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-51
EM-68
EM-449
EM-493
EM-541
EM-628
EM-630
EM-686
EM-702
EM-706
EM-709

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-51	EM-68	EM-449	EM-493	EM-541	EM-628	EM-630	EM-686	EM-702	EM-706	EM-709
EM Short Name em.detail.shortNameHelp ?	EnviroAtlas-Nat. filtration-water	Fodder crude protein content, Central French Alps	Decrease in erosion (shoreline), St. Croix, USVI	EnviroAtlas-Carbon sequestered by trees	InVEST fisheries, lobster, South Africa	SolVES, Bridger-Teton NF, WY	WaterWorld v2, Santa Basin, Peru	Estuary recreational use, Cape Cod, MA	Northern Shoveler recruits, CREP wetlands, IA, USA	WESP Method	Pollinators on landfill sites, United Kingdom
EM Full Name em.detail.fullNameHelp ?	US EPA EnviroAtlas - Natural filtration (of water by tree cover); Example is shown for Durham NC and vicinity, USA	Fodder crude protein content, Central French Alps	Decrease in erosion (shoreline) by reef, St. Croix, USVI	US EPA EnviroAtlas - Total carbon sequestered by tree cover; Example is shown for Durham NC and vicinity, USA	Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa	SolVES, Social Values for Ecosystem Services, Bridger-Teton National Forest, WY	WaterWorld v2, Santa Basin, Peru	Estuary recreational use, Cape Cod, MA	Northern Shoveler duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	Method for the Wetland Ecosystem Services Protocol (WESP)	Pollinating insects on landfill sites, East Midlands, United Kingdon
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	US EPA \| EnviroAtlas \| i-Tree ? Comment: EnviroAtlas uses an application of the i-Tree Hydro model.	EU Biodiversity Action 5	US EPA	US EPA \| EnviroAtlas \| i-Tree	InVEST	None	None	US EPA	None	None	None
EM Source Document ID	223	260	335	223 ? Comment: Additional source: I-tree Eco (doc# 345).	349 ? Comment: Supplemented with the InVEST Users Guide fisheries.	369	368	387	372 ? Comment: Document 373 is a secondary source for this EM.	390	389
Document Author em.detail.documentAuthorHelp ?	US EPA Office of Research and Development - National Exposure Research Laboratory	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Yee, S. H., Dittmar, J. A., and L. M. Oliver	US EPA Office of Research and Development - National Exposure Research Laboratory	Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby	Sherrouse, B.C., Semmens, D.J., and J.M. Clement	Van Soesbergen, A. and M. Mulligan	Mulvaney, K K., Atkinson, S.F., Merrill, N.H., Twichell, J.H., and M.J. Mazzotta	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	Adamus, P. R.	Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin
Document Year em.detail.documentYearHelp ?	2013	2011	2014	2013	2018	2014	2018	2019	2010	2016	2013
Document Title em.detail.sourceIdHelp ?	EnviroAtlas - Featured Community	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	EnviroAtlas - Featured Community	Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals	An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming	Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru	Quantifying Recreational Use of an Estuary: A case study of three bays, Cape Cod, USA	Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt	Manual for the Wetland Ecosystem Services Protocol (WESP) v. 1.3.	Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects
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	Peer reviewed and published	Peer reviewed but unpublished (explain in Comment)	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published on US EPA EnviroAtlas website	Published journal manuscript	Published journal manuscript	Published on US EPA EnviroAtlas website	Published journal manuscript	Published journal manuscript	Published journal manuscript	Draft manuscript-work progressing	Published report	Published report	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-51	EM-68	EM-449	EM-493	EM-541	EM-628	EM-630	EM-686	EM-702	EM-706	EM-709
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	https://www.epa.gov/enviroatlas	Not applicable	Not applicable	https://www.epa.gov/enviroatlas	https://www.naturalcapitalproject.org/invest/	Not applicable	www.policysupport.org/waterworld	Not applicable	Not applicable	http://people.oregonstate.edu/~adamusp/WESP/ ? Comment: This is an Excel spreadsheet calculator	Not applicable
Contact Name em.detail.contactNameHelp ?	EnviroAtlas Team	Sandra Lavorel	Susan H. Yee	EnviroAtlas Team	Michelle Ward	Benson Sherrouse	Arnout van Soesbergen	Mulvaney, Kate	David Otis	Paul R. Adamus	Sam Tarrant
Contact Address	Not reported	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	Not reported	ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia	USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA	Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK	US EPA, ORD, NHEERL, Atlantic Ecology Division, Narragansett, RI	U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University	6028 NW Burgundy Dr. Corvallis, OR 97330	RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K.
Contact Email	enviroatlas@epa.gov	sandra.lavorel@ujf-grenoble.fr	yee.susan@epa.gov	enviroatlas@epa.gov	m.ward@uq.edu.au	bcsherrouse@usgs.gov	arnout.van_soesbergen@kcl.ac.uk	Mulvaney.Kate@epa.gov	dotis@iastate.edu	adamus7@comcast.net	sam.tarrant@rspb.org.uk

EM Description

EM ID em.detail.idHelp ?	EM-51	EM-68	EM-449	EM-493	EM-541	EM-628	EM-630	EM-686	EM-702	EM-706	EM-709
Summary Description em.detail.summaryDescriptionHelp ?	The Natural Filtration model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. METADATA ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina... runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA DESCRIPTION: "The i-Tree Hydro model estimates the effects of tree and impervious cover on hourly stream flow values for a watershed (Wang et al 2008). i-Tree Hydro also estimates changes in water quality using hourly runoff estimates and mean and median national event mean concentration (EMC) values. The model was calibrated using hourly stream flow data to yield the best fit between model and measured stream flow results… After calibration, the model was run a number of times under various conditions to see how the stream flow would respond given varying tree and impervious cover in the watershed… The term event mean concentration (EMC) is a statistical parameter used to represent the flow-proportional average concentration of a given parameter during a storm event. EMC data is used for estimating pollutant loading into watersheds. The response outputs were calculated as kg of pollutant per square meter of land area for pollutants. These per square meter values were multiplied by the square meters of land area in the block group to estimate the effects at the block group level." METADATA DESCRIPTION PARAPHRASED: Changes in water quality were estimated for the following pollutants (entered as separate runs); total suspended solids (TSS), total phosphorus, soluble phosphorus, nitrites and nitrates, total Kjeldahl nitrogen (TKN), biochemical oxygen demand (BOD5), chemical oxygen demand (COD5), and copper. "Reduction in annual runoff (census block group)" variable data was derived from the EnviroAtlas water recharge coverage which used the i-Tree Hydro model.	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., fodder crude protein content), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in fodder crude protein content 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…Fodder crude protein for each pixel was calculated and mapped using model estimates...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 fodder protein content. 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."	ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef."	The Total carbon sequestered by tree cover model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. DATA FACT SHEET: "This EnviroAtlas community map estimates the total metric tons (mt) of carbon that are removed annually from the atmosphere and sequestered in the above-ground biomass of trees in each census block group. The data for this map were derived from a high-resolution tree cover map developed by EPA. Within each census block group derived from U.S. Census data, the total amount of tree cover (m2) was determined using this remotely-sensed land cover data. The USDA Forest Service i-Tree model was used to estimate the annual carbon sequestration rate from state-based rates of kgC/m2 of tree cover/year. The state rates vary based on length of growing season and range from 0.168 kgC/m2 of tree cover/year (Alaska) to 0.581 kgC/m2 of tree cover/year (Hawaii). The national average rate is 0.306 kgC/m2 of tree cover/year. These national and state values are based on field data collected and analyzed in several cities by the U.S. Forest Service. These values were converted to metric tons of carbon removed and sequestered per year by census block group."	AUTHOR'S DESCRIPTION: "Here we develop a method for assessing future scenarios of environmental management change that improve coastal ecosystem services and thereby, support the success of the SDGs. We illustrate application of the method using a case study of South Africa’s West Coast Rock Lobster fishery within the Table Mountain National Park (TMNP) Marine Protected Area...We calculated the retrospective and current value of the West Coast Rock Lobster fishery using published and unpublished data from various sources and combined the market worth of landed lobster from recreational fishers, small-scale fisheries (SSF), large-scale fisheries (LSF) and poachers. Then using the InVEST tool, we combined data to build scenarios that describe possible futures for the West Coast Rock Lobster fishery (see Table 1). The first scenario, entitled ‘Business as Usual’ (BAU), takes the current situation and most up-to-date data to model the future if harvest continues at the existing rate. The second scenario is entitled ‘Redirect the Poachers’ (RP), which attempts to model implementation of strict management, whereby poaching is minimised from the Marine Protected Area and other economic and nutritional sources are made available through government initiatives. The third scenario, entitled ‘Large Scale Cutbacks’ (LSC), excludes large-scale fisheries from harvesting West Coast Rock Lobster within the TMNP Marine Protected Area."	[ABSTRACT: " "Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders.With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems.	ABSTRACT: "Water resources in the Santa basin in the Peruvian Andes are increasingly under pressure from climate change and population increases. Impacts of temperature-driven glacier retreat on stream flow are better studied than those from precipitation changes, yet present and future water resources are mostly dependent on precipitation which is more difficult to predict with climate models. This study combines a broad range of projections from climate models with a hydrological model (WaterWorld), showing a general trend towards an increase in water availability due to precipitation increases over the basin. However, high uncertainties in these projections necessitate the need for basin-wide policies aimed at increased adaptability." AUTHOR'S DESCRIPTION: "WaterWorld is a fully distributed, process-based hydrological model that utilises remotely sensed and globally available datasets to support hydrological analysis and decision-making at national and local scales globally, with a particular focus on un-gauged and/or data-poor environments, which makes it highly suited to this study. The model (version 2) currently runs on either 10 degree tiles, large river basins or countries at 1-km2 resolution or 1 degree tiles at 1-ha resolution utilising different datasets. It simulates a hydrological baseline as a mean for the period 1950-2000 and can be used to calculate the hydrological impact of scenarios of climate change, land use change, land management options, impacts of extractives (oil & gas and mining) and impacts of changes in population and demography as well as combinations of these. The model is ‘self parameterising’ (Mulligan, 2013a) in the sense that all data required for model application anywhere in the world is provided with the model, removing a key barrier to model application. However, if users have better data than those provided, it is possible to upload these to WaterWorld as GIS files and use them instead. Results can be viewed visually within the web browser or downloaded as GIS maps. The model’s equations and processes are described in more detail in Mulligan and Burke (2005) and Mulligan (2013b). The model parameters are not routinely calibrated to observed flows as it is designed for hydrological scenario analysis in which the physical basis of its parameters must be retained and the model is also often used in un-gauged basins. Calibration is inappropriate under these circumstances (Sivapalan et al., 2003). The freely available nature of the model means that anyone can apply it and replicate the results shown here. WaterWorld’s (V2) snow and ice module is capable of simulating the processes of melt water production, snow fall and snow pack, making this version highly suited to the current application. The model component is based on a full energy-balance for snow accumulation and melting based on Walter et al., (2005) with input data provided globally by the SimTerra database (Mulligan, 2011) upon which the model r	[ABSTRACT: "Estimates of the types and number of recreational users visiting an estuary are critical data for quantifying the value of recreation and how that value might change with variations in water quality or other management decisions. However, estimates of recreational use are minimal and conventional intercept surveys methods are often infeasible for widespread application to estuaries. Therefore, a practical observational sampling approach was developed to quantify the recreational use of an estuary without the use of surveys. Designed to be simple and fast to allow for replication, the methods involved the use of periodic instantaneous car counts multiplied by extrapolation factors derived from all-day counts. This simple sampling approach can be used to estimate visitation to diverse types of access points on an estuary in a single day as well as across multiple days. Evaluation of this method showed that when periodic counts were taken within a preferred time window (from 11am-4:30pm), the estimates were within 44 percent of actual daily visitation. These methods were applied to the Three Bays estuary system on Cape Cod, USA. The estimated combined use across all its public access sites is similar to the use at a mid-sized coastal beach, demonstrating the value of estuarine systems. Further, this study is the first to quantify the variety and magnitude of recreational uses at several different types of access points throughout the estuary using observational methods. This model focused on the various use by access point type (beaches, landings and way to water, boat use). This work can be transferred to the many small coastal access points used for recreation across New England and beyond." ]	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 = 2Rn 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 = HZB/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).	Author Description: " The Wetland Ecosystem Services Protocol (WESP) is a standardized template for creating regionalized methods which then can be used to rapid assess ecosystem services (functions and values) of all wetland types throughout a focal region. To date, regionalized versions of WESP have been developed (or are ongoing) for government agencies or NGOs in Oregon, Alaska, Alberta, New Brunswick, and Nova Scotia. WESP also may be used directly in its current condition to assess these services at the scale of an individual wetland, but without providing a regional context for interpreting that information. Nonetheless, WESP takes into account many landscape factors, especially as they relate to the potential or actual benefits of a wetland’s functions. A WESP assessment requires completing a single three-part data form, taking about 1-3 hours. Responses to questions on that form are based on review of aerial imagery and observations during a single site visit; GIS is not required. After data are entered in an Excel spreadsheet, the spreadsheet uses science-based logic models to automatically generate scores intended to reflect a wetland’s ability to support the following functions: Water Storage and Delay, Stream Flow Support, Water Cooling, Sediment Retention and Stabilization, Phosphorus Retention, Nitrate Removal and Retention, Carbon Sequestration, Organic Nutrient Export, Aquatic Invertebrate Habitat, Anadromous Fish Habitat, Non-anadromous Fish Habitat, Amphibian & Reptile Habitat, Waterbird Feeding Habitat, Waterbird Nesting Habitat, Songbird, Raptor and Mammal Habitat, Pollinator Habitat, and Native Plant Habitat. For all but two of these functions, scores are given for both components of an ecosystem service: function and benefit. In addition, wetland Ecological Condition (Integrity), Public Use and Recognition, Wetland Sensitivity, and Stressors are scored. Scores generated by WESP may be used to (a) estimate a wetland’s relative ecological condition, stress, and sensitivity, (b) compare relative levels of ecosystem services among different wetland types, or (c) compare those in a single wetland before and after restoration, enhancement, or loss."]	ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level…In the first year of study, plants in flower and flower visitors were surveyed using the same transects as for the floral resources surveys. The transect was left undisturbed for 20 minutes following the initial plant survey to allow the flower visitors to return. Each transect was surveyed at a rate of approximately 3m/minute for 30 minutes. All insects observed to touch the sexual parts of flowers were either captured using a butterfly net and transferred into individually labeled specimen jars, or directly captured into the jars. After the survey was completed, those insects that could be identified in the field were recorded and released. The flower-visitor surveys were conducted in the morning, within 1 hour of midday, and in the afternoon to sample those insects active at different times. Insects that could not be identified in the field were collected as voucher specimens for later identification. Identifications were verified using reference collections and by taxon specialists. Relatively low capture rates in the first year led to methods being altered in the second year when surveying followed a spiral pattern from a randomly determined point on the sites, at a standard pace of 10 m/minute for 30 minutes, following Nielsen and Bascompte (2007) and Kalikhman (2007). Given a 2-m wide transect, an area of approximately 600m2 was sampled in each
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	None identified	None identified	Future rock lobster fisheries management	None	None identified	None identified	None identified	None identified	None identified
Biophysical Context	No additional description provided	Elevation ranges from 1552 to 2442 m, on predominantely south-facing slopes	No additional description provided	No additional description provided	No additional description provided	Rocky mountain conifer forests	Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal.	None identified	Prairie Pothole Region of Iowa	None	No additional description provided
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	No scenarios presented	Fisheries exploitation; fishing vulnerability (of age classes)	N/A	Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100	N/A	No scenarios presented	N/A	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-51	EM-68	EM-449	EM-493	EM-541	EM-628	EM-630	EM-686	EM-702	EM-706	EM-709
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application (multiple runs exist)	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method Only	Method + Application (multiple runs exist) View EM Runs
New or Pre-existing EM? em.detail.newOrExistHelp ?	Application of existing model	New or revised model	Application of existing model	Application of existing model	Application of existing model	New or revised model	Application of existing model	New or revised model	New or revised model	New or revised model	New or revised model

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

EM ID em.detail.idHelp ?	EM-51	EM-68	EM-449	EM-493	EM-541	EM-628	EM-630	EM-686	EM-702	EM-706	EM-709
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-198	Doc-260 \| Doc-269	Doc-335	Doc-345	None	None	None	None	Doc-372 \| Doc-373	None	Doc-389
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-137 \| EM-142	EM-65 \| EM-66 \| EM-69 \| EM-70 \| EM-71 \| EM-79 \| EM-80 \| EM-81 \| EM-82 \| EM-83	EM-447 \| EM-448	None	None	EM-629 \| EM-626	None	EM-682 \| EM-684 \| EM-685	EM-705 \| EM-704 \| EM-703 \| EM-701 \| EM-700 \| EM-632	EM-718	EM-697

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-51	EM-68	EM-449	EM-493	EM-541	EM-628	EM-630	EM-686	EM-702	EM-706	EM-709
EM Temporal Extent em.detail.tempExtentHelp ?	1999-2010	2007-2009	2006-2007, 2010	2010-2013	1986-2115	2004-2008	1950-2071	Summer 2017	1987-2007	Not applicable	2007-2008
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary ? Comment: The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, operated on an hourly timestep. The final annual flow parameter however is time stationary.	time-stationary	time-stationary	time-stationary	time-dependent	time-stationary	time-dependent	time-dependent	time-stationary	time-stationary	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	future time	Not applicable	both	past time	Not applicable	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	discrete	Not applicable	discrete	discrete	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	1	Not applicable	1	1	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Year	Not applicable	Month	Day	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-51	EM-68	EM-449	EM-493	EM-541	EM-628	EM-630	EM-686	EM-702	EM-706	EM-709
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or Ecological	Physiographic or ecological	Geopolitical	Geopolitical	Geopolitical	Watershed/Catchment/HUC	Physiographic or ecological	Multiple unrelated locations (e.g., meta-analysis)	Not applicable	Multiple unrelated locations (e.g., meta-analysis)
Spatial Extent Name em.detail.extentNameHelp ?	Durham, NC and vicinity	Central French Alps	Coastal zone surrounding St. Croix	Durham NC and vicinity	Table Mountain National Park Marine Protected Area	National Park	Santa Basin	Three Bays, Cape Cod	CREP (Conservation Reserve Enhancement Program	Not applicable	East Midlands
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	100-1000 km^2	10-100 km^2	100-1000 km^2	100-1000 km^2	100-1000 km^2	1000-10,000 km^2.	10,000-100,000 km^2	1000-10,000 km^2.	10,000-100,000 km^2	Not applicable	1000-10,000 km^2.

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-51	EM-68	EM-449	EM-493	EM-541	EM-628	EM-630	EM-686	EM-702	EM-706	EM-709
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: Census block groups	spatially lumped (in all cases)	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)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	area, for pixel or radial feature	area, for pixel or radial feature	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)
Spatial Grain Size em.detail.spGrainSizeHelp ?	irregular	20 m x 20 m	10 m x 10 m	irregular	Not applicable	30m2	1 km2	beach length	multiple, individual, irregular sites	not reported	multiple unrelated locations

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-51	EM-68	EM-449	EM-493	EM-541	EM-628	EM-630	EM-686	EM-702	EM-706	EM-709
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic ? Comment: The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, was numeric. The final parameter however did not require iteration.	Analytic	Analytic	Numeric	Numeric	Numeric	*	Numeric	Analytic	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	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	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-51	EM-68	EM-449	EM-493	EM-541	EM-628	EM-630	EM-686	EM-702	EM-706	EM-709
Model Calibration Reported? em.detail.calibrationHelp ?	Unclear	No	Yes	No	No	No	No	Yes	Unclear	Not applicable	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Yes	No	No	No	Yes	No	No	No	Not applicable	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	Adjusted R \| 62.4 \|	None	None	None	Z score \| -5.045to -18.302 \| NA R value \| 0.483 - 0.762 \| NA	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	Unclear	Yes	Yes	No	Yes ? Comment: A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data.	No	Yes	No	No	No	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	Unclear	No	No	No	No	No	No	No	No	Not applicable	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	Unclear	No	No	No	No	No	No	No	No	Not applicable	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	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-51	EM-68	EM-449	EM-493	EM-541	EM-628	EM-630	EM-686	EM-702	EM-706	EM-709
North America United States North Carolina	Europe France	None	North America United States North Carolina	None	North America United States Wyoming Colorado	None	None	North America United States Iowa	None	Europe United Kingdom

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

EM-51	EM-68	EM-449	EM-493	EM-541	EM-628	EM-630	EM-686	EM-702	EM-706	EM-709
None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	Temperate South Africa South Africa Benguela Namaqua	None	None	Temperate North Atlantic Northwest Atlantic Cold Temperate Northwest Atlantic Gulf of Maine/Bay of Fundy	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-51	EM-68	EM-449	EM-493	EM-541	EM-628	EM-630	EM-686	EM-702	EM-706	EM-709
Centroid Latitude em.detail.ddLatHelp ?	35.99	45.05	17.73	35.99	-34.18	43.93	-9.05	41.62	42.62	Not applicable	52.22
Centroid Longitude em.detail.ddLongHelp ?	-78.96	6.4	-64.77	-78.96	18.35	110.24	-77.81	-70.42	-93.84	Not applicable	-0.91
Centroid Datum em.detail.datumHelp ?	None provided	WGS84	WGS84	None provided	WGS84	WGS84	WGS84	WGS84	WGS84	Not applicable	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Provided	Estimated	Estimated	Provided	Estimated	Estimated	Estimated	Estimated	Not applicable	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-51	EM-68	EM-449	EM-493	EM-541	EM-628	EM-630	EM-686	EM-702	EM-706	EM-709
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Rivers and Streams \| Created Greenspace	Agroecosystems \| Grasslands	Near Coastal Marine and Estuarine	Created Greenspace \| Atmosphere	Near Coastal Marine and Estuarine	Forests	None	Near Coastal Marine and Estuarine	Inland Wetlands \| Agroecosystems \| Grasslands	Inland Wetlands	Created Greenspace \| Grasslands
Specific Environment Type em.detail.specificEnvTypeHelp ?	Urban areas including streams	Subalpine terraces, grasslands, and meadows	Coral reefs	Urban and vicinity	Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf	Montain forest	tropical, coastal to montane	Beaches	Wetlands buffered by grassland within agroecosystems	Wetlands	restored landfills and grasslands
EM Ecological Scale em.detail.ecoScaleHelp ?	Not applicable	Not applicable	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale 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	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-51	EM-68	EM-449	EM-493	EM-541	EM-628	EM-630	EM-686	EM-702	EM-706	EM-709
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Community	Not applicable	Not applicable	Individual or population, within a species	Not applicable	Not applicable	Not applicable	Individual or population, within a species	Not applicable	Individual or population, within a species

Taxonomic level and name of organisms or groups identified

EM-51	EM-68	EM-449	EM-493	EM-541	EM-628	EM-630	EM-686	EM-702	EM-706	EM-709
None Available	None Available	None Available	None Available	Species West Coast Rock Lobster (Jasus lalandii)	None Available	None Available	None Available	Species Northern Shoveler (Anas clypeata)	None Available	Genus Calliopum Lasioglossum Species Apis mellifera Bombus lapidarius Oedemera nobilis Episyrphus balteatus Bombus terrestris/lucorum Bombus pascuorum

EnviroAtlas URL

EM-51	EM-68	EM-449	EM-493	EM-541	EM-628	EM-630	EM-686	EM-702	EM-706	EM-709
None Available	GAP Ecological Systems, Carbon storage by tree biomass (kg/m2)	National Hydrography Dataset Plus (NHD PlusV2)	Carbon Storage by Tree Biomass	Big game hunting recreation demand	GAP Ecological Systems, Enabling Conditions	Average Annual Precipitation	None Available	Acres of Land Enrolled in the Conservation Reserve Program (CRP)	None Available	GAP Ecological Systems

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-51	EM-68	EM-449	EM-493	EM-541	EM-628	EM-630	EM-686	EM-702	EM-706	EM-709
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of freshwaters Mediation of waste, toxics and other nuisances Mediation by biota Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals	[Ecosystem] Provisioning Materials Biomass Materials from plants, algae and animals for agricultural use	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs Wild plants, algae and their outputs [Ecosystem] Cultural Spiritual, symbolic and other interactions with biota, ecosystems, and land-/seascapes [environmental settings] Spiritual and/or emblematic Symbolic Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Physical use of land-/seascapes in different environmental settings Intellectual and representative interactions Aesthetic Educational	None	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs [Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Physical use of land-/seascapes in different environmental settings	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[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 Maintaining nursery populations and habitats Water conditions Chemical condition of freshwaters Mediation of flows Liquid flows Flood protection Hydrological cycle and water flow maintenance Mediation of waste, toxics and other nuisances Mediation by biota 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 Pollination and seed dispersal

<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-51	EM-68	EM-449	EM-493	EM-541	EM-628	EM-630	EM-686	EM-702	EM-706	EM-709
Rivers and Streams (111) Water (3) Liquid water Quality Indicator Urban/Suburban (27) Composite (8) Presence of environmental class/subclass Quality Indicator	Agroecosystems (22) Flora (5) Fodder quality Quality Indicator	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Extreme Event Indicator	None	Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Stock Indicator	Forests (21) Composite (8) Scapes: views, sounds and scents of land, sea, sky or a combination Quality Indicator	None	Near Coastal Marine/Estuarine (113) Composite (8) Scapes: views, sounds and scents of land, sea, sky or a combination Site Indicator Quality Indicator Fauna (4) Fish Stock Indicator Clams Stock Indicator	Wetlands (12) Fauna (4) Duck Stock Indicator	Wetlands (12) Water (3) Liquid water Flow Indicator Quality Indicator Fauna (4) Wildlife Site Indicator Pollinators Quality Indicator Fish Stock Indicator Birds Site Indicator	None

EcoService Models Library (ESML)

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EM Locations, Environments, Ecology

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

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

Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

Taxonomic level and name of organisms or groups identified

EnviroAtlas URL

EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system

CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)

<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>

(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)