EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?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.
EM Identity and Description
EM ID
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EM-24 |
EM-59 ![]() |
EM-69 | EM-93 |
EM-102 ![]() |
EM-112 ![]() |
EM-137 | EM-193 | EM-315 | EM-376 | EM-439 |
EM-584 ![]() |
EM-667 ![]() |
EM-683 |
EM-777 ![]() |
EM-796 ![]() |
EM-993 |
EM Short Name
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i-Tree Eco: Carbon storage & sequestration, USA | EnviroAtlas-Air pollutant removal | Soil carbon content, Central French Alps | Stream nitrogen removal, Mississippi R. basin, USA | Fish species habitat value, Tampa Bay, FL, USA | InVEST nutrient retention, Hood Canal, WA, USA | i-Tree Hydro v4.0 | Cultural ecosystem services, Bilbao, Spain | ARIES open Space, Puget Sound Region, USA | MIMES: For Massachusetts Ocean (v1.0) | WaSSI, Conterminous USA | Nutrient Tracking Tool (NTT), north central Texas, USA | Alewife derived nutrients, Connecticut, USA | Estuary visitation, Cape Cod, MA | Bees and managed prairie plants and soil, MO, USA | Wildflower mix supporting bees, MI, USA | Velma- 6PPD-Q concentrations, Seattle, WA |
EM Full Name
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i-Tree Eco carbon storage and sequestration (trees), USA | US EPA EnviroAtlas - Pollutants (air) removed annually by tree cover; Example is shown for Durham NC and vicinity, USA | Soil carbon content, Central French Alps | Stream nitrogen removal, Upper Mississippi, Ohio and Missouri River sub-basins, USA | Fish species habitat value, Tampa Bay, FL, USA | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) nutrient retention, Hood Canal, WA, USA | i-Tree Hydro v4.0 (default data option) | Cultural ecosystem services, Bilbao, Spain | ARIES (Artificial Intelligence for Ecosystem Services) Open Space Proximity for Homeowners, Puget Sound Region, Washington, USA | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | Water Supply Stress Index, Conterminous USA | Nutrient Tracking Tool (NTT), Upper North Bosque River watershed, Texas, USA | Alewife derived nutrients in stream food web, Connecticut, USA | Value of recreational use of an estuary, Cape Cod, Massachusetts | Tallgrass prairie bee community affected by management effects on plant community and soil properties, Missouri, USA | Wildflower planting mix supporting bees in agricultural landscapes, MI, USA | VELMA: 6PPD-Quinone stormwater concentrations , Seattle, Washington |
EM Source or Collection
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i-Tree | USDA Forest Service |
US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Eco model. |
EU Biodiversity Action 5 | US EPA | US EPA | InVEST | i-Tree | USDA Forest Service |
None ?Comment:EU Mapping Studies |
ARIES | US EPA |
USDA Forest Service ?Comment: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. |
None | None | US EPA | None | None | US EPA |
EM Source Document ID
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195 | 223 | 260 | 52 | 187 | 205 | 198 | 191 | 302 | 316 | 341 | 354 | 384 | 387 | 398 | 400 | 465 |
Document Author
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Nowak, D. J., Greenfield, E. J., Hoehn, R. E. and Lapoint, E. | 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. | Hill, B. and Bolgrien, D. | Fulford, R., Yoskowitz, D., Russell, M., Dantin, D., and Rogers, J. | Toft, J. E., Burke, J. L., Carey, M. P., Kim, C. K., Marsik, M., Sutherland, D. A., Arkema, K. K., Guerry, A. D., Levin, P. S., Minello, T. J., Plummer, M., Ruckelshaus, M. H., and Townsend, H. M. | USDA Forest Service | Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Altman, I., R.Boumans, J. Roman, L. Kaufman | Peter Caldwell, Ge Sun, Steve McNulty, Jennifer Moore Myers, Erika Cohen, Robert Herring, Erik Martinez | Saleh, A., O. Gallego, E. Osei, H. Lal, C. Gross, S. McKinney, and H. Cover | Walters, A. W., R. T. Barnes, and D. M. Post | Mulvaney, K K., Atkinson, S.F., Merrill, N.H., Twichell, J.H., and M.J. Mazzotta | Buckles, B. J., and A. N. Harmon-Threatt | Williams, N.M., Ward, K.L., Pope, N., Isaacs, R., Wilson, J., May, E.A., Ellis, J., Daniels, J., Pence, A., Ullmann, K., and J. Peters | Halama JJ, McKane RB, Barnhart BL, Pettus PP, Brookes AF, Adams AK, Gockel CK, Djang KS, Phan V, Chokshi SM, Graham JJ, Tian Z, Peter KT and Kolodziej,EP |
Document Year
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2013 | 2013 | 2011 | 2011 | 2016 | 2013 | Not Reported | 2013 | 2014 | 2012 | 2013 | 2011 | 2009 | 2019 | 2019 | 2015 | 2024 |
Document Title
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Carbon storage and sequestration by trees in urban and community areas of the United States | EnviroAtlas - Featured Community | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Nitrogen removal by streams and rivers of the Upper Mississippi River basin | Habitat and recreational fishing opportunity in Tampa Bay: Linking ecological and ecosystem services to human beneficiaries | From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA | i-Tree Hydro User's Manual v. 4.0 | Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | WaSSI Ecosystem Services Model | Nutrient Tracking Tool - a user-friendly tool for calculating nutrient reductions for water quality trading | Anadromous alewives (Alosa pseudoharengus) contribute marine-derived nutrients to coastal stream food webs | Quantifying Recreational Use of an Estuary: A case study of three bays, Cape Cod, USA | Bee diversity in tallgrass prairies affected by management and its effects on above‐ and below‐ground resources | Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States | Watershed analysis of urban stormwater contaminant 6PPD-Quinone hotspots and stream concentrations using a process-based ecohydrological model |
Document Status
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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 and published | Peer reviewed and published | Documented, not peer reviewed | Not peer reviewed but is published (explain in Comment) | 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
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Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Webpage | Published journal manuscript | Published journal manuscript | Published report | 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. | Published journal manuscript | Published journal manuscript | Draft manuscript-work progressing | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-24 |
EM-59 ![]() |
EM-69 | EM-93 |
EM-102 ![]() |
EM-112 ![]() |
EM-137 | EM-193 | EM-315 | EM-376 | EM-439 |
EM-584 ![]() |
EM-667 ![]() |
EM-683 |
EM-777 ![]() |
EM-796 ![]() |
EM-993 |
Not applicable | https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | http://www.itreetools.org | Not applicable | http://aries.integratedmodelling.org/ | http://www.afordablefutures.com/orientation-to-what-we-do | http://www.wassiweb.sgcp.ncsu.edu/ | http://ntt.tiaer.tarleton.edu/welcomes/new?locale=en | Not applicable | Not applicable | Not applicable | Not applicable | Not reported | |
Contact Name
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David J. Nowak | EnviroAtlas Team | Sandra Lavorel | Brian Hill | Richard Fulford | J.E. Toft | Not applicable | Izaskun Casado-Arzuaga | Ken Bagstad | Irit Altman | Ge Sun | Ali Saleh | Annika W. Walters | Mulvaney, Kate | Alexandra N. Harmon‐Threatt | Neal Williams | Jonathan Halama |
Contact Address
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USDA Forest Service, Northern Research Station, Syracuse, NY 13210, USA | Not reported | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Mid-Continent Ecology Division NHEERL, ORD. USEPA 6201 Congdon Blvd. Duluth, MN 55804, USA | USEPA Gulf Ecology Division, Gulf Breeze, FL 32561 | The Natural Capital Project, Stanford University, 371 Serra Mall, Stanford, CA 94305-5020, USA | Not applicable | Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain | Geosciences and Environmental Change Science Center, US Geological Survey | Boston University, Portland, Maine | Eastern Forest Environmental Threat Assessment Center, Southern Research Station, USDA Forest Service, 920 Main Campus Dr. Venture II, Suite 300, Raleigh, NC 27606 | Texas Institute for Applied Environmental Research-Tarleton State University, Stephenville, TX 76401,USA | Dept. of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511, USA | US EPA, ORD, NHEERL, Atlantic Ecology Division, Narragansett, RI | Department of Entomology, University of Illinois, Urbana, IL, USA | Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616 | U.S. Environmental Protection Agency, Corvallis, OR |
Contact Email
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dnowak@fs.fed.us | enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | hill.brian@epa.gov | Fulford.Richard@epa.gov | jetoft@stanford.edu | Not applicable | izaskun.casado@ehu.es | kjbagstad@usgs.gov | iritaltman@bu.edu | gesun@fs.fed.us | saleh@tiaer.tarleton.edu | annika.walters@yale.edu | None reported | aht@illinois.edu | nmwilliams@ucdavis.edu | Halama.Jonathan@epa.gov |
EM ID
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EM-24 |
EM-59 ![]() |
EM-69 | EM-93 |
EM-102 ![]() |
EM-112 ![]() |
EM-137 | EM-193 | EM-315 | EM-376 | EM-439 |
EM-584 ![]() |
EM-667 ![]() |
EM-683 |
EM-777 ![]() |
EM-796 ![]() |
EM-993 |
Summary Description
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ABSTRACT: "Carbon storage and sequestration by urban trees in the United States was quantified to assess the magnitude and role of urban forests in relation to climate change. Urban tree field data from 28 cities and 6 states were used to determine the average carbon density per unit of tree cover. These data were applied to statewide urban tree cover measurements to determine total urban forest carbon storage and annual sequestration by state and nationally. Urban whole tree carbon storage densities average 7.69 kg C m^2 of tree cover and sequestration densities average 0.28 kg C m^2 of tree cover per year. Total tree carbon storage in U.S. urban areas (c. 2005) is estimated at 643 million tonnes ($50.5 billion value; 95% CI = 597 million and 690 million tonnes) and annual sequestration is estimated at 25.6 million tonnes ($2.0 billion value; 95% CI = 23.7 million to 27.4 million tonnes)." | The Air Pollutant Removal model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina. ... pollution removal ... 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: The maps, estimate and illustrate the variation in the amount of six airborne pollutants, carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter (PM10), and particulate matter (PM2.5), removed by trees. PM10 is for particulate matter greater than 2.5 microns and less than 10 microns. DATA FACT SHEET: "The data for this map are based on the land cover derived for each EnviroAtlas community and the pollution removal models in i-Tree, a toolkit developed by the USDA Forest Service. The land cover data were created from aerial photography through remote sensing methods; tree cover was then summarized as the percentage of each census block group. The i-Tree pollution removal module uses the tree cover data by block group, the closest hourly meteorological monitoring data for the community, and the closest pollution monitoring data... hourly estimates of pollution removal by trees were combined with atmospheric data to estimate hourly percent air quality improvement due to pollution removal for each pollutant." | 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, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in soil carbon 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…Soil carbon 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 soil carbon. 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 used stream chemistry and hydrogeomorphology data from 549 stream and 447 river sites to estimate NO3–N removal in the Upper Mississippi, Missouri, and Ohio Rivers. We used two N removal models to predict NO3–N input and removal. NO3–N input ranged from 0.01 to 338 kg/km*d in the Upper Mississippi River to 0.01–54 kg/ km*d in the Missouri River. Cumulative river network NO3–N input was 98700–101676 Mg/year in the Ohio River, 85,961–89,288 Mg/year in the Upper Mississippi River, and 59,463–61,541 Mg/year in the Missouri River. NO3–N output was highest in the Upper Mississippi River (0.01–329 kg/km*d ), followed by the Ohio and Missouri Rivers (0.01–236 kg/km*d ) sub-basins. Cumulative river network NO3–N output was 97,499 Mg/year for the Ohio River, 84,361 Mg/year for the Upper Mississippi River, and 59,200 Mg/year for the Missouri River. Proportional NO3–N removal (PNR) based on the two models ranged from 0.01 to 0.28. NO3–N removal was inversely correlated with stream order, and ranged from 0.01 to 8.57 kg/km*d in the Upper Mississippi River to 0.001–1.43 kg/km*d in the Missouri River. Cumulative river network NO3–N removal predicted by the two models was: Upper Mississippi River 4152 and 4152 Mg/year, Ohio River 3743 and 378 Mg/year, and Missouri River 2,277 and 197 Mg/year. PNR removal was negatively correlated with both stream order (r = −0.80–0.87) and the percent of the catchment in agriculture (r = −0.38–0.76)." | ABSTRACT: "Estimating value of estuarine habitat to human beneficiaries requires that we understand how habitat alteration impacts function through both production and delivery of ecosystem goods and services (EGS). Here we expand on the habitat valuation technique of Bell (1997) with an estimate of recreational angler willingness-to-pay combined with estimates of angler effort, fish population size, and fish and angler distribution. Results suggest species-specific fishery value is impacted by angler interest and stock status, as the most targeted fish (spotted seatrout) did not have the highest specific value (fish−1). Reduced population size and higher size at capture resulted in higher specific value for common snook. Habitat value estimated from recreational fishing value and fish-angler distributions supported an association between seagrass and habitat value, yet this relationship was also impacted by distance to access points. This analysis does not provide complete valuation of habitat as it considers only one service (fishing), but demonstrates a methodology to consider functional equivalency of all habitat features as a part of a habitat mosaic rather than in isolation, as well as how to consider both EGS production and delivery to humans (e.g., anglers) in any habitat valuation, which are critical for a transition to ecosystem management." | InVEST Nutrient Retention Model Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "We modelled discharge and total nitrogen for the 153 perennial sub-watersheds in Hood Canal based on spatial variation in hydrological factors, land and water use, and vegetation.To do this, we reparameterized a set of fresh water models available in the InVEST tool (Tallis and Polasky, 2009; Kareiva et al., 2011)" (2) "We used the InVEST Nutrient Retention model to quantify the total nitrogen load for each subwatershed. Inputs to the Nutrient Retention model include water yield, land use and land cover, and nutrient loading and filtration rates (Table 1; Conte et al., 2011; Tallis et al., 2011). The nutrient model quantifies natural and anthropogenic sources of total nitrogen within each subwatershed, allowing managers to identify subwatersheds potentially at risk of contributing excessive nitrogen loads given the predicted development and climate future." ( P. 4) | ABSTRACT: "i-Tree Hydro is the first urban hydrology model that is specifically designed to model vegetation effects and to be calibrated against measured stream flow data. It is designed to model the effects of changes in urban tree cover and impervious surfaces on hourly stream flows and water quality at the watershed level." AUTHOR'S DESCRIPTION: "The purpose of i-Tree Hydro is to simulate hourly changes in stream flow (and water quality) given changes in tree and impervious cover in the watershed. The following is an overview of the process: 1) Determine your watershed of analysis and stream gauge station. i-Tree Hydro works on a watershed basis with the watershed determined as the total drainage area upstream from a measured stream gauge. Stream gauge availability varies. 2) Download national digital elevation data. Once the area and location of the watershed are known, digital elevation data are downloaded from the USGS for an area that encompasses the entire watershed. ArcGIS software is then used to create a digital elevation map and to determine the exact boundary for the watershed upstream from the gauge station location. 3) Determine cover attributes of the watershed and gather other required data. i-Tree Canopy and other sources can be used to determine the tree cover, shrub cover, impervious surface and other cover types. Information about other aspects of the watershed such as proportion of evergreen trees and shrubs, leaf area index, and a variety of hydrologic parameters must be collected. 4) Get started with Hydro. Once these input data are ready, they are loaded into Hydro to begin analysis. 5) Calibrate the model. The Hydro model contains an auto-calibration routine that tries to find the best fit between the stream flow predicted by the model and the stream flow measured at the stream gauge station given the various inputs. The model can also be manually calibrated to improve the fit by changing the parameters as needed. 6) Model new scenarios: Once the model is properly calibrated, tree and impervious cover parameters can be changed to illustrate the impact on stream flow and water quality." | ABSTRACT "This paper presents a method to quantify cultural ecosystem services (ES) and their spatial distribution in the landscape based on ecological structure and social evaluation approaches. The method aims to provide quantified assessments of ES to support land use planning decisions. A GIS-based approach was used to estimate and map the provision of recreation and aesthetic services supplied by ecosystems in a peri-urban area located in the Basque Country, northern Spain. Data of two different public participation processes (frequency of visits to 25 different sites within the study area and aesthetic value of different landscape units) were used to validate the maps. Three maps were obtained as results: a map showing the provision of recreation services, an aesthetic value map and a map of the correspondences and differences between both services. The data obtained in the participation processes were found useful for the validation of the maps. A weak spatial correlation was found between aesthetic quality and recreation provision services, with an overlap of the highest values for both services only in 7.2 % of the area. A consultation with decision-makers indicated that the results were considered useful to identify areas that can be targeted for improvement of landscape and recreation management." | ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "For open space proximity, we mapped the relative value of open space, highways that impede walking access or reduce visual and soundscape quality, and housing locations, connected by a flow model simulating physical access to desirable spaces. We used reviews of the hedonic valuation literature (Bourassa et al. 2004, McConnell and Walls 2005) to inform model development, ranking the influence of different open space characteristics on property values to parameterize the source and sink models. The model includes a distance decay function that accounts for changes with distance in the value of open space. We then computed the ratio of actual to theoretical provision of open space to compare the values accruing to homeowners relative to those for the entire landscape." | AUTHORS DESCRIPTION: "MIMES uses a systems approach to model ecosystem dynamics across a spatially explicit environment. The modeling platform used by this work is a commercially available, object-based modeling and simulation software. This model, referred to as Massachusetts Ocean MIMES, was applied to a selected area of Massachusetts’ coastal waters and nearshore waters. The model explores the implications of management decisions on select marine resources and economic production related to a suite of marine based economic sectors. | 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." | ABSTRACT: "The Nutrient Tracking Tool (NTrT) is an enhanced version of the Nitrogen Trading Tool, a user-friendly Web-based computer program originally developed by the USDA. The NTrT estimates nutrient (nitrogen and phosphorus) and sediment losses from fields managed under a variety of cropping patterns and management practices through its user-friendly, Web-based linkage to the Agricultural Policy Environmental eXtender (APEX) model. It also accesses the USDA Natural Resources Conservation Service’s Web Soil Survey to utilize their geographic information system interface for field and operation identification and load soil information. The NTrT provides farmers, government officials, and other users with a fast and efficient method of estimating nitrogen and phosphorus credits for water quality trading, as well as other water quality, water quantity, and farm production impacts associated with conservation practices. The information obtained from the tool can help farmers determine the most cost-effective conservation practice alternatives for their individual operations and provide them with more advantageous options in a water quality credit trading program. An application of the NTrT to evaluate conservation practices on fields receiving dairy manure in a north central Texas watershed indicates that phosphorus-based application rates, filter strips, forest buffers, and complete manure export off the farm all result in reduced phosphorus losses from the fields on which those practices were implemented. When compared to a base¬line condition that entailed manure application at the nitrogen agronomic rate of receiving crops, the reductions in total phosphorus losses associated with these practices ranged from 15% (2P Rate scenario) to 76% (forest buffer scenario)." AUTHOR'S DESCRIPTION: "This paper provides a brief overview of the NTrT and presents results of verification and application of the tool on a selected field on a test field in the Upper North Bosque River (UNBR) watershed in Texas…simulations for the baseline and all five alternative scenarios were replicated for each of 90 specific soil types in Erath County, Texas…results reported and discussed in this report represent the averages of the output for all soil types." | ABSTRACT: "Diadromous fish are an important link between marine and freshwater food webs. Pacific salmon (Oncorhynchus spp.) strongly impact nutrient dynamics in inland waters and anadromous alewife (Alosa pseudoharengus) may play a similar ecological role along the Atlantic coast. The annual spawning migration of anadromous alewife contributes, on average, 1050 g of nitrogen and 120 g of phosphorus to Bride Brook, Connecticut, USA, through excretion and mortality each year... There was no significant effect of this nutrient influx on water chemistry, leaf decomposition, or periphyton accrual. Dam removal and fish ladder construction will allow anadromous alewife to regain access to historical freshwater spawning habitats, potentially impacting food web dynamics and nutrient cycling in coastal freshwater systems." | [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 work can be transferred to the many small coastal access points used for recreation across New England and beyond." ] | ABSTRACT: "1. Habitat management methods are crucial to maintaining habitats in the long term and ensuring vital resources are available for declining species. However, when management focuses on a single resource there is the potential to reduce or degrade other critical resources and negatively affect species of concern. Although both floral and nesting resources are critical to supporting bee populations, little consideration is given to the availability of nesting resources. Given known effects of management methods on soils, where the majority of bees nest, and floral food resources, increasing our understanding of management effects on both soils and floral resources is important to improving bee conservation efforts. 2. In 20 tallgrass prairie plots managed under 1 of 3 common methods: burning, haying and patch‐burn grazing, we assessed effects on bee communities and necessary above‐ and below‐ground resources. We also considered how management and resources affected below‐ground nesting in each prairie. 3. Management type affected both soil conditions and floral resources with patchburn grazing sites providing overall worse resources for bees compared to ungrazed sites. Soil conditions were also important for predicting most aspects of the bee community including abundance and community composition. Soil conditions also decreased floral richness and Floristic Quality Index (FQI). This suggests management affects bee communities both directly and indirectly through soil. 4. Increased nesting was observed in sites with greater floral abundance and soil conditions that correspond to increased bare ground, lower soil moisture and warmer soil temperatures suggesting management that helps increase floral abundance and improve soil conditions could be critical to increasing bee nesting. 5. Synthesis and applications. Measuring and tracking bare ground, Floristic Quality Index (FQI) and floral richness may help managers determine if their management methods are adversely affecting bees. Grazing and haying management negatively affected the bee community, vital nesting and…" floraresources and nesting rate. These managements may need to be avoided to meet bee conservation goals in prairies. Additionally, while soils have been largely overlooked, we found soil conditions to be an important predictor for bee communities and floral resources, and should be considered more explicitly in conserved areas. | Abstract: " Global trends in pollinator-dependent crops have raised awareness of the need to support managed and wild bee populations to ensure sustainable crop production. Provision of sufficient forage resources is a key element for promoting bee populations within human impacted landscapes, particularly those in agricultural lands where demand for pollination service is high and land use and management practices have reduced available flowering resources. Recent government incentives in North America and Europe support the planting of wildflowers to benefit pollinators; surprisingly, in North America there has been almost no rigorous testing of the performance of wildflower mixes, or their ability to support wild bee abundance and diversity. We tested different wildflower mixes in a spatially replicated, multiyear study in three regions of North America where production of pollinatordependent crops is high: Florida, Michigan, and California. In each region, we quantified flowering among wildflower mixes composed of annual and perennial species, and with high and low relative diversity. We measured the abundance and species richness of wild bees, honey bees, and syrphid flies at each mix over two seasons. In each region, some but not all wildflower mixes provided significantly greater floral display area than unmanaged weedy control plots. Mixes also attracted greater abundance and richness of wild bees, although the identity of best mixes varied among regions. By partitioning floral display size from mix identity we show the importance of display size for attracting abundant and diverse wild bees. Season-long monitoring also revealed that designing mixes to provide continuous bloom throughout the growing season is critical to supporting the greatest pollinator species richness. Contrary to expectation, perennials bloomed in their first season, and complementarity in attraction of pollinators among annuals and perennials suggests that inclusion of functionally diverse species may provide the greatest benefit. Wildflower mixes may be particularly important for providing resources for some taxa, such as bumble bees, which are known to be in decline in several regions of North America. No mix consistently attained the full diversity that was planted. Further study is needed on how to achieve the desired floral display and diversity from seed mixes. " Additional information in supplemental Appendices online: http://dx.doi.org/10.1890/14-1748.1.sm | ABSTRACT: "Coho salmon (Oncorhynchus kisutch) are highly sensitive to 6PPD-Quinone (6PPD-Q). Details of the hydrological and biogeochemical processes controlling spatial and temporal dynamics of 6PPD-Q fate and transport from points of deposition to receiving waters (e.g., streams, estuaries) are poorly understood. To understand the fate and transport of 6PPD and mechanisms leading to salmon mortality Visualizing Ecosystem Land Management Assessments (VELMA), an ecohydrological model developed by US Environmental Protection Agency (EPA), was enhanced to better understand and inform stormwater management planning by municipal, state, and federal partners seeking to reduce stormwater contaminant loads in urban streams draining to the Puget Sound National Estuary. This work focuses on the 5.5 km2 Longfellow Creek upper watershed (Seattle, Washington, United States), which has long exhibited high rates of acute urban runoff mortality syndrome in coho salmon. We present VELMA model results to elucidate these processes for the Longfellow Creek watershed across multiple scales–from 5-m grid cells to the entire watershed. Our results highlight hydrological and biogeochemical controls on 6PPD-Q flow paths, and hotspots within the watershed and its stormwater infrastructure, that ultimately impact contaminant transport to Longfellow Creek and Puget Sound. Simulated daily average 6PPD-Q and available observed 6PPD-Q peak in-stream grab sample concentrations (ng/L) corresponds within plus or minus 10 ng/L. Most importantly, VELMA’s high-resolution spatial and temporal analysis of 6PPD-Q hotspots provides a tool for prioritizing the locations, amounts, and types of green infrastructure that can most effectively reduce 6PPD-Q stream concentrations to levels protective of coho salmon and other aquatic species. " |
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
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Not reported | None identified | None identified | Not applicable | None identifed | Land use change | None identified | Land management, ecosystem management, response to EU 2020 Biodiversity Strategy | None identified | None identified | 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 | None identified | None identified | Management strategies of prairie remnants for pollinator community | None identrified | Not reported |
Biophysical Context
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Urban areas 3.0% of land in U.S. and Urban/community land (5.3%) in 2000. | No additional description provided | Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | Agricultural landuse , 1st-10th order streams | shallow bay (mean 3.7m), transition zone between warm temperate and tropical biogeographic provinces. Highly urbanized watershed | No additional description provided | No additional description provided | Northern Spain; Bizkaia region | No additional description provided | No additional description provided | Conterminous US | The UNBR watershed is comprised primarily of two main physiographic areas, the West Cross Timbers and the Grand Prairie Land Resource Areas. In the West Cross Timbers, soils are primarily fine sandy loams with sandy clay subsoils. Soils in the Grand Prairie area, on the other hand, are typically calcareous clays and clay loams (Ward et al. 1992). | Alewife spawning runs typically occur Mid March - May. | None identified | No additional description provided | field plots near agricultural fruit and vegetable research farms | 6PPD deposition from vehicle tire wear particles. |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | Not applicable | No scenarios presented | Future land use and land cover; climate change | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented |
No scenarios presented ?Comment:Model can be run from WaSSI website using a historic data set (1961 - 2010) or projections from various climate models representing different emissions scenarios and time periods from recent past to 2099. |
Conservation management strategies to reduce phosphorus losses | No scenarios presented | N/A | Alternative management strategies: burning, haying and patch‐burn grazing | Varied wildflower planting mixes of annuals and perennials | N/A |
EM ID
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EM-24 |
EM-59 ![]() |
EM-69 | EM-93 |
EM-102 ![]() |
EM-112 ![]() |
EM-137 | EM-193 | EM-315 | EM-376 | EM-439 |
EM-584 ![]() |
EM-667 ![]() |
EM-683 |
EM-777 ![]() |
EM-796 ![]() |
EM-993 |
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
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Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application |
New or Pre-existing EM?
em.detail.newOrExistHelp
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Application of existing model | Application of existing model | New or revised model | New or revised 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 |
Application of existing model ?Comment:. |
New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
em.detail.idHelp
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EM-24 |
EM-59 ![]() |
EM-69 | EM-93 |
EM-102 ![]() |
EM-112 ![]() |
EM-137 | EM-193 | EM-315 | EM-376 | EM-439 |
EM-584 ![]() |
EM-667 ![]() |
EM-683 |
EM-777 ![]() |
EM-796 ![]() |
EM-993 |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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None | Doc-345 | Doc-260 | Doc-269 | Doc-154 | Doc-155 | None | Doc-309 | Doc-338 | Doc-190 | Doc-223 | None | Doc-303 | Doc-305 | None | None | Doc-352 | Doc-383 | None | None | Doc-400 | Doc-366 | Doc-423 | Doc-430 |
EM ID for related EM
em.detail.relatedEmEmIdHelp
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None | None | EM-65 | EM-66 | EM-68 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | None | EM-363 | EM-438 | EM-109 | EM-142 | EM-51 | None | None | None | None | EM-549 | EM-661 | EM-665 | EM-666 | EM-672 | EM-674 | EM-673 | EM-682 | EM-684 | EM-685 | None | EM-784 | None |
EM Modeling Approach
EM ID
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EM-24 |
EM-59 ![]() |
EM-69 | EM-93 |
EM-102 ![]() |
EM-112 ![]() |
EM-137 | EM-193 | EM-315 | EM-376 | EM-439 |
EM-584 ![]() |
EM-667 ![]() |
EM-683 |
EM-777 ![]() |
EM-796 ![]() |
EM-993 |
EM Temporal Extent
em.detail.tempExtentHelp
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1989-2010 | 2008-2010 | 2007-2009 | 2000-2008 | 2006-2011 | 2005-7; 2035-45 | Not applicable | 2000 - 2007 | 2000-2011 | Not applicable | 1961-2009 | 1960-2001 | 1979-2009 | Summer 2017 | 2012-2016 | 2010-2011 | 9/2020-6/2021 |
EM Time Dependence
em.detail.timeDependencyHelp
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time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-dependent | time-stationary | time-dependent | time-dependent |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
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future time | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | future time | future time | Not applicable | past time | Not applicable | past time | past time |
EM Time Continuity
em.detail.continueDiscreteHelp
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discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | discrete | discrete | Not applicable | discrete | Not applicable | discrete | discrete |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | 1 | 1 | Not applicable | 1 | Not applicable | 1 | 1 |
EM Temporal Grain Size Unit
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Year | Hour | Not applicable | Not applicable | Not applicable | Not applicable | Hour | Not applicable | Not applicable | Year | Month | Day | Not applicable | Day | Not applicable | Year | Day |
EM ID
em.detail.idHelp
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EM-24 |
EM-59 ![]() |
EM-69 | EM-93 |
EM-102 ![]() |
EM-112 ![]() |
EM-137 | EM-193 | EM-315 | EM-376 | EM-439 |
EM-584 ![]() |
EM-667 ![]() |
EM-683 |
EM-777 ![]() |
EM-796 ![]() |
EM-993 |
Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Geopolitical | Physiographic or Ecological | Watershed/Catchment/HUC | Physiographic or Ecological | Watershed/Catchment/HUC | Not applicable | Geopolitical | Physiographic or ecological | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical |
Point or points ?Comment:This is a guess based on information in the document. 3 field sites were separated by up to 9km |
Watershed/Catchment/HUC |
Spatial Extent Name
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United States | Durham NC and vicinity | Central French Alps | Upper Mississippi, Ohio and Missouri River sub-basins | Tampa Bay | Hood Canal | Not applicable | Bilbao Metropolitan Greenbelt | Puget Sound Region | Massachusetts Ocean | All 8-digit hydrologic unit codes (HUC-8) in the conterminous USA | Upper North Bosque River watershed | Bride Brook | Three Bays, Cape Cod | Counties: Barton, St. Clair, Cedar, Dade and Polk | Agricultural plots | Longfellow creek |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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>1,000,000 km^2 | 100-1000 km^2 | 10-100 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | Not applicable | 100-1000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | >1,000,000 km^2 | 100-1000 km^2 | 1-10 ha | 1000-10,000 km^2. | 1000-10,000 km^2. | 10-100 km^2 | 1-10 km^2 |
EM ID
em.detail.idHelp
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EM-24 |
EM-59 ![]() |
EM-69 | EM-93 |
EM-102 ![]() |
EM-112 ![]() |
EM-137 | EM-193 | EM-315 | EM-376 | EM-439 |
EM-584 ![]() |
EM-667 ![]() |
EM-683 |
EM-777 ![]() |
EM-796 ![]() |
EM-993 |
EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Spatial grain type is census block group. |
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) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Spatial grain for computations is the HUC-8. A HUC-12 version is under development. Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. |
spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | Not applicable | Not applicable |
Spatial Grain Size
em.detail.spGrainSizeHelp
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1 m^2 | irregular | 20 m x 20 m | 1 km | 1 km^2 | 30 m x 30 m | 30 x 30 m | 2 m x 2 m | 200m x 200m | 1 km x1 km | 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 | Not applicable | beach length | 1 ha | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-24 |
EM-59 ![]() |
EM-69 | EM-93 |
EM-102 ![]() |
EM-112 ![]() |
EM-137 | EM-193 | EM-315 | EM-376 | EM-439 |
EM-584 ![]() |
EM-667 ![]() |
EM-683 |
EM-777 ![]() |
EM-796 ![]() |
EM-993 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Numeric | Numeric | Analytic | Analytic | Analytic | Other or unclear (comment) | Numeric | Analytic | Analytic | Numeric | Numeric | Numeric | Analytic | Numeric | Analytic | Numeric | Analytic |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-24 |
EM-59 ![]() |
EM-69 | EM-93 |
EM-102 ![]() |
EM-112 ![]() |
EM-137 | EM-193 | EM-315 | EM-376 | EM-439 |
EM-584 ![]() |
EM-667 ![]() |
EM-683 |
EM-777 ![]() |
EM-796 ![]() |
EM-993 |
Model Calibration Reported?
em.detail.calibrationHelp
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No | Unclear | No | No | No | Yes | Not applicable | No | No | No | No | Yes |
Yes ?Comment:The fish counter (for alewife numbers) was calibrated. |
Yes | No | No | Yes |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | Yes | No | No | No | Not applicable | No | No | No | No | No | No | No | Not applicable | No | No |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None |
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None | None | None | None | None | None | None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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No | No | Yes | No | No | Yes | Not applicable | Yes | No | No | No | No | No | No | No | No | Yes |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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Yes ?Comment:An error of sampling was reported, but not an error of estimation Estimation error was unknown and reported as likely larger than the error of sampling. |
No | No | Yes | No | No | Not applicable | No | No | No | No | No | No | No | No | No | Unclear |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | Unclear | No | Yes | Not applicable | No | No | No | No | No | No | No | Yes | No | Unclear |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-24 |
EM-59 ![]() |
EM-69 | EM-93 |
EM-102 ![]() |
EM-112 ![]() |
EM-137 | EM-193 | EM-315 | EM-376 | EM-439 |
EM-584 ![]() |
EM-667 ![]() |
EM-683 |
EM-777 ![]() |
EM-796 ![]() |
EM-993 |
Comment:EM presents carbon storage and sequestration rates for country and by individual state |
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None |
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None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-24 |
EM-59 ![]() |
EM-69 | EM-93 |
EM-102 ![]() |
EM-112 ![]() |
EM-137 | EM-193 | EM-315 | EM-376 | EM-439 |
EM-584 ![]() |
EM-667 ![]() |
EM-683 |
EM-777 ![]() |
EM-796 ![]() |
EM-993 |
None | None | None | None |
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None | None |
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None | None |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-24 |
EM-59 ![]() |
EM-69 | EM-93 |
EM-102 ![]() |
EM-112 ![]() |
EM-137 | EM-193 | EM-315 | EM-376 | EM-439 |
EM-584 ![]() |
EM-667 ![]() |
EM-683 |
EM-777 ![]() |
EM-796 ![]() |
EM-993 |
Centroid Latitude
em.detail.ddLatHelp
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40.16 | 35.99 | 45.05 | 36.98 | 27.74 | 47.8 | -9999 | 43.25 | 48 | 41.72 | 39.83 | 32.09 | 41.32 | 41.62 | 37.68 | 43.87 | 47.55 |
Centroid Longitude
em.detail.ddLongHelp
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-99.79 | -78.96 | 6.4 | -89.13 | -82.57 | -122.7 | -9999 | -2.92 | -123 | -69.87 | -98.58 | -98.12 | -72.24 | -70.42 | -93.71 | -85.64 | 122.37 |
Centroid Datum
em.detail.datumHelp
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WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Not applicable | Provided | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Provided | Provided |
EM ID
em.detail.idHelp
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EM-24 |
EM-59 ![]() |
EM-69 | EM-93 |
EM-102 ![]() |
EM-112 ![]() |
EM-137 | EM-193 | EM-315 | EM-376 | EM-439 |
EM-584 ![]() |
EM-667 ![]() |
EM-683 |
EM-777 ![]() |
EM-796 ![]() |
EM-993 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Forests | Created Greenspace | Created Greenspace | Atmosphere | Agroecosystems | Grasslands | Rivers and Streams | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Rivers and Streams | Ground Water | Created Greenspace | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Created Greenspace | Near Coastal Marine and Estuarine |
Lakes and Ponds ?Comment:Watershed model represents all land areas, major streams and rivers. Since leaf area index, LAI, is an important variable, forests, created greenspaces (e.g., urban forests) and scrub/shrub subclasses are included. |
Agroecosystems | Rivers and Streams | Near Coastal Marine and Estuarine | Grasslands | Agroecosystems | Rivers and Streams |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Urban forests | Urban and vicinity | Subalpine terraces, grasslands, and meadows. | Not applicable | Habitat Zones (Low, Med, High, Optimal) around seagrass and emergent marsh | glacier-carved saltwater fjord | Urban watersheds | none | Terrestrial environment surrounding a large estuary | None identified | Not applicable | Rangeland and forage fields for dairy | Coastal stream | Beaches | Remnant tallgrass prairie | Agricultural landscape | small stream |
EM Ecological Scale
em.detail.ecoScaleHelp
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Zone within an ecosystem | Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Ecological scale corresponds to the Environmental Sub-class | Zone within an ecosystem | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Ecological scale is coarser than that of the Environmental Sub-class ?Comment:Terrestrial characteristics are aggregated at a broad (HUC-8) scale; different types of aquatic sub-classes are not differentiated. |
Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-24 |
EM-59 ![]() |
EM-69 | EM-93 |
EM-102 ![]() |
EM-112 ![]() |
EM-137 | EM-193 | EM-315 | EM-376 | EM-439 |
EM-584 ![]() |
EM-667 ![]() |
EM-683 |
EM-777 ![]() |
EM-796 ![]() |
EM-993 |
EM Organismal Scale
em.detail.orgScaleHelp
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Species ?Comment:Trees were identified to species for the differential growth and biomass estimates part of the analysis. |
Not applicable | Community | Not applicable | Species | Not applicable | Community | Not applicable | Not applicable | Species | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Species | Species | Species |
Taxonomic level and name of organisms or groups identified
EM-24 |
EM-59 ![]() |
EM-69 | EM-93 |
EM-102 ![]() |
EM-112 ![]() |
EM-137 | EM-193 | EM-315 | EM-376 | EM-439 |
EM-584 ![]() |
EM-667 ![]() |
EM-683 |
EM-777 ![]() |
EM-796 ![]() |
EM-993 |
None Available | None Available | None Available | None Available |
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None Available | None Available | None Available | None Available |
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None Available | None Available |
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None Available |
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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)
EM-24 |
EM-59 ![]() |
EM-69 | EM-93 |
EM-102 ![]() |
EM-112 ![]() |
EM-137 | EM-193 | EM-315 | EM-376 | EM-439 |
EM-584 ![]() |
EM-667 ![]() |
EM-683 |
EM-777 ![]() |
EM-796 ![]() |
EM-993 |
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None |
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None |
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<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-24 |
EM-59 ![]() |
EM-69 | EM-93 |
EM-102 ![]() |
EM-112 ![]() |
EM-137 | EM-193 | EM-315 | EM-376 | EM-439 |
EM-584 ![]() |
EM-667 ![]() |
EM-683 |
EM-777 ![]() |
EM-796 ![]() |
EM-993 |
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None |
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None |
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None |
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Comment:Model identifies toxicant concentrations relative to the known LC50 for coho juveniles which is 95ng/L (Spromber and Scholz, 2011; |