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-94 | EM-97 | EM-137 | EM-194 | EM-317 |
EM-422 ![]() |
EM-450 | EM-462 | EM-492 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-660 ![]() |
EM-685 |
EM-734 ![]() |
EM-842 | EM-848 | EM-876 |
EM Short Name
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Reduction in pesticide runoff risk, Europe | AnnAGNPS, Kaskaskia River watershed, IL, USA | i-Tree Hydro v4.0 | Coral and land development, St.Croix, VI, USA | ARIES carbon, Puget Sound Region, USA | HexSim v2.4, San Joaquin kit fox, CA, USA | Decrease in wave runup, St. Croix, USVI | Value of finfish, St. Croix, USVI | EnviroAtlas - Restorable wetlands | InVEST fisheries, lobster, South Africa | Nutrient Tracking Tool (NTT) | VELMA v2.0, Ohio, USA | RUM: Valuing fishing quality, Michigan, USA | Visitor value lost to a beach closure, MA, USA | WESP: Marsh and open water, ID, USA | Horned lark abundance, Piedmont region, USA | National invertebrate community rank index | Neighborhood greenness and health, FL, USA |
EM Full Name
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Reduction in pesticide runoff risk, Europe | AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | i-Tree Hydro v4.0 (default data option) | Coral colony density and land development, St.Croix, Virgin Islands, USA | ARIES (Artificial Intelligence for Ecosystem Services) Carbon Storage and Sequestration, Puget Sound Region, Washington, USA | HexSim v2.4, San Joaquin kit fox rodenticide exposure, California, USA | Decrease in wave runup (by reef), St. Croix, USVI | Relative value of finfish (on reef), St. Croix, USVI | US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa | Nutrient Tracking Tool (NTT) | Visualizing Ecosystems for Land Management Assessments (VELMA) v2.0, Shayler Crossing watershed, Ohio, USA | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | Visitor value lost to a beach closure, Barnstable, Massachusetts, USA | WESP: Deepwater marsh and open Water waterfowl habitat, Idaho, USA | Horned lark abundance, Piedmont ecoregion, USA | National invertebrate community ranking index (NICRI) | Neighborhood greenness and chronic health conditions in Medicare beneficiaries, Miami-Dade County, Florida, USA |
EM Source or Collection
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None | US EPA | i-Tree | USDA Forest Service | US EPA | ARIES | US EPA | US EPA | US EPA | US EPA | EnviroAtlas | InVEST | None | US EPA | None | US EPA | None | None | None | None |
EM Source Document ID
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255 | 137 | 198 | 96 | 302 |
337 ?Comment:The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships. |
335 | 335 | 262 |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
352 |
359 ?Comment:Document #366 is a supporting document for this EM. McKane et al. 2014, VELMA Version 2.0 User Manual and Technical Documentation. |
382 ?Comment:Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010. |
386 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
405 | 407 | 417 |
Document Author
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Lautenbach, S., Maes, J., Kattwinkel, M., Seppelt, R., Strauch, M., Scholz, M., Schulz-Zunkel, C., Volk, M., Weinert, J. and Dormann, C. | Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | USDA Forest Service | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Nogeire, T. M., J. J. Lawler, N. H. Schumaker, B. L. Cypher, and S. E. Phillips | Yee, S. H., Dittmar, J. A., and L. M. Oliver | 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 | Saleh, A. and O. Gallego | Hoghooghi, N., H. E. Golden, B. P. Bledsoe, B. L. Barnhart, A. F. Brookes, K. S. Djang, J. J. Halama, R. B. McKane, C. T. Nietch, and P. P. Pettus | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | Murphy, C. and T. Weekley | Riffel, S., Scognamillo, D., and L. W. Burger | Cuffney, Tom | Brown, S. C., J. Lombard, K. Wang, M. M. Byrne, M. Toro, E. Plater-Zyberk, D. J. Feaster, J. Kardys, M. I. Nardi, G. Perez-Gomez, H. M. Pantin, and J. Szapocznik |
Document Year
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2012 | 2011 | Not Reported | 2011 | 2014 | 2015 | 2014 | 2014 | 2013 | 2018 | 2018 | 2018 | 2014 | 2018 | 2012 | 2008 | 2003 | 2016 |
Document Title
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Mapping water quality-related ecosystem services: concepts and applications for nitrogen retention and pesticide risk reduction | AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | i-Tree Hydro User's Manual v. 4.0 | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Land use as a driver of patterns of rodenticide exposure in modeled kit fox populations | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | EnviroAtlas - National | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals | Nutrient Tracking Tool (NTT) User Manual | Cumulative effects of low impact development on watershed hydrology in a mixed land-cover system | Valuing recreational fishing quality at rivers and streams | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Invertebrate Status Index | Neighborhood greenness and chronic health conditions in Medicare beneficiaries |
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 | 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 | Other or unclear (explain in Comment) | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Webpage | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | webpage | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published report | Published journal manuscript |
EM ID
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EM-94 | EM-97 | EM-137 | EM-194 | EM-317 |
EM-422 ![]() |
EM-450 | EM-462 | EM-492 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-660 ![]() |
EM-685 |
EM-734 ![]() |
EM-842 | EM-848 | EM-876 |
Not applicable | https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | http://www.itreetools.org | Not applicable | http://aries.integratedmodelling.org/ | http://www.hexsim.net/ | Not applicable | Not applicable | https://www.epa.gov/enviroatlas | https://www.naturalcapitalproject.org/invest/ | http://ntt.tiaer.tarleton.edu/welcomes/new?locale=en | https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | |
Contact Name
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Sven Lautenbach | Yongping Yuan | Not applicable | Leah Oliver | Ken Bagstad | Theresa M. Nogeire | Susan H. Yee | Susan H. Yee | EnviroAtlas Team | Michelle Ward |
Ali Saleh ?Comment:Phone # 254-968-9079 |
Heather Golden | Richard Melstrom | Kate K, Mulvaney | Chris Murphy | Sam Riffell | Tom Cuffney | Scott C. Brown |
Contact Address
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Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany | U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | Not applicable | National Health and Environmental Research Effects Laboratory | Geosciences and Environmental Change Science Center, US Geological Survey | School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | 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 | Associate Director, Texas Institute for Applied Environmental Research, P.O. Box T410, Tarleton State University Stephenville, TX 76402 | National Exposure Research Laboratory, Office of Research and Development, US EPA, Cincinnati, OH 45268, USA | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | Not reported | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | 3916 Sunset Ridge Rd, Raleigh, NC 27607 | Department of Public Health Sciences, University of Miami Miller School of Medicine, 1120 NW 14th Street, Clinical Research Building (CRB), Room 1065, Miami FL 33136 |
Contact Email
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sven.lautenbach@ufz.de | yuan.yongping@epa.gov | Not applicable | leah.oliver@epa.gov | kjbagstad@usgs.gov | tnogeire@gmail.com | yee.susan@epa.gov | yee.susan@epa.gov | enviroatlas@epa.gov | m.ward@uq.edu.au | saleh@tarleton.edu | Golden.Heather@epa.gov | melstrom@okstate.edu | Mulvaney.Kate@EPA.gov | chris.murphy@idfg.idaho.gov | sriffell@cfr.msstate.edu | tcuffney@usgs.gov | sbrown@med.miami.edu |
EM ID
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EM-94 | EM-97 | EM-137 | EM-194 | EM-317 |
EM-422 ![]() |
EM-450 | EM-462 | EM-492 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-660 ![]() |
EM-685 |
EM-734 ![]() |
EM-842 | EM-848 | EM-876 |
Summary Description
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AUTHOR'S DESCRIPTION: "We used a spatially explicit model to predict the potential exposure of small streams to insecticides (run-off potential – RP) as well as the resulting ecological risk (ER) for freshwater fauna on the European scale (Schriever and Liess 2007; Kattwinkel et al. 2011)...The recovery of community structure after exposure to insecticides is facilitated by the presence of undisturbed upstream stretches that can act as sources for recolonization (Niemi et al. 1990; Hatakeyama and Yokoyama 1997). In the absence of such sources for recolonization, the structure of the aquatic community at sites that are exposed to insecticides differs significantly from that of reference sites (Liess and von der Ohe 2005)...Hence, we calculated the ER depending on RP for insecticides and the amount of recolonization zones. ER gives the percentage of stream sites in each grid cell (10 × 10 km) in which the composition of the aquatic community deviated from that of good ecological status according to the WFD. In a second step, we estimated the service provided by the environment comparing the ER of a landscape lacking completely recolonization sources with that of the actual landscape configuration. Hence, the ES provided by non-arable areas (forests, pastures, natural grasslands, moors and heathlands) was calculated as the reduction of ER for sensitive species. The service can be thought of as a habitat provisioning/nursery service that leads to an improvement of ecological water quality." | AUTHORS' DESCRIPTION: "AnnAGNPS is an advanced simulation model developed by the USDA-ARS and Natural Resource Conservation Services (NRCS) to help evaluate watershed response to agricultural management practices. It is a continuous simulation, daily time step, pollutant loading model designed to simulate water, sediment and chemical movement from agricultural watersheds.p. 198" | 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." | AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | ABSTRACT: "...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: "We quantified carbon sequestration and storage in vegetation and soils using Bayesian models (Bagstad et al. 2011) calibrated with Moderate-resolution Imaging Spectroradiometer Net Primary Productivity (MODIS GPP/NPP Project, http://secure.ntsg.umt. edu/projects/index.php/ID/ca2901a0/fuseaction/prohttp://www.whrc.org/ational Bwww.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_053627)vey Geographic Dahttp://www.geomac.gov/index.shtml)wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_053627) soils data, respectively. By overlaying fire boundary polygons from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov/index.shtml) we estimated carbon storage losses caused by wildfire, using fuel consumption coefficients from Spracklen et al. (2009) and carbon pool data from Smith et al. (2006). By incorporating the impacts of land-cover change from urbanization (Bolte and Vache 2010) within carbon models, we quantified resultant changes in carbon storage." | ABSTRACT: "...Here, we use an individual-based population model to assess potential population-wide effects of rodenticide exposures on the endangered San Joaquin kit fox (Vulpes macrotis mutica). We estimate likelihood of rodenticide exposure across the species range for each land cover type based on a database of reported pesticide use and literature…" AUTHOR'S DESCRIPTION: "We simulated individual kit foxes across their range using HexSim [33], a computer modeling platform for constructing spatially explicit population models. Our model integrated life history traits, repeated exposures to rodenticides, and spatial data layers describing habitat and locations of likely exposures. We modeled female kit foxes using yearly time steps in which each individual had the potential to disperse, establish a home range, acquire resources from their habitat, reproduce, accumulate rodenticide exposures, and die." "Simulated kit foxes assembled home ranges based on local habitat suitability, with range size inversely related to habitat suitability [34,35]. Kit foxes aimed to acquire a home range with a target score corresponding to the observed 544 ha home range size in the most suitable habitat [26]. Modeled home ranges varied in size from 170 ha to 1000 ha. Kit foxes were assigned to a resource class depending on the quality of the habitat in their acquired home range. The resource class then influenced rates of kit fox survival," "Juveniles and adults without ranges searched for a home range across 30 km2 outside of their natal range, using HexSim’s ‘adaptive’ exploration algorithm [33]." | 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, storm damage, or coastal inundation during extreme events...Wave run-up, R, can be estimated as R = H(tan α/(√H/Ho) where H is the wave height nearshore, Ho is the deep water wave height, and α is the angle of the beach slope. R may be corrected by a multiplier depending on the porosity of the shoreline surface...The contribution of each grid cell to reduction in wave run-up would depend on its contribution to wave height attenuation (Eq. (S3))." | 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…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(5) value of finfish," | DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map." | 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." | AUTHOR'S DESCRIPTION: "The Nutrient Tracking Tool (NTT) was designed and developed by the Texas Institute for Applied Environmental Research (TIAER), Tarleton State University with funding from USDA Office of Environmental Markets, USDA-NRCS Conservation Innovation Grants program, and various state agencies. NTT is a web-based, site-specific application that estimates nutrient and sediment losses at the field scale or at the small watershed scale. Agricultural producers and land managers can define a number of management scenarios and generate a report showing the expected nutrient loss differences between any selected scenarios for a given field or small watershed. NTT compares agricultural management systems to calculate a change in expected flow, nitrogen, phosphorus, sediment losses, and crop yield. Estimates are made using the APEX model (Williams et al. 2000). Results represent average losses from the field based on 35 years of simulated weather. NTT requires regional soils, climate and site-specific crop management information. NTT currently provides selections for all regions of U.S. and Puerto Rico territory, but it has only been validated for a limited number of states and counties. As validation becomes possible in other parts of the country, parameter files may be updated for additional counties in future versions. There are two versions of new NTT program available: The BASIC version is a user-friendly version of NTT that allows users to estimate N, P and sediment from crop and pasture lands. The Research and Education version of NTT (NTT-RE) was developed for researchers and educational institutes for teaching and training purposes. NTT-RE includes additional functions allowing the user to view and edit soil layers, view crop water and nutrient stresses, and modify and the APEX parameters file for calibration and validation purposes. The data sources and APEX simulations in both versions are identical. For more information regarding NTT, please refer to Saleh et al. (2011 and 2015)." | ABSTRACT: "Low Impact Development (LID) is an alternative to conventional urban stormwater management practices, which aims at mitigating the impacts of urbanization on water quantity and quality. Plot and local scale studies provide evidence of LID effectiveness; however, little is known about the overall watershed scale influence of LID practices. This is particularly true in watersheds with a land cover that is more diverse than that of urban or suburban classifications alone. We address this watershed-scale gap by assessing the effects of three common LID practices (rain gardens, permeable pavement, and riparian buffers) on the hydrology of a 0.94 km2 mixed land cover watershed. We used a spatially-explicit ecohydrological model, called Visualizing Ecosystems for Land Management Assessments (VELMA), to compare changes in watershed hydrologic responses before and after the implementation of LID practices. For the LID scenarios, we examined different spatial configurations, using 25%, 50%, 75% and 100% implementation extents, to convert sidewalks into rain gardens, and parking lots and driveways into permeable pavement. We further applied 20 m and 40 m riparian buffers along streams that were adjacent to agricultural land cover…" AUTHOR'S DESCRIPTION: "VELMA’s modeling domain is a three-dimensional matrix that includes information regarding surface topography, land use, and four soil layers. VELMA uses a distributed soil column framework to model the lateral and vertical movement of water and nutrients through the four soil layers. A soil water balance is solved for each layer. The soil column model is placed within a watershed framework to create a spatially distributed model applicable to watersheds (Figure 2, shown here with LID practices). Adjacent soil columns interact through down-gradient water transport. Water entering each pixel (via precipitation or flow from an adjacent pixel) can either first infiltrate into the implemented LID and the top soil layer, and then to the downslope pixel, or continue its downslope movement as the lateral surface flow. Surface and subsurface lateral flow are routed using a multiple flow direction method, as described in Abdelnour et al. [21]. A detailed description of the processes and equations can be found in McKane et al. [32], Abdelnour et al. [21], Abdelnour et al. [40]." | ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. " | ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "While it might be assumed that the economic value of a beach day and the value of a lost beach day would be symmetric, they are not quite the same in our analysis. This is because the town has many fixed costs for beach management, including staff, facility maintenance, and other amenities. These fixed costs are offset by the daily parking fees charged to out-of-town visitors and the various beach stickers available for town residents. Assuming the town does not make a profit and just breaks even on beach parking fees in relation to the costs incurred to provide the services, the net economic value of a day without a closure (benefits less costs) would simply be the consumer surplus for the public. However, this amount is different than the net economic value lost due to a beach closure, which includes the lost consumer surplus as well as the lost revenue to the town. This revenue is money the town would have collected to cover costs and therefore is considered a loss (negative producer surplus). Therefore, a beach day affected by a closure is valued as a loss of consumer surplus plus lost parking revenue…" Equation 3, page 19, provides the resulting formula for the value lost from a beach closure. | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds." | ABSTRACT: "The Invertebrate Status Index is a multimetric index that was derived for the NAWQA Program to provide a simple national characterization of benthic invertebrate communities. This index— referred to here as the National Invertebrate Community Ranking Index (NICRI)—provides a simple method of placing community conditions within the context of all sites sampled by the NAWQA Program. The multimetric index approach is the most commonly used method of characterizing biological conditions within the U.S. (Barbour and others, 1999). Using this approach, communities may be compared by considering how individual metrics vary among sites or by combining individual metrics into a single composite (i.e., multimetric) index and examining how this single index varies among sites. Combining metrics into a single multimetric index simplifies the presentation of results (Barbour and others, 1999) and minimizes weaknesses that may be associated with individual metrics (Ohio EPA, 1987a,b). The NICRI is a multimetric index that combines 11 metrics (RICH, EPTR, CG_R, PR_R, EPTRP, CHRP, V2DOMP, EPATOLR, EPATOLA, DIVSHAN, and EVEN; Table 1) into a single, nationally consistent, composite index. The NICRI was used to rank 140 sites of the FY94 group of study units, with median values used for sites where data were available for multiple reaches and(or) multiple years. Average metric scores were then rescaled using the PERCENTRANK function and multiplied by 100 to produce a final NICRI score that ranged from 0 (low ranking relative to other NAWQA Program sites and presumably diminished community conditions) to 100 (high ranking relative to other NAWQA Program sites and presumably excellent community conditions). " | ABSTRACT: "Introduction: Prior studies suggest that exposure to the natural environment may impact health. The present study examines the association between objective measures of block-level greenness (vegetative presence) and chronic medical conditions, including cardiometabolic conditions, in a large population-based sample of Medicare beneficiaries in Miami-Dade County, Florida. Methods: The sample included 249,405 Medicare beneficiaries aged >=65 years whose location (ZIP+4) within Miami-Dade County, Florida, did not change, from 2010 to 2011. Data were obtained in 2013 and multilevel analyses conducted in 2014 to examine relationships between greenness, measured by mean Normalized Difference Vegetation Index from satellite imagery at the Census block level, and chronic health conditions in 2011, adjusting for neighborhood median household income, individual age, gender, race, and ethnicity. Results: Higher greenness was significantly associated with better health, adjusting for covariates: An increase in mean block-level Normalized Difference Vegetation Index from 1 SD less to 1 SD more than the mean was associated with 49 fewer chronic conditions per 1,000 individuals, which is approximately similar to a reduction in age of the overall study population by 3 years. This same level of increase in mean Normalized Difference Vegetation Index was associated with a reduced risk of diabetes by 14%, hypertension by 13%, and hyperlipidemia by 10%. Planned post-hoc analyses revealed stronger and more consistently positive relationships between greenness and health in lower- than higher-income neighborhoods. Conclusions: Greenness or vegetative presence may be effective in promoting health in older populations, particularly in poor neighborhoods, possibly due to increased time outdoors, physical activity, or stress mitigation." |
Specific Policy or Decision Context Cited
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European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | Not reported | None identified | Not applicable | None identified | None identified | None identified | None identified | None Identified | Future rock lobster fisheries management | None identified | None identified | None identified | Economic value of protecting coastal beach water quality from contamination caused closures. | None identified | None reported | None Identified | None identified |
Biophysical Context
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Not applicable | Upper Mississipi River basin, elevation 142-194m, | No additional description provided | nearshore; <1.5 km offshore; <12 m depth | No additional description provided | No additional description provided | No additional description provided | No additional description provided | No additional description provided | No additional description provided | No additional description provided | The Shayler Crossing (SHC) watershed is a subwatershed of the East Fork Little Miami River Watershed in southwest Ohio, USA and falls within the Till Plains region of the Central Lowland physiographic province. The Till Plains region is a topographically young and extensive flat plain, with many areas remaining undissected by even the smallest stream. The bedrock is buried under a mantle of glacial drift 3–15 m thick. The Digital Elevation Model (DEM) has a maximum value of ~269 m (North American_1983 datum) within the watershed boundary (Figure 1). The soils are primarily the Avonburg and Rossmoyne series, with high silty clay loam content and poor to moderate infiltration. Average annual precipitation for the period, 1990 through 2011, was 1097.4 _ 173.5 mm. Average annual air temperature for the same period was 12 _C Mixed land cover suburban watershed. The primary land uses consist of 64.1% urban or developed area (including 37% lawn, 12% building, 6.5% street, 6.4% sidewalk, and 2.1% parking lot and driveway), 23% agriculture, and 13% deciduous forest. Total imperviousness covers approximately 27% of the watershed area. | stream and river reaches of Michigan | Four separate beaches within the community of Barnstable | restored, enhanced and created wetlands | Conservation Reserve Program lands left to go fallow | Streams and Rivers | No additional description provided |
EM Scenario Drivers
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No scenarios presented | Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | No scenarios presented | Not applicable | No scenarios presented | Rodenticide exposure level, and rodenticide exposure on low intensity development land cover class | No scenarios presented | No scenarios presented | No scenarios presented | Fisheries exploitation; fishing vulnerability (of age classes) | No scenarios presented | Three types of Low Impact Development (LID) practices (rain gardens, permeable pavements, forested riparian buffers) applied a different conversion levels. | targeted sport fish biomass | No scenarios presented | Sites, function or habitat focus | N/A | N/A | No scenarios presented |
EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-137 | EM-194 | EM-317 |
EM-422 ![]() |
EM-450 | EM-462 | EM-492 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-660 ![]() |
EM-685 |
EM-734 ![]() |
EM-842 | EM-848 | EM-876 |
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
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Method + Application | Method + Application | Method Only | Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships. |
Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | 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 | Method + Application | Method + Application |
New or Pre-existing EM?
em.detail.newOrExistHelp
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Application of existing model | New or revised model | New or revised model | New or revised 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 | WESP Deepwater Marsh | 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
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EM-94 | EM-97 | EM-137 | EM-194 | EM-317 |
EM-422 ![]() |
EM-450 | EM-462 | EM-492 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-660 ![]() |
EM-685 |
EM-734 ![]() |
EM-842 | EM-848 | EM-876 |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-254 | Doc-256 ?Comment:Document 254 was also used as a source document for this EM |
Doc-142 | Doc-190 | Doc-223 | None | Doc-303 | Doc-305 | Doc-328 | Doc-327 | Doc-2 | Doc-335 | None | None | None | None | Doc-13 | Doc-366 | None | Doc-386 | Doc-387 | Doc-390 | Doc-405 | None | None |
EM ID for related EM
em.detail.relatedEmEmIdHelp
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None | None | EM-109 | EM-142 | EM-51 | None | None | EM-403 | EM-98 | EM-447 | None | None | None | EM-584 | EM-375 | EM-377 | EM-378 | EM-884 | EM-883 | EM-887 | None | EM-682 | EM-684 | EM-683 | EM-686 | EM-718 | EM-729 | EM-743 | EM-756 | EM-757 | EM-759 | EM-760 | EM-761 | EM-763 | EM-764 | EM-766 | EM-767 | EM-751 | EM-768 | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-843 | EM-844 | EM-845 | EM-846 | EM-847 | EM-850 | None |
EM Modeling Approach
EM ID
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EM-94 | EM-97 | EM-137 | EM-194 | EM-317 |
EM-422 ![]() |
EM-450 | EM-462 | EM-492 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-660 ![]() |
EM-685 |
EM-734 ![]() |
EM-842 | EM-848 | EM-876 |
EM Temporal Extent
em.detail.tempExtentHelp
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2000 | 1980-2006 | Not applicable | 2006-2007 | 1950-2007 | 60 yr | 2006-2007, 2010 | 2006-2007, 2010 | 2006-2013 | 1986-2115 | 35 yr | Jan 1, 2009 to Dec 31, 2011 | 2008-2010 | July 1, 2011 to June 31, 2016 | 2010-2013 | 2008 | 1991-1994 | 2010-2011 |
EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | future time | Not applicable | past time | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | discrete | discrete | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | 1 | 1 | 1 | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Hour | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Year | Day | Day | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-137 | EM-194 | EM-317 |
EM-422 ![]() |
EM-450 | EM-462 | EM-492 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-660 ![]() |
EM-685 |
EM-734 ![]() |
EM-842 | EM-848 | EM-876 |
Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Watershed/Catchment/HUC | Not applicable | Physiographic or Ecological | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Geopolitical | Geopolitical | Not applicable | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Other | Geopolitical |
Spatial Extent Name
em.detail.extentNameHelp
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EU-27 | East Fork Kaskaskia River watershed basin | Not applicable | St. Croix, U.S. Virgin Islands | Puget Sound Region | San Joaquin Valley, CA | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | conterminous United States | Table Mountain National Park Marine Protected Area | Not applicable | Shayler Crossing watershed, a subwatershed of the East Fork Little Miami River Watershed | HUCS in Michigan | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | Wetlands in Idaho | Piedmont Ecoregion | Not applicable | Miami-Dade County |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100-1000 km^2 | Not applicable | 10-100 km^2 | 10,000-100,000 km^2 | 10,000-100,000 km^2 | 100-1000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | Not applicable | 10-100 ha | 100,000-1,000,000 km^2 | 10-100 ha | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | Not applicable | 1000-10,000 km^2. |
EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-137 | EM-194 | EM-317 |
EM-422 ![]() |
EM-450 | EM-462 | EM-492 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-660 ![]() |
EM-685 |
EM-734 ![]() |
EM-842 | EM-848 | EM-876 |
EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | 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 lumped (in all cases) | 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 lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | Not applicable | 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 | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) | Not applicable | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
em.detail.spGrainSizeHelp
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10 km x 10 km | 1 km^2 | 30 x 30 m | Not applicable | 200m x 200m | 14 ha | 10 m x 10 m | 10 m x 10 m | irregular | Not applicable | Not applicable | 10m x 10m | reach in HUC | by beach site | Not applicable | Not applicable | stream reach | Census block |
EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-137 | EM-194 | EM-317 |
EM-422 ![]() |
EM-450 | EM-462 | EM-492 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-660 ![]() |
EM-685 |
EM-734 ![]() |
EM-842 | EM-848 | EM-876 |
EM Computational Approach
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Analytic | Numeric | Numeric | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Numeric | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-94 | EM-97 | EM-137 | EM-194 | EM-317 |
EM-422 ![]() |
EM-450 | EM-462 | EM-492 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-660 ![]() |
EM-685 |
EM-734 ![]() |
EM-842 | EM-848 | EM-876 |
Model Calibration Reported?
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No | No | Not applicable | Yes | Yes | Unclear | Yes | Yes | No | No | Not applicable | Yes | No | Yes | No | Yes | Not applicable | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | Not applicable | Yes | No | No | No | No | No | No | Not applicable |
Yes ?Comment:Goodness of fit for calibrated (2009-2010) and observed streamflow. |
Yes | No | No | No | Not applicable | No |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None |
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None | None | None | None | None | None | None | None |
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None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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Yes | Yes | Not applicable | No | No | No | 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. |
Unclear | Yes | No | No | No | No | No | No |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | Yes | Not applicable | Yes | No | No | No | No | No | No | Not applicable | No | No | No | No | No | Yes | No |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | Unclear | Not applicable | No | No | Yes | No | No | No | No | Not applicable | No | No |
No ?Comment:n/a |
No | Yes | Yes | No |
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 | Not applicable | Not applicable | Yes | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-94 | EM-97 | EM-137 | EM-194 | EM-317 |
EM-422 ![]() |
EM-450 | EM-462 | EM-492 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-660 ![]() |
EM-685 |
EM-734 ![]() |
EM-842 | EM-848 | EM-876 |
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None | None |
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None | None |
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None | None |
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Comment:No specific location but developed in United States |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-94 | EM-97 | EM-137 | EM-194 | EM-317 |
EM-422 ![]() |
EM-450 | EM-462 | EM-492 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-660 ![]() |
EM-685 |
EM-734 ![]() |
EM-842 | EM-848 | EM-876 |
None | None | None |
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None | None |
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None |
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None | None | None |
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None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-137 | EM-194 | EM-317 |
EM-422 ![]() |
EM-450 | EM-462 | EM-492 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-660 ![]() |
EM-685 |
EM-734 ![]() |
EM-842 | EM-848 | EM-876 |
Centroid Latitude
em.detail.ddLatHelp
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50.53 | 38.69 | -9999 | 17.75 | 48 | 36.13 | 17.73 | 17.73 | 39.5 | -34.18 | Not applicable | 39.19 | 45.12 | 41.64 | 44.06 | 36.23 | Not applicable | 25.64 |
Centroid Longitude
em.detail.ddLongHelp
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7.6 | -89.1 | -9999 | -64.75 | -123 | -120 | -64.77 | -64.77 | -98.35 | 18.35 | Not applicable | -84.29 | 85.18 | -70.29 | -114.69 | -81.9 | Not applicable | -80.5 |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | Not applicable | NAD83 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Provided | Not applicable | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Not applicable | Provided | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated |
EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-137 | EM-194 | EM-317 |
EM-422 ![]() |
EM-450 | EM-462 | EM-492 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-660 ![]() |
EM-685 |
EM-734 ![]() |
EM-842 | EM-848 | EM-876 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Agroecosystems | Rivers and Streams | Ground Water | Created Greenspace | Near Coastal Marine and Estuarine | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Forests | Atmosphere | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Agroecosystems | Near Coastal Marine and Estuarine | Agroecosystems | Rivers and Streams | Ground Water | Forests | Agroecosystems | Created Greenspace | Rivers and Streams | Near Coastal Marine and Estuarine | Inland Wetlands | Grasslands | Rivers and Streams | Created Greenspace |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Streams and near upstream environments | Row crop agriculture in Kaskaskia river basin | Urban watersheds | stony coral reef | Terrestrial environment surrounding a large estuary | Agricultural region (converted desert) and terrestrial perimeter | Coral reefs | Coral reefs | Terrestrial | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | Agroecosystems | Mixed land cover suburban watershed | stream reaches | Saltwater beach | created, restored and enhanced wetlands | grasslands | benthic habitat | urban neighborhood greenspace |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is coarser 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 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 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 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 corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-137 | EM-194 | EM-317 |
EM-422 ![]() |
EM-450 | EM-462 | EM-492 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-660 ![]() |
EM-685 |
EM-734 ![]() |
EM-842 | EM-848 | EM-876 |
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Community | Guild or Assemblage | Not applicable | Individual or population, within a species | Not applicable | Guild or Assemblage | Not applicable | Individual or population, within a species | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Species |
Other (Comment) ?Comment:Community metrics of tolerance, food groups, sensitivity, taxa richness, diversity |
Not applicable |
Taxonomic level and name of organisms or groups identified
EM-94 | EM-97 | EM-137 | EM-194 | EM-317 |
EM-422 ![]() |
EM-450 | EM-462 | EM-492 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-660 ![]() |
EM-685 |
EM-734 ![]() |
EM-842 | EM-848 | EM-876 |
None Available | None Available | None Available |
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None Available |
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None Available |
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None Available |
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None Available | None Available |
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None Available | None Available |
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None Available | None Available |
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-94 | EM-97 | EM-137 | EM-194 | EM-317 |
EM-422 ![]() |
EM-450 | EM-462 | EM-492 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-660 ![]() |
EM-685 |
EM-734 ![]() |
EM-842 | EM-848 | EM-876 |
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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-94 | EM-97 | EM-137 | EM-194 | EM-317 |
EM-422 ![]() |
EM-450 | EM-462 | EM-492 |
EM-541 ![]() |
EM-549 |
EM-605 ![]() |
EM-660 ![]() |
EM-685 |
EM-734 ![]() |
EM-842 | EM-848 | EM-876 |
None |
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None | None |
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None |
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None |
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None |
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