[Ecosystem] Regulation & Maintenance
- Mediation of flows
  - Liquid flows
    - Flood protection
  - Mass flows
    - Mass stabilisation and control of erosion rates

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Lifecycle maintenance, habitat and gene pool protection
    - Maintaining nursery populations and habitats

[Ecosystem] Provisioning
- Nutrition
  - Biomass
    - Wild animals and their outputs

[Ecosystem] Provisioning
- Nutrition
  - Biomass
    - Wild animals and their outputs

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Lifecycle maintenance, habitat and gene pool protection
    - Maintaining nursery populations and habitats

[Ecosystem] Regulation & Maintenance
- 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

[Ecosystem] Cultural
- Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings]
  - Physical and experiential interactions
    - Experiential use of plants, animals and land-/seascapes in different environmental settings
  - Intellectual and representative interactions
    - Educational

[Non-ecosystem] Regulation & Maintenance by natural physical structures and processes
- Mediation of waste, toxics and other nuisances
  - By natural chemical and physical processes
    - Not applicable

None

<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-83

EM-86

EM-109

EM-428

EM-439

EM-466

EM-542

EM-649

EM-655

EM-705

EM-820

EM-841

EM-845

EM-875

EM-971

EM-1011

None

Rivers and Streams (111)
- Composite (8)
  - Regulation of extreme events
    - Flow Indicator
Urban/Suburban (27)
- Composite (8)
  - Regulation of extreme events
    - Flow Indicator

None

Rivers and Streams (111)
- Water (3)
  - Liquid water
    - Stock Indicator

None

Near Coastal Marine/Estuarine (113)
- Composite (8)
  - Regulation of extreme events
    - Extreme Event Indicator
    - Flow Indicator

Grasslands (23)
- Fauna (4)
  - Birds
    - Stock Indicator

None

Wetlands (12)
- Fauna (4)
  - Duck
    - Stock Indicator

Rivers and Streams (111)
- Fauna (4)
  - Fish
    - Quality Indicator
Lakes and Ponds (112)
- Fauna (4)
  - Fish
    - Quality Indicator

Grasslands (23)
- Fauna (4)
  - Birds
    - Stock Indicator

Grasslands (23)
- Fauna (4)
  - Birds
    - Stock Indicator

Forests (21)
- Flora (5)
  - Vegetation
    - Site Indicator

Urban/Surburban (27)
- Composite (8)
  - Presence of environmental class/subclass
    - Flow Indicator

None

US EPA

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:

Comment:

Comment:

Comment:

Comment:

Comment:

Comment:

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

Comment:

EM Modeling Approach

Comment:

Comment:

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)

Comment:

Comment:

Comment:

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)