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
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                        ? | EM-51   | EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-111   | EM-143 | EM-154 | EM-306 | EM-338   | EM-344 | EM-467   | EM-485   | EM-493 | EM-542   | EM-628 | EM-686   | EM-697   | EM-702 | 
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                        ? | EnviroAtlas-Nat. filtration-water | EnviroAtlas - Natural biological nitrogen fixation | Fodder crude protein content, Central French Alps | Pollination ES, Central French Alps | ACRU, South Africa | Birds in estuary habitats, Yaquina Estuary, WA, USA | InVEST water yield, Hood Canal, WA, USA | InVEST habitat quality | Mangrove development, Tampa Bay, FL, USA | Urban Temperature, Baltimore, MD, USA | InVEST crop pollination, California, USA | InVEST water yield, Xitiaoxi River basin, China | Yasso07 v1.0.1, Switzerland | Yasso07 v1.0.1, Switzerland, site level | EnviroAtlas-Carbon sequestered by trees | Coastal protection in Belize | SolVES, Bridger-Teton NF, WY | Estuary recreational use, Cape Cod, MA | Floral resources on landfill sites, United Kingdom | Northern Shoveler recruits, CREP wetlands, IA, USA | 
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                    EM Full Name
                
                
             
           
     
                            
                                
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                        ? | US EPA EnviroAtlas - Natural filtration (of water by tree cover); Example is shown for Durham NC and vicinity, USA | US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Fodder crude protein content, Central French Alps | Pollination ecosystem service estimated from plant functional traits, Central French Alps | ACRU (Agricultural Catchments Research Unit), South Africa | Bird use of estuarine habitats, Yaquina Estuary, WA, USA | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) water yield, Hood Canal, WA, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs) Habitat Quality | Mangrove wetland development, Tampa Bay, FL, USA | Urban Air Temperature Change, Baltimore, MD, USA | InVEST crop pollination, California, USA | InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) water yield, Xitiaoxi River basin, China | Yasso07 v1.0.1 forest litter decomposition, Switzerland | Yasso07 v1.0.1 forest litter decomposition, Switzerland, site level | US EPA EnviroAtlas - Total carbon sequestered by tree cover; Example is shown for Durham NC and vicinity, USA | Coastal Protection provided by Coral, Seagrasses and Mangroves in Belize: | SolVES, Social Values for Ecosystem Services, Bridger-Teton National Forest, WY | Estuary recreational use, Cape Cod, MA | Floral resources on landfill sites, East Midlands, United Kingdom | Northern Shoveler duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | 
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                    EM Source or Collection
                
             
           
     
                            
                            
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                        ? | US EPA | EnviroAtlas | i-Tree ? Comment:EnviroAtlas uses an application of the i-Tree Hydro model. | US EPA | EnviroAtlas | EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | US EPA | InVEST | InVEST ? Comment:From the Natural Capital Project website | US EPA | i-Tree | USDA Forest Service | InVEST | InVEST | None | None | US EPA | EnviroAtlas | i-Tree | InVEST | None | US EPA | None | None | 
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                    EM Source Document ID
                
             
           
     | 223 | 262 ? Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. | 260 | 260 | 271 | 275 | 205 | 278 | 97 | 217 | 279 | 307 | 343 | 343 | 223 ? Comment:Additional source: I-tree Eco (doc# 345). | 350 | 369 | 387 | 389 | 372 ? Comment:Document 373 is a secondary source for this EM. | 
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                        ? | US EPA Office of Research and Development - National Exposure Research Laboratory | 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. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Frazier, M. R., Lamberson, J. O. and Nelson, W. G. | 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. | Natural Capital Project | Osland, M. J., Spivak, A. C., Nestlerode, J. A., Lessmann, J. M., Almario, A. E., Heitmuller, P. T., Russell, M. J., Krauss, K. W., Alvarez, F., Dantin, D. D., Harvey, J. E., From, A. S., Cormier, N. and Stagg, C.L. | Heisler, G. M., Ellis, A., Nowak, D. and Yesilonis, I. | Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | Zhang C., Li, W., Zhang, B., and Liu, M. | Didion, M., B. Frey, N. Rogiers, and E. Thurig | Didion, M., B. Frey, N. Rogiers, and E. Thurig | US EPA Office of Research and Development - National Exposure Research Laboratory | Guannel, G., Arkema, K., Ruggiero, P., and G. Verutes | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | Mulvaney, K K., Atkinson, S.F., Merrill, N.H., Twichell, J.H., and M.J. Mazzotta | Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | 
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                        ? | 2013 | 2013 | 2011 | 2011 | 2008 | 2014 | 2013 | 2014 | 2012 | 2016 | 2009 | 2012 | 2014 | 2014 | 2013 | 2016 | 2014 | 2019 | 2013 | 2010 | 
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                        ? | EnviroAtlas - Featured Community | EnviroAtlas - National | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Mapping ecosystem services for planning and management | Intertidal habitat utilization patterns of birds in a Northeast Pacific estuary | From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA | Habitat Quality model - InVEST ver. 3.0 | Ecosystem development after mangrove wetland creation: plant–soil change across a 20-year chronosequence | Modeling and imaging land-cover influences on air-temperature in and near Baltimore, MD | Modelling pollination services across agricultural landscapes | Water yield of Xitiaoxi River basin based on InVEST modeling | Validating tree litter decomposition in the Yasso07 carbon model | Validating tree litter decomposition in the Yasso07 carbon model | EnviroAtlas - Featured Community | The Power of Three: Coral Reefs, Seagrasses and Mangroves Protect Coastal Regions and Increase Their Resilience | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | Quantifying Recreational Use of an Estuary: A case study of three bays, Cape Cod, USA | Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | 
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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 | Peer reviewed and published | Peer reviewed but unpublished (explain in Comment) | Peer reviewed and published | Peer reviewed and published | 
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                        ? | Published on US EPA EnviroAtlas website | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published on Natural Capital Project website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Draft manuscript-work progressing | Published journal manuscript | Published report | 
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                    EM ID
                
             
           
     
                            
                            
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                        ? | EM-51   | EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-111   | EM-143 | EM-154 | EM-306 | EM-338   | EM-344 | EM-467   | EM-485   | EM-493 | EM-542   | EM-628 | EM-686   | EM-697   | EM-702 | 
| https://www.epa.gov/enviroatlas | https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | http://www.naturalcapitalproject.org/models/crop_pollination.html | https://www.naturalcapitalproject.org/invest/ | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | https://www.epa.gov/enviroatlas | Not identified in paper | Not applicable | Not applicable | Not applicable | Not applicable | |
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                        ? | EnviroAtlas Team | EnviroAtlas Team ? Comment:Additional contact: Jana Compton, EPA | Sandra Lavorel | Sandra Lavorel | Roland E Schulze | M. R. Frazier ? Comment:Present address: M. R. Frazier National Center for Ecological Analysis and Synthesis, 735 State St. Suite 300, Santa Barbara, CA 93101, USA | J.E. Toft | The Natural Capital Project | Michael Osland | Gordon M. Heisler | Eric Lonsdorf | Li Wenhua | Markus Didion ? Comment:Tel.: +41 44 7392 427 | Markus Didion | EnviroAtlas Team | Greg Guannel | Benson Sherrouse | Mulvaney, Kate | Sam Tarrant | David Otis | 
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                    Contact Address
                
             
           
     | Not reported | Not reported | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | School of Bioresources Engineering and Environmental Hydrology, University of Natal, South Africa | Western Ecology Division, Office of Research and Development, U.S. Environmental Protection Agency, Pacific coastal Ecology Branch, 2111 SE marine Science Drive, Newport, OR 97365 | The Natural Capital Project, Stanford University, 371 Serra Mall, Stanford, CA 94305-5020, USA | 371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | U.S. Environmental Protection Agency, Gulf Ecology Division, gulf Breeze, FL 32561 | 5 Moon Library, c/o SUNY-ESF, Syracuse, NY 13210 | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Not reported | The Nature Conservancy, Coral Gables, FL. USA | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | US EPA, ORD, NHEERL, Atlantic Ecology Division, Narragansett, RI | RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K. | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | 
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     | enviroatlas@epa.gov | enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | schulzeR@nu.ac.za | frazier@nceas.ucsb.edu | jetoft@stanford.edu | invest@naturalcapitalproject.org | mosland@usgs.gov | gheisler@fs.fed.us | ericlonsdorf@lpzoo.org | liwh@igsnrr.ac.cn | markus.didion@wsl.ch | markus.didion@wsl.ch | enviroatlas@epa.gov | greg.guannel@gmail.com | bcsherrouse@usgs.gov | Mulvaney.Kate@epa.gov | sam.tarrant@rspb.org.uk | dotis@iastate.edu | 
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                        ? | EM-51   | EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-111   | EM-143 | EM-154 | EM-306 | EM-338   | EM-344 | EM-467   | EM-485   | EM-493 | EM-542   | EM-628 | EM-686   | EM-697   | EM-702 | 
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                        ? | The Natural Filtration model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. METADATA ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina... runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA DESCRIPTION: "The i-Tree Hydro model estimates the effects of tree and impervious cover on hourly stream flow values for a watershed (Wang et al 2008). i-Tree Hydro also estimates changes in water quality using hourly runoff estimates and mean and median national event mean concentration (EMC) values. The model was calibrated using hourly stream flow data to yield the best fit between model and measured stream flow results… After calibration, the model was run a number of times under various conditions to see how the stream flow would respond given varying tree and impervious cover in the watershed… The term event mean concentration (EMC) is a statistical parameter used to represent the flow-proportional average concentration of a given parameter during a storm event. EMC data is used for estimating pollutant loading into watersheds. The response outputs were calculated as kg of pollutant per square meter of land area for pollutants. These per square meter values were multiplied by the square meters of land area in the block group to estimate the effects at the block group level." METADATA DESCRIPTION PARAPHRASED: Changes in water quality were estimated for the following pollutants (entered as separate runs); total suspended solids (TSS), total phosphorus, soluble phosphorus, nitrites and nitrates, total Kjeldahl nitrogen (TKN), biochemical oxygen demand (BOD5), chemical oxygen demand (COD5), and copper. "Reduction in annual runoff (census block group)" variable data was derived from the EnviroAtlas water recharge coverage which used the i-Tree Hydro model. | DATA FACT SHEET: "This EnviroAtlas national map displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., fodder crude protein content), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in fodder crude protein content was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy…Fodder crude protein for each pixel was calculated and mapped using model estimates...This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on fodder protein content. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The pollination ecosystem service map was a simple sums of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to pollination ecosystem services are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions." | AUTHOR'S DESCRIPTION (Doc ID 272): "ACRU is a daily timestep, physical conceptual and multipurpose model structured to simulate impacts of land cover/ use change. The model can output, inter alia, components of runoff, irrigation supply and demand, reservoir water budgets as well as sediment and crop yields." AUTHOR'S DESCRIPTION (Doc ID 271): "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…The total benefit to people of water supply is a function of both the quantity and quality with the ecosystem playing a key role in the latter. However, due to the lack of suitable national scale data on water quality for quantifying the service, runoff was used as an estimate of the benefit where runoff is the total water yield from a watershed including surface and subsurface flow. This assumes that runoff is positively correlated with quality, which is the case in South Africa (Allanson et al., 1990)…In South Africa, water resources are mapped in water management areas called catchments (vs. watersheds) where a catchment is defined as the area of land that is drained by a single river system, including its tributaries (DWAF, 2004). There are 1946 quaternary (4th order) catchments in South Africa, the smallest is 4800 ha and the average size is 65,000 ha. Schulze (1997) modelled annual runoff for each quaternary catchment. During modelling of runoff, he used rainfall data collected over a period of more than 30 years, as well as data on other climatic factors, soil characteristics and grassland as the land cover. In this study, median annual simulated runoff was used as a measure of surface water supply. The volume of runoff per quaternary catchment was calculated for surface water supply. The range (areas with runoff of 30 million m^3 or more) and hotspots (areas with runoff of 70 million m^3 or more) were defined using a combination of statistics and expert inputs due to a lack of published thresholds in the literature." | AUTHOR'S DESCRIPTION: "To describe bird utilization patterns of intertidal habitats within Yaquina estuary, Oregon, we conducted censuses to obtain bird species and abundance data for the five dominant estuarine intertidal habitats: Zostera marina (eelgrass), Upogebia (mud shrimp)/ mudflat, Neotrypaea (ghost shrimp)/sandflat, Zostera japonica (Japanese eelgrass), and low marsh. EPFs were developed for the following metrics of bird use: standardized species richness; Shannon diversity; and density for the following four groups: all birds, all birds excluding gulls, waterfowl (ducks and geese), and shorebirds." | InVEST Water Yield and Scarcity 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 reparame-terized a set of fresh water models available in the InVEST tool (Tallis and Polasky, 2009; Kareiva et al., 2011)… We modelled discharge using the InVESTWater Yield and Scarcity model. The model estimates discharge for user-defined subwatersheds based on the average annual precipitation, annual reference evapotranspiration, and a correction factor for vegetation type, soil depth, plant available water content, land use and land cover, root depth, elevation, saturated hydraulic conductivity, and consumptive water use" (2) | Please note: This ESML entry describes an InVEST model version that was current as of 2014. More recent versions may be available at the InVEST website. AUTHORS DESCRIPTION: "The InVEST habitat quality model combines information on LULC and threats to biodiversity to produce habitat quality maps. This approach generates two key sets of information that are useful in making an initial assessment of conservation needs: the relative extent and degradation of different types of habitat types in a region and changes across time. This approach further allows rapid assessment of the status of and change in a proxy for more detailed measures of biodiversity status. If habitat changes are taken as representative of genetic, species, or ecosystem changes, the user is assuming that areas with high quality habitat will better support all levels of biodiversity and that decreases in habitat extent and quality over time means a decline in biodiversity persistence, resilience, breadth and depth in the area of decline. The habitat rarity model indicates the extent and pattern of natural land cover types on the current or a potential future landscape vis-a-vis the extent of the same natural land cover types in some baseline period. Rarity maps allow users to create a map of the rarest habitats on the landscape relative to the baseline chosen by the user to represent the mix of habitats on the landscape that is most appropriate for the study area’s native biodiversity. The model requires basic data that are available virtually everywhere in the world, making it useful in areas for which species distribution data are poor or lacking altogether. Extensive occurrence (presence/absence) data may be available in many places for current conditions. However, modeling the change in occurrence, persistence, or vulnerability of multiple species under future conditions is often impossible or infeasible. While a habitat approach leaves out the detailed species occurrence data available for current conditions, several of its components represent advances in functionality over many existing biodiversity conservation planning tools. The most significant is the ability to characterize the sensitivity of habitats types to various threats. Not all habitats are affected by all threats in the same way, and the InVEST model accounts for this variability. Further, the model allows users to estimate the relative impact of one threat over another so that threats that are more damaging to biodiversity persistence on the landscape can be represented as such. For example, grassland could be particularly sensitive to threats generated by urban areas yet moderately sensitive to threats generated by roads. In addition, the distance over which a threat will degrade natural systems can be incorporated into the model. Model assessment of the current landscape can be used as an input to a coarse-filter assessment of current conservation needs and opportunities. Model assessment of pote | ABSTRACT: "Mangrove wetland restoration and creation effortsare increasingly proposed as mechanisms to compensate for mangrove wetland losses. However, ecosystem development and functional equivalence in restored and created mangrove wetlands are poorly understood. We compared a 20-year chronosequence of created tidal wetland sites in Tampa Bay, Florida (USA) to natural reference mangrove wetlands. Across the chronosequence, our sites represent the succession from salt marsh to mangrove forest communities. Our results identify important soil and plant structural differences between the created and natural reference wetland sites; however, they also depict a positive developmental trajectory for the created wetland sites that reflects tightly coupled plant-soil development. Because upland soils and/or dredge spoils were used to create the new mangrove habitats, the soils at younger created sites and at lower depths (10–30 cm) had higher bulk densities, higher sand content, lower soil organic matter (SOM), lower total carbon (TC), and lower total nitrogen (TN) than did natural reference wetland soils. However, in the upper soil layer (0–10 cm), SOM, TC, and TN increased with created wetland site age simultaneously with mangrove forest growth. The rate of created wetland soil C accumulation was comparable to literature values for natural mangrove wetlands. Notably, the time to equivalence for the upper soil layer of created mangrove wetlands appears to be faster than for many other wetland ecosystem types. Collectively, our findings characterize the rate and trajectory of above- and below-ground changes associated with ecosystem development in created mangrove wetlands; this is valuable information for environmental managers planning to sustain existing mangrove wetlands or mitigate for mangrove wetland losses." | An empirical model for predicting below-canopy air temperature differences is developed for evaluating urban structural and vegetation influences on air temperature in and near Baltimore, MD. AUTHOR'S DESCRIPTION: "The study . . . Developed an equation for predicting air temperature at the 1.5m height as temperature difference, T, between a reference weather station and other stations in a variety of land uses. Predictor variables were derived from differences in land cover and topography along with forcing atmospheric conditions. The model method was empirical multiple linear regression analysis.. . Independent variables included remotely sensed tree cover, impervious cover, water cover, descriptors of topography, an index of thermal stability, vapor pressure deficit, and antecedent precipitation." | 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. ABSTRACT: "Background and Aims: Crop pollination by bees and other animals is an essential ecosystem service. Ensuring the maintenance of the service requires a full understanding of the contributions of landscape elements to pollinator populations and crop pollination. Here, the first quantitative model that predicts pollinator abundance on a landscape is described and tested. Methods: Using information on pollinator nesting resources, floral resources and foraging distances, the model predicts the relative abundance of pollinators within nesting habitats. From these nesting areas, it then predicts relative abundances of pollinators on the farms requiring pollination services. Model outputs are compared with data from coffee in Costa Rica, watermelon and sunflower in California and watermelon in New Jersey–Pennsylvania (NJPA). Key Results: Results from Costa Rica and California, comparing field estimates of pollinator abundance, richness or services with model estimates, are encouraging, explaining up to 80 % of variance among farms. However, the model did not predict observed pollinator abundances on NJPA, so continued model improvement and testing are necessary. The inability of the model to predict pollinator abundances in the NJPA landscape may be due to not accounting for fine-scale floral and nesting resources within the landscapes surrounding farms, rather than the logic of our model. Conclusions: The importance of fine-scale resources for pollinator service delivery was supported by sensitivity analyses indicating that the model's predictions depend largely on estimates of nesting and floral resources within crops. Despite the need for more research at the finer-scale, the approach fills an important gap by providing quantitative and mechanistic model from which to evaluate policy decisions and develop land-use plans that promote pollination conservation and service delivery. " | 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. ABSTRACT: "A water yield model based on InVEST was employed to estimate water runoff in the Xitiaoxi River basin…In order to test model accuracy the natural runoff of Xitiaoxi River was estimated based on linear regression relation of rainfall-runoff in a 'reference period'." AUTHOR'S DESCRIPTION: "The water yield model is based on the Budyko curve (1974) and annual precipitation…Water yield models require land use and land cover, precipitation, average annual potential evapotranspiration, soil depth, plant available water content, watersheds and sub-watersheds as well as a biophysical table reflecting the attributes of each land use and land cover." | ABSTRACT: "...We examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to (i) examine the effect of the application of three different published Yasso07 parameter sets on simulated decay rate; (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests;…" AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root litter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The result of the simulation was an annual estimate of the remaining fraction of the initial mass, which could then be compared with observed data." | ABSTRACT: "...We examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to... (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests; and (iii) evaluate the suitability of Yasso07 for regional and national scale applications in Swiss forests." AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "The decomposition of below- and aboveground litter was studied over 10 years on five forest sites in Switzerland…" "At the time of this study, three parameter sets have been developed and published:... (3): Rantakari et al., 2012 (henceforth P12)… For the development of P12, Rantakari et al. (2012) obtained a subset of the previously used data which was restricted to European sites." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root lit-ter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The r | The Total carbon sequestered by tree cover model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. DATA FACT SHEET: "This EnviroAtlas community map estimates the total metric tons (mt) of carbon that are removed annually from the atmosphere and sequestered in the above-ground biomass of trees in each census block group. The data for this map were derived from a high-resolution tree cover map developed by EPA. Within each census block group derived from U.S. Census data, the total amount of tree cover (m2) was determined using this remotely-sensed land cover data. The USDA Forest Service i-Tree model was used to estimate the annual carbon sequestration rate from state-based rates of kgC/m2 of tree cover/year. The state rates vary based on length of growing season and range from 0.168 kgC/m2 of tree cover/year (Alaska) to 0.581 kgC/m2 of tree cover/year (Hawaii). The national average rate is 0.306 kgC/m2 of tree cover/year. These national and state values are based on field data collected and analyzed in several cities by the U.S. Forest Service. These values were converted to metric tons of carbon removed and sequestered per year by census block group." | AUTHOR'S DESCRIPTION: "Natural habitats have the ability to protect coastal communities against the impacts of waves and storms, yet it is unclear how different habitats complement each other to reduce those impacts. Here, we investigate the individual and combined coastal protection services supplied by live corals on reefs, seagrass meadows, and mangrove forests during both non-storm and storm conditions, and under present and future sea-level conditions. Using idealized profiles of fringing and barrier reefs, we quantify the services supplied by these habitats using various metrics of inundation and erosion. We find that, together, live corals, seagrasses, and mangroves supply more protection services than any individual habitat or any combination of two habitats. Specifically, we find that, while mangroves are the most effective at protecting the coast under non-storm and storm conditions, live corals and seagrasses also moderate the impact of waves and storms, thereby further reducing the vulnerability of coastal regions. Also, in addition to structural differences, the amount of service supplied by habitats in our analysis is highly dependent on the geomorphic setting, habitat location and forcing conditions: live corals in the fringing reef profile supply more protection services than seagrasses; seagrasses in the barrier reef profile supply more protection services than live corals; and seagrasses, in our simulations, can even compensate for the long-term degradation of the barrier reef. Results of this study demonstrate the importance of taking integrated and place-based approaches when quantifying and managing for the coastal protection services supplied by ecosystems." | [ABSTRACT: " "Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders.With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems. | [ABSTRACT: "Estimates of the types and number of recreational users visiting an estuary are critical data for quantifying the value of recreation and how that value might change with variations in water quality or other management decisions. However, estimates of recreational use are minimal and conventional intercept surveys methods are often infeasible for widespread application to estuaries. Therefore, a practical observational sampling approach was developed to quantify the recreational use of an estuary without the use of surveys. Designed to be simple and fast to allow for replication, the methods involved the use of periodic instantaneous car counts multiplied by extrapolation factors derived from all-day counts. This simple sampling approach can be used to estimate visitation to diverse types of access points on an estuary in a single day as well as across multiple days. Evaluation of this method showed that when periodic counts were taken within a preferred time window (from 11am-4:30pm), the estimates were within 44 percent of actual daily visitation. These methods were applied to the Three Bays estuary system on Cape Cod, USA. The estimated combined use across all its public access sites is similar to the use at a mid-sized coastal beach, demonstrating the value of estuarine systems. Further, this study is the first to quantify the variety and magnitude of recreational uses at several different types of access points throughout the estuary using observational methods. This model focused on the various use by access point type (beaches, landings and way to water, boat use). This work can be transferred to the many small coastal access points used for recreation across New England and beyond." ] | ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level… A “floral cover” method to represent available floral resources was used which combines floral abundance with inflorescence size. Mean area of the floral unit from above was measured for each flowering plant species and then multiplied by their frequencies." "Insect pollinated flowering plant species composition and floral abundance between sites by type were represented by non-metric multidimensional scaling (NMDS)...This method is sensitive to showing outliers and the distance between points shows the relative similarity (McCune & Grace 2002; Ollerton et al. 2009)." (This data is not entered into ESML) | ABSTRACT: "Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2*R*n where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = H*Z*B/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632). | 
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                        ? | None identified | None Identified | None identified | None identified | None identified | None identified | Land use change | None identified | Not applicable | None identified | None identified | None identified | None identified | None identified | None identified | Future rock lobster fisheries management | None | None identified | None identified | None identified | 
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     | No additional description provided | No additional description provided | Elevation ranges from 1552 to 2442 m, on predominantely south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | Estuarine intertidal, eelgrass, mudflat, sandflat and low marsh | Not additional description provided | Not applicable | mangrove forest,Salt marsh, estuary, sea level, | One airport site, one urban site, one site in deciduous leaf litter, and four sites in short grass ground cover. Measured sky view percentages ranged from 6% at the woods site, to 96% at the rural open site. | No additional description provided | Mean elevation of 266 m, with southwestern mountainous area. Subtropical monsoon climate. Annual average temperature of 12.2-15.6 °C. Annual mean precipitation is 1500 mm, and over 70% of rainfall occurs in the flood season (Apr-Oct). | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | No additional description provided | barrier reef and fringing reef in nearshore coastal marine system | Rocky mountain conifer forests | None identified | No additional description provided | Prairie Pothole Region of Iowa | 
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                        ? | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Future land use and land cover; climate change | Potential land Use Land Class (LULC) future and baseline ? Comment:model requires current landuse but can compare to baseline (prior to intensive management of the land) and potential future landuse. These are the two scenarios suggested in the documentation. | Not applicable | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented ? Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). | No scenarios presented | No scenarios presented | Reef type, Sea level increase, storm conditions, seagrass conditions, coral conditions, vegetation types and conditions | N/A | N/A | No scenarios presented | No scenarios presented | 
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                        ? | EM-51   | EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-111   | EM-143 | EM-154 | EM-306 | EM-338   | EM-344 | EM-467   | EM-485   | EM-493 | EM-542   | EM-628 | EM-686   | EM-697   | EM-702 | 
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                        ? | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist)  	     View EM Runs ? Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). | Method + Application (multiple runs exist)  	     View EM Runs ? Comment:Model runs are for different sites (Beatenberg, Vordemwald, Bettlachstock, Schanis, and Novaggio) differentiated by climate and forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | Method + Application | 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 | 
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                        ? | Application of existing model | New or revised model | New or revised model | New or revised 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 | Application of existing model | Application of existing model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | 
Related EMs (for example, other versions or derivations of this EM) described in ESML
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                        ? | EM-51   | EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-111   | EM-143 | EM-154 | EM-306 | EM-338   | EM-344 | EM-467   | EM-485   | EM-493 | EM-542   | EM-628 | EM-686   | EM-697   | EM-702 | 
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                        ? | Doc-198 | Doc-346 | Doc-347 ? Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. | Doc-260 | Doc-269 | Doc-260 | Doc-272 ? Comment:Doc ID 272 was also used as a source document for this EM | None | Doc-280 | Doc-307 | Doc-311 | Doc-338 | Doc-309 | None | Doc-220 | Doc-219 | Doc-218 | Doc-279 | Doc-280 | Doc-311 | Doc-338 | Doc-205 | Doc-342 | Doc-344 | Doc-342 | Doc-343 | Doc-345 | None | None | None | None | Doc-372 | Doc-373 | 
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                        ? | EM-137 | EM-142 | None | EM-65 | EM-66 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-83 | None | None | EM-148 | EM-344 | EM-368 | EM-437 | EM-345 | None | None | EM-340 | EM-339 | EM-148 | EM-368 | EM-437 | EM-111 | EM-466 | EM-469 | EM-480 | EM-485 | EM-466 | EM-467 | EM-469 | EM-480 | None | None | EM-629 | EM-626 | EM-682 | EM-684 | EM-685 | EM-709 | EM-705 | EM-704 | EM-703 | EM-701 | EM-700 | EM-632 | 
EM Modeling Approach
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                        ? | EM-51   | EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-111   | EM-143 | EM-154 | EM-306 | EM-338   | EM-344 | EM-467   | EM-485   | EM-493 | EM-542   | EM-628 | EM-686   | EM-697   | EM-702 | 
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                    EM Temporal Extent
                
                
             
           
     
                            
                                
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                        ? | 1999-2010 | 2006-2010 | 2007-2009 | Not reported | 1950-1993 | December 2007 - November 2008 | 2005-7; 2035-45 | Not applicable | 1990-2010 | May 5-Sept 30 2006 | 2001-2002 | 2003-2007 | 1993-2013 | 2000-2010 | 2010-2013 | 2005-2013 | 2004-2008 | Summer 2017 | 2007-2008 | 1987-2007 | 
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                    EM Time Dependence
                
                
             
           
     
                            
                                
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                        ? | time-stationary ? Comment:The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, operated on an hourly timestep. The final annual flow parameter however is time stationary. | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | 
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                        ? | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | future time | future time | Not applicable | Not applicable | future time | future time | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable | 
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                    EM Time Continuity
                
                
             
           
     
                            
                                
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                        ? | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | continuous | discrete | Not applicable | Not applicable | discrete | discrete | Not applicable | discrete | Not applicable | discrete | Not applicable | Not applicable | 
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                        ? | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | 1 | Not applicable | 1 | Not applicable | 1 | Not applicable | Not applicable | 
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                    EM Temporal Grain Size Unit
                
                
             
           
     
                            
                                
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                        ? | Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Hour | Not applicable | Not applicable | Year | Year | Not applicable | Second | Not applicable | Day | Not applicable | Not applicable | 
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                                em.detail.idHelp
                            
                        ? | EM-51   | EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-111   | EM-143 | EM-154 | EM-306 | EM-338   | EM-344 | EM-467   | EM-485   | EM-493 | EM-542   | EM-628 | EM-686   | EM-697   | EM-702 | 
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                    Bounding Type
                
             
           
     
                            
                            
                                em.detail.boundingTypeHelp
                            
                        ? | Geopolitical | Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Physiographic or ecological | Watershed/Catchment/HUC | No location (no locational reference given) | Physiographic or Ecological | Geopolitical | Other | Watershed/Catchment/HUC | Geopolitical | Geopolitical | Geopolitical | Geopolitical | Geopolitical | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | 
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                    Spatial Extent Name
                
             
           
     
                            
                            
                                em.detail.extentNameHelp
                            
                        ? | Durham, NC and vicinity | counterminous United States | Central French Alps | Central French Alps | South Africa | Yaquina Estuary (intertidal), Oregon, USA | Hood Canal | Not applicable | Tampa Bay | Baltimore, MD | Agricultural landscape, Yolo County, Central Valley | Xitiaoxi River basin | Switzerland | Switzerland | Durham NC and vicinity | Coast of Belize | National Park | Three Bays, Cape Cod | East Midlands | CREP (Conservation Reserve Enhancement Program | 
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                    Spatial Extent Area (Magnitude)
                
             
           
     
                            
                            
                                em.detail.extentAreaHelp
                            
                        ? | 100-1000 km^2 | >1,000,000 km^2 | 10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | 1-10 km^2 | 100,000-1,000,000 km^2 | Not applicable | 100-1000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | 10,000-100,000 km^2 | 10,000-100,000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. | 10,000-100,000 km^2 | 
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-51   | EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-111   | EM-143 | EM-154 | EM-306 | EM-338   | EM-344 | EM-467   | EM-485   | EM-493 | EM-542   | EM-628 | EM-686   | EM-697   | EM-702 | 
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                    EM Spatial Distribution
                
                
             
           
     
                            
                                
                                    em.detail.distributeLumpHelp
                                
                                
                            
                            
                        ? | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) ? Comment:Watersheds (12-digit HUCs). | 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) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) ? Comment:Census block groups | 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) | 
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                    Spatial Grain Type
                
             
           
     
                            
                            
                                em.detail.spGrainTypeHelp
                            
                        ? | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | other (habitat type) | 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 | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | 
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                    Spatial Grain Size
                
             
           
     
                            
                            
                                em.detail.spGrainSizeHelp
                            
                        ? | irregular | irregular | 20 m x 20 m | 20 m x 20 m | Distributed by catchments with average size of 65,000 ha | 0.87-104.29 ha | 30 m x 30 m | LULC pixel size | m^2 | 10m x 10m | 30 m x 30 m | Not reported | 5 sites | Not applicable | irregular | 1 meter | 30m2 | beach length | multiple unrelated locations | multiple, individual, irregular sites | 
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-51   | EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-111   | EM-143 | EM-154 | EM-306 | EM-338   | EM-344 | EM-467   | EM-485   | EM-493 | EM-542   | EM-628 | EM-686   | EM-697   | EM-702 | 
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                    EM Computational Approach
                
                
             
           
     
                            
                                
                                    em.detail.emComputationalApproachHelp
                                
                                
                            
                            
                        ? | Analytic ? Comment:The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, was numeric. The final parameter however did not require iteration. | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Numeric | Analytic | Numeric | Numeric | Analytic | Analytic | 
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                    EM Determinism
                
                
             
           
     
                            
                                
                                    em.detail.deterStochHelp
                                
                                
                            
                            
                        ? | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | 
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                    Statistical Estimation of EM
                
             
           
     
                            
                            
                                em.detail.statisticalEstimationHelp
                            
                        ? | 
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-51   | EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-111   | EM-143 | EM-154 | EM-306 | EM-338   | EM-344 | EM-467   | EM-485   | EM-493 | EM-542   | EM-628 | EM-686   | EM-697   | EM-702 | 
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                    Model Calibration Reported?
                
             
           
     
                            
                            
                                em.detail.calibrationHelp
                            
                        ? | Unclear | No | No | No | No | Unclear | Yes | Not applicable | No | Yes | Unclear | Yes | No | No | No | No | No | Yes | Not applicable | Unclear | 
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                    Model Goodness of Fit Reported?
                
                
             
           
     
                            
                                
                                    em.detail.goodnessFitHelp
                                
                                
                            
                            
                        ? | No | No | Yes | No | No | No | No | Not applicable | No | Yes | No | No | No | No | No | No | Yes | No | Not applicable | No | 
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                    Goodness of Fit (metric| value | unit)
                
                
             
           
     
                            
                                
                                    em.detail.goodnessFitValuesHelp
                                
                                
                            
                            
                        ? | None | None | 
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                    Model Operational Validation Reported?
                
                
             
           
     
                            
                                
                                    em.detail.validationHelp
                                
                                
                            
                            
                        ? | Unclear | No | Yes | No | No | No | Yes | Not applicable | No | No | Yes ? Comment:Performed just for "Total pollinator abundance service score". | No | Yes | Yes | No | No ? Comment:Used the SWAN model (see below for referenece) with Generation 1 or 2 wind-wave formulations to validate the wave development portion of the model. Booij N, Ris RC, Holthuijsen LH. A third-generation wave model for coastal regions 1. Model description and validation. J Geophys Res. American Geophysical Union; 1999;104: 7649?7666. | No | No | Not applicable | No | 
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                    Model Uncertainty Analysis Reported?
                
                
             
           
     
                            
                                
                                    em.detail.uncertaintyAnalysisHelp
                                
                                
                            
                            
                        ? | Unclear | No | No | No | No | No | No | Not applicable | Yes | No | No | No | No | Yes | No | No | No | No | Not applicable | No | 
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                    Model Sensitivity Analysis Reported?
                
                
             
           
     
                            
                                
                                    em.detail.sensAnalysisHelp
                                
                                
                            
                            
                        ? | Unclear | No | No | No | No | No | Yes | Not applicable | Yes | No | No | Yes | No | No | No | No | No | No | Not applicable | No | 
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                    Model Sensitivity Analysis Include Interactions?
                
             
           
     
                            
                            
                                em.detail.interactionConsiderHelp
                            
                        ? | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | No | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-51   | EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-111   | EM-143 | EM-154 | EM-306 | EM-338   | EM-344 | EM-467   | EM-485   | EM-493 | EM-542   | EM-628 | EM-686   | EM-697   | EM-702 | 
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-51   | EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-111   | EM-143 | EM-154 | EM-306 | EM-338   | EM-344 | EM-467   | EM-485   | EM-493 | EM-542   | EM-628 | EM-686   | EM-697   | EM-702 | 
| None | None | None | None | None | 
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 | None | 
 Comment:Realm: Tropical Atlantic Region: West Tropical Atlantic Province: Tropical Northwestern Atlantic Ecoregion: Floridian | None | None | None | None | None | None | 
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Centroid Lat/Long (Decimal Degree)
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-51   | EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-111   | EM-143 | EM-154 | EM-306 | EM-338   | EM-344 | EM-467   | EM-485   | EM-493 | EM-542   | EM-628 | EM-686   | EM-697   | EM-702 | 
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                    Centroid Latitude
                
                
             
           
     
                            
                                
                                    em.detail.ddLatHelp
                                
                                
                            
                            
                        ? | 35.99 | 39.5 | 45.05 | 45.05 | -30 | 44.62 | 47.8 | -9999 | 27.8 | 39.28 | 38.7 | 30.55 | 46.82 | 46.82 | 35.99 | 18.63 | 43.93 | 41.62 | 52.22 | 42.62 | 
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                    Centroid Longitude
                
                
             
           
     
                            
                                
                                    em.detail.ddLongHelp
                                
                                
                            
                            
                        ? | -78.96 | -98.35 | 6.4 | 6.4 | 25 | -124.06 | -122.7 | -9999 | -82.4 | -76.62 | -121.8 | 119.5 | 8.23 | 8.23 | -78.96 | -88.22 | 110.24 | -70.42 | -0.91 | -93.84 | 
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                    Centroid Datum
                
                
             
           
     
                            
                                
                                    em.detail.datumHelp
                                
                                
                            
                            
                        ? | None provided | WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | 
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                    Centroid Coordinates Status
                
                
             
           
     
                            
                                
                                    em.detail.coordinateStatusHelp
                                
                                
                            
                            
                        ? | Estimated | Estimated | Provided | Provided | Estimated | Provided | Estimated | Not applicable | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | 
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-51   | EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-111   | EM-143 | EM-154 | EM-306 | EM-338   | EM-344 | EM-467   | EM-485   | EM-493 | EM-542   | EM-628 | EM-686   | EM-697   | EM-702 | 
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                    EM Environmental Sub-Class
                
                
             
           
     
                            
                                
                                    em.detail.emEnvironmentalSubclassHelp
                                
                                
                            
                            
                        ? | Rivers and Streams | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Atmosphere | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Forests | Forests | Created Greenspace | Atmosphere | Near Coastal Marine and Estuarine | Forests | Near Coastal Marine and Estuarine | Created Greenspace | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | 
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                    Specific Environment Type
                
                
             
           
     
                            
                                
                                    em.detail.specificEnvTypeHelp
                                
                                
                            
                            
                        ? | Urban areas including streams | Terrestrial | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Not reported | Estuarine intertidal | glacier-carved saltwater fjord | Not applicable | Created Mangrove wetlands | Urban landscape and surrounding area | Cropland and surrounding landscape | Watershed | forests | forests | Urban and vicinity | coral reefs | Montain forest | Beaches | restored landfills and grasslands | Wetlands buffered by grassland within agroecosystems | 
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                    EM Ecological Scale
                
                
             
           
     
                            
                                
                                    em.detail.ecoScaleHelp
                                
                                
                            
                            
                        ? | Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale 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 corresponds to the Environmental Sub-class | 
Scale of differentiation of organisms modeled
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-51   | EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-111   | EM-143 | EM-154 | EM-306 | EM-338   | EM-344 | EM-467   | EM-485   | EM-493 | EM-542   | EM-628 | EM-686   | EM-697   | EM-702 | 
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                    EM Organismal Scale
                
                
             
           
     
                            
                                
                                    em.detail.orgScaleHelp
                                
                                
                            
                            
                        ? | Not applicable | Not applicable | Community | Community | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Not applicable | Species | Not applicable | Community | Community | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Individual or population, within a species | Individual or population, within a species | 
Taxonomic level and name of organisms or groups identified
| EM-51   | EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-111   | EM-143 | EM-154 | EM-306 | EM-338   | EM-344 | EM-467   | EM-485   | EM-493 | EM-542   | EM-628 | EM-686   | EM-697   | EM-702 | 
| None Available | None Available | None Available | None Available | None Available | 
 | None Available | None Available | 
 | None Available | 
 | None Available | None Available | None Available | None Available | None Available | None Available | 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-51   | EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-111   | EM-143 | EM-154 | EM-306 | EM-338   | EM-344 | EM-467   | EM-485   | EM-493 | EM-542   | EM-628 | EM-686   | EM-697   | EM-702 | 
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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-51   | EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-111   | EM-143 | EM-154 | EM-306 | EM-338   | EM-344 | EM-467   | EM-485   | EM-493 | EM-542   | EM-628 | EM-686   | EM-697   | EM-702 | 
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