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 ID
                
                
             
           
     
                            
                                
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                        ? | EM-66 | EM-137 | EM-154 | EM-349   | EM-367 | EM-380   | EM-417 | EM-421 | EM-444 | EM-457 | EM-467   | EM-485   | EM-492 | EM-541   | EM-598 | EM-603 | EM-604 | EM-684 | EM-699 | EM-718   | EM-837 | EM-885 | EM-887 | EM-961 | 
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                    EM Short Name
                
             
           
     
                            
                            
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                        ? | Litter biomass production, Central French Alps | i-Tree Hydro v4.0 | Mangrove development, Tampa Bay, FL, USA | InVEST (v1.004) Carbon, Indonesia | InVEST Coastal Blue Carbon | VELMA plant-soil, Oregon, USA | SWAT, Guanica Bay, Puerto Rico, USA | Water storage in Lago Lucchetti, Puerto Rico, USA | State of the reef index, St. Croix, USVI | Visitation to reef dive sites, St. Croix, USVI | Yasso07 v1.0.1, Switzerland | Yasso07 v1.0.1, Switzerland, site level | EnviroAtlas - Restorable wetlands | InVEST fisheries, lobster, South Africa | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | Chinook salmon value, Yaquina Bay, OR | Chinook salmon value (household), Yaquina Bay, OR | Beach visitation, Barnstable, MA, USA | Fish species richness, St. John, USVI, USA | WESP: Riparian & stream habitat, ID, USA | Bird species diversity on restored landfills, UK | Regional Human well being index for U.S. | VELMA v. 2.0 disturbance | Drainage water recycling, Midwest, USA | 
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                    EM Full Name
                
                
             
           
     
                            
                                
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                        ? | Litter biomass production, Central French Alps | i-Tree Hydro v4.0 (default data option) | Mangrove wetland development, Tampa Bay, FL, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004) carbon storage and sequestration, Sumatra, Indonesia | InVEST v3.0 Coastal Blue Carbon | VELMA (Visualizing Ecosystems for Land Management Assessments) plant-soil, Oregon, USA | SWAT (Soil and Water Assessment Tool) Guánica Bay, Puerto Rico, USA | Water storage in Lago Lucchetti, Puerto Rico, USA | State of the reef index, St. Croix, USVI | Visitation to dive sites (reef), St. Croix, USVI | Yasso07 v1.0.1 forest litter decomposition, Switzerland | Yasso07 v1.0.1 forest litter decomposition, Switzerland, site level | US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa | DeNitrification-DeComposition simulation of N2O flux Ireland | Economic value of Chinook salmon by angler effort method, Yaquina Bay, OR | Economic value of Chinook salmon per household method, Yaquina Bay, OR | Beach visitation, Barnstable, Massachusetts, USA | Fish species richness, St. John, USVI, USA | WESP: Riparian and stream habitat focus projects, ID, USA | Bird species diversity on restored landfills compared to paired reference sites, East Midlands, UK | Human well being index for geographic regions, United States | VELMA (Visualizing Ecosystems for Land Management Assessment) version 2.0 disturbance | Drainage water recycling, Midwest, US | 
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                    EM Source or Collection
                
             
           
     
                            
                            
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                        ? | EU Biodiversity Action 5 | i-Tree | USDA Forest Service | US EPA | InVEST | InVEST | US EPA | US EPA | US EPA | US EPA | US EPA | None | None | US EPA | EnviroAtlas | InVEST | None | US EPA | US EPA | US EPA | None | None | None | US EPA | US EPA | None | 
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                    EM Source Document ID
                
             
           
     | 260 | 198 | 97 | 309 | 310 | 317 | 334 | 336 | 335 | 335 | 343 | 343 | 262 | 349 ? Comment:Supplemented with the InVEST Users Guide fisheries. | 358 | 324 | 324 | 386 | 355 | 393 ? Comment:Additional data came from electronic appendix provided by author Chris Murphy. | 406 | 421 | 366 | 446 | 
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                    Document Author
                
                
             
           
     
                            
                                
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                        ? | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | USDA Forest Service | 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. | Bhagabati, N. K., Ricketts, T., Sulistyawan, T. B. S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A., Tallis, H., and Wolney, S. | Natural Capital Project | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Hu, W. and Y. Yuan | Bousquin, J., W.S. Fisher, J. Carriger, and E. Huertas | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | 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 | Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Murphy, C. and T. Weekley | Rahman, M. L., S. Tarrant, D. McCollin, and J. Ollerton | Smith, L.M., Harwell, L.C., Summers, J.K., Smith, H.M., Wade, C.M., Straub, K.R. and J.L. Case | McKane, R. B., A. Brookes, K. Djang, M. Stieglitz, A. G. Abdelnour, F. Pan, J. J. Halama, P. B. Pettus and D. L. Phillips | Reinhart, B.D., Frankenberger, J.R., Hay, C.H., and Helmers, J.M. | 
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                    Document Year
                
                
             
           
     
                            
                                
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                        ? | 2011 | Not Reported | 2012 | 2014 | 2014 | 2013 | 2013 | 2014 | 2014 | 2014 | 2014 | 2014 | 2013 | 2018 | 2010 | 2012 | 2012 | 2018 | 2007 | 2012 | 2011 | 2014 | 2014 | 2019 | 
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                    Document Title
                
             
           
     
                            
                            
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                        ? | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | i-Tree Hydro User's Manual v. 4.0 | Ecosystem development after mangrove wetland creation: plant–soil change across a 20-year chronosequence | Ecosystem services reinforce Sumatran tiger conservation in land use plans | Blue Carbon model - InVEST (v3.0) | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | Evaluation of Soil Erosion and Sediment Yield for the Ridge Watersheds in the Guanica Bay Watershed, Puerto Rico, Using the SWAT Model | A Bayesian belief network approach to explore alternative decisions for sediment control and water storage capacity at Lago Lucchetti, Puerto Rico | 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 | Validating tree litter decomposition in the Yasso07 carbon model | Validating tree litter decomposition in the Yasso07 carbon model | EnviroAtlas - National | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals | Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | The conservation value of restored landfill sites in the East Midlands, UK for supporting bird communities in the East Midlands, UK for supporting bird communities | A U.S. Human Well-being index (HWBI) for multiple scales: linking service provisioning to human well-being endpoints (2000-2010) | VELMA Version 2.0 User Manual and Technical Documentation | Simulated water quality and irrigation benefits from drainage wter recycling at two tile-drained sites in the U.S. Midwest | 
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                    Document Status
                
             
           
     
                            
                            
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                        ? | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Documented, not peer reviewed | 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 and published | Peer reviewed and published | 
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                    Comments on Status
                
             
           
     
                            
                            
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                        ? | Published journal manuscript | Webpage | Published journal manuscript | Published journal manuscript | other | Published journal manuscript | Published EPA report | Published EPA report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published EPA report | Published report | Published journal manuscript | 
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                    EM ID
                
             
           
     
                            
                            
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                        ? | EM-66 | EM-137 | EM-154 | EM-349   | EM-367 | EM-380   | EM-417 | EM-421 | EM-444 | EM-457 | EM-467   | EM-485   | EM-492 | EM-541   | EM-598 | EM-603 | EM-604 | EM-684 | EM-699 | EM-718   | EM-837 | EM-885 | EM-887 | EM-961 | 
| Not applicable | http://www.itreetools.org | Not applicable | https://www.naturalcapitalproject.org/invest/ | http://ncp-dev.stanford.edu/~dataportal/invest-releases/documentation/current_release/blue_carbon.html#running-the-model | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable | Not applicable | Not applicable | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | https://www.epa.gov/enviroatlas | https://www.naturalcapitalproject.org/invest/ | http://www.dndc.sr.unh.edu | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | Not applicable | |
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                    Contact Name
                
                
             
           
     
                            
                                
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                        ? | Sandra Lavorel | Not applicable | Michael Osland | Nirmal K. Bhagabati | Gregg Verutes | Alex Abdelnour | Yongping Yuan | Susan Yee | Susan H. Yee | Susan H. Yee | Markus Didion ? Comment:Tel.: +41 44 7392 427 | Markus Didion | EnviroAtlas Team | Michelle Ward | M. Abdalla | Stephen Jordan | Stephen Jordan | Kate K, Mulvaney | Simon Pittman | Chris Murphy | Lutfor Rahman | Lisa Smith | Robert B. McKane | Benjamin Reinhart | 
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                    Contact Address
                
             
           
     | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Not applicable | U.S. Environmental Protection Agency, Gulf Ecology Division, gulf Breeze, FL 32561 | The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | Stanford University | Dept. of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | USEPA, ORD, NERL, Environmental sciences Division, Las Vegas, Nevada | NHEERL Gulf Ecology Division, Gulf Breeze FL 32561 | 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 | 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 | ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA | U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA | Not reported | 1305 East-West Highway, Silver Spring, MD 20910, USA | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Landscape and Biodiversity Research Group, School of Science and Technology, The University of Northampton, Avenue Campus, Northampton NN2 6JD, UK | 1 Sabine Island Dr, Gulf Breeze, FL 32561 | U.S. EPA, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon 97333 | Agricultural & Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA | 
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                    Contact Email
                
             
           
     | sandra.lavorel@ujf-grenoble.fr | Not applicable | mosland@usgs.gov | nirmal.bhagabati@wwfus.org | gverutes@stanford.edu | abdelnouralex@gmail.com | Yuan.Yongping@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | markus.didion@wsl.ch | markus.didion@wsl.ch | enviroatlas@epa.gov | m.ward@uq.edu.au | abdallm@tcd.ie | jordan.steve@epa.gov | jordan.steve@epa.gov | Mulvaney.Kate@EPA.gov | simon.pittman@noaa.gov | chris.murphy@idfg.idaho.gov | lutfor.rahman@northampton.ac.uk | smith.lisa@epa.gov | mckane.bob@epa.gov | breinhar@purdue.edu | 
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                    EM ID
                
             
           
     
                            
                            
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                        ? | EM-66 | EM-137 | EM-154 | EM-349   | EM-367 | EM-380   | EM-417 | EM-421 | EM-444 | EM-457 | EM-467   | EM-485   | EM-492 | EM-541   | EM-598 | EM-603 | EM-604 | EM-684 | EM-699 | EM-718   | EM-837 | EM-885 | EM-887 | EM-961 | 
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                    Summary Description
                
                
             
           
     
                            
                                
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                        ? | 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., litter biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in litter biomass production 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…Litter biomass production 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 litter mass. 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: "i-Tree Hydro is the first urban hydrology model that is specifically designed to model vegetation effects and to be calibrated against measured stream flow data. It is designed to model the effects of changes in urban tree cover and impervious surfaces on hourly stream flows and water quality at the watershed level." AUTHOR'S DESCRIPTION: "The purpose of i-Tree Hydro is to simulate hourly changes in stream flow (and water quality) given changes in tree and impervious cover in the watershed. The following is an overview of the process: 1) Determine your watershed of analysis and stream gauge station. i-Tree Hydro works on a watershed basis with the watershed determined as the total drainage area upstream from a measured stream gauge. Stream gauge availability varies. 2) Download national digital elevation data. Once the area and location of the watershed are known, digital elevation data are downloaded from the USGS for an area that encompasses the entire watershed. ArcGIS software is then used to create a digital elevation map and to determine the exact boundary for the watershed upstream from the gauge station location. 3) Determine cover attributes of the watershed and gather other required data. i-Tree Canopy and other sources can be used to determine the tree cover, shrub cover, impervious surface and other cover types. Information about other aspects of the watershed such as proportion of evergreen trees and shrubs, leaf area index, and a variety of hydrologic parameters must be collected. 4) Get started with Hydro. Once these input data are ready, they are loaded into Hydro to begin analysis. 5) Calibrate the model. The Hydro model contains an auto-calibration routine that tries to find the best fit between the stream flow predicted by the model and the stream flow measured at the stream gauge station given the various inputs. The model can also be manually calibrated to improve the fit by changing the parameters as needed. 6) Model new scenarios: Once the model is properly calibrated, tree and impervious cover parameters can be changed to illustrate the impact on stream flow and water quality." | ABSTRACT: "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." | 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: "...Here we use simple spatial analyses on readily available datasets to compare the distribution of five ecosystem services with tiger habitat in central Sumatra. We assessed services and habitat in 2008 and the changes in these variables under two future scenarios: a conservation-friendly Green Vision, and a Spatial Plan developed by the Indonesian government..." AUTHOR'S DESCRIPTION: "We used a modeling tool, InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004; Tallis et al., 2010), to map and quantify tiger habitat quality and five ecosystem services. InVEST maps ecosystem services and the quality of species habitat as production functions of LULC using simple biophysical models. Models were parameterized using data from regional agencies, literature surveys, global databases, site visits and prior field experience (Table 1)... We mapped biomass carbon by assigning carbon values (in ton ha_1) for aboveground, belowground, and dead organic matter to each LULC class based on values from literature, as described in Tallis et al. (2010). We mapped soil carbon separately, as large quantities of carbon are stored in peat soil (Page et al., 2011). We estimated total losses in peat carbon over 50 years into the future scenarios, using reported annual emission rates for specific LULC transitions on peat (Uryu et al., 2008)...Although InVEST reports ecosystem services in biophysical units, its simple models are best suited to understanding broad patterns of spatial variation (Tallis and Polasky, 2011), rather than for precise quantification. Additionally, we lacked field measurements against which to calibrate our outputs. Therefore, we focused on relative spatial distribution across the landscape, and relative change to scenarios." | 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. "InVEST Coastal Blue Carbon models the carbon cycle through a bookkeeping-type approach (Houghton, 2003). This approach simplifies the carbon cycle by accounting for storage in four main pools (aboveground biomass, belowground biomass, standing dead carbon and sediment carbon… Accumulation of carbon in coastal habitats occurs primarily in sediments (Pendleton et al., 2012). The model requires users to provide maps of coastal ecosystems that store carbon, such as mangroves and seagrasses. Users must also provide data on the amount of carbon stored in the four carbon pools and the rate of annual carbon accumulation in the sediments. If local information is not available, users can draw on the global database of values for carbon stocks and accumulation rates sourced from the peer-reviewed literature that is included in the model. If data from field studies or other local sources are available, these values should be used instead of those in the global database. The model requires land cover maps, which represent changes in human use patterns in coastal areas or changes to sea level, to estimate the amount of carbon lost or gained over a specified period of time. The model quantifies carbon storage across the land or seascape by summing the carbon stored in these four carbon pools. | ABSTRACT: "We used a new ecohydrological model, Visualizing Ecosystems for Land Management Assessments (VELMA), to analyze the effects of forest harvest on catchment carbon and nitrogen dynamics. We applied the model to a 10 ha headwater catchment in the western Oregon Cascade Range where two major disturbance events have occurred during the past 500 years: a stand-replacing fire circa 1525 and a clear-cut in 1975. Hydrological and biogeochemical data from this site and other Pacific Northwest forest ecosystems were used to calibrate the model. Model parameters were first calibrated to simulate the postfire buildup of ecosystem carbon and nitrogen stocks in plants and soil from 1525 to 1969, the year when stream flow and chemistry measurements were begun. Thereafter, the model was used to simulate old-growth (1969–1974) and postharvest (1975–2008) temporal changes in carbon and nitrogen dynamics…" AUTHOR'S DESCRIPTION: "The soil column model consists of three coupled submodels:...a plant-soil model (Figure (A3)) that simulates ecosystem carbon storage and the cycling of C and N between a plant biomass layer and the active soil pools. Specifically, the plant-soil model simulates the interaction among aboveground plant biomass, soil organic carbon (SOC), soil nitrogen including dissolved nitrate (NO3), ammonium (NH4), and organic nitrogen, as well as DOC (equations (A7)–(A12)). Daily atmospheric inputs of wet and dry nitrogen deposition are accounted for in the ammonium pool of the shallow soil layer (equation (A13)). Uptake of ammonium and nitrate by plants is modeled using a Type II Michaelis-Menten function (equation (A14)). Loss of plant biomass is simulated through a density-dependent mortality. The mortality rate and the nitrogen uptake rate mimic the exponential increase in biomass mortality and the accelerated growth rate, respectively, as plants go through succession and reach equilibrium (equations (A14)–(A18)). Vertical transport of nutrients from one layer to another in a soil column is a function of water drainage (equations (A19)–(A22)). Decomposition of SOC follows first-order kinetics controlled by soil temperature and moisture content as described in the terrestrial ecosystem model (TEM) of Raich et al. [1991] (equations (A23)–(A26)). Nitrification (equations (A27)–(A30)) and denitrification (equations (A31)–(A34)) were simulated using the equations from the generalized model of N2 and N2O production of Parton et al. [1996, 2001] and Del Grosso et al. [2000]. [12] The soil column model is placed within a catchment framework to create a spatially distributed model applicable to watersheds and landscapes. Adjacent soil columns interact with each other through the downslope lateral transport of water and nutrients (Figure (A1)). Surface and subsurface lateral flow are routed using a multiple flow direction method [Freeman, 1991; Quinn et al., 1991]. As with vertical drainage of soil water, lateral subsurface downslope flow i | AUTHOR'S DESCRIPTION: " SWAT is a physically-based continuous watershed simulation model that operates on a daily time step. It is designed for long-term simulations. The U.S. Department of Agriculture-Agriculture Research Station (USDA-ARS) Grassland, Soil and Water Research Laboratory in Temple, Texas created SWAT in the early 1990s. It has undergone continual review and expansion of capabilities since it was created (Arnold et al., 1998; Neitsch, et al., 2011a and b). This model has the ability to predict changes in water, sediment, nutrient and pesticide loads with respect to the different management conditions in watershed. Major components of the SWAT model include hydrology, weather, erosion, soil temperature, crop growth, nutrients, pesticides and agricultural management practices (Neitsch et al., 2011b). SWAT subdivides a watershed into multiple sub-watersheds, and the subwatersheds are further divided into Hydrologic Response Units (HRUs) that consist of homogeneous land use, soils, slope, and management (Gassman et al., 2007; Neitsch, et al., 2011b; Williams et al., 2008). | AUTHORS DESCRIPTION: " A Bayesian belief network (BBN) was developed to characterize the effects of sediment accumulation on the water storage capacity of Lago Lucchetti (located in southwest Puerto Rico) and to forecast the life expectancy (usefulness) of the reservoir under different management scenarios. The conceptual approach included water and sediment delivery from two sources, from the Lucchetti watershed and from a tunnel linking Lago Lucchetti to three upstream reservoirs. Variables in the model included precipitation and erosion factors (soil type, landscape slope, and land use) applied to the Lucchetti watershed and to watersheds of the upstream reservoirs. The lack of available data for water and sediment flows in the watershed and through tunnels connecting the reservoirs led to several unique methods for network data acquisition. Status quo model runs demonstrated that sediment trapping has continuously declined in all four reservoirs since their construction and that every year a greater proportion of sediment is moving downstream through tunnels or spillways. The model compared favorably with incidental measured data in the region. Sensitivity analysis demonstrated that current sediment accumulation in Lago Lucchetti can be attributed in large part to sediment erosion from the Lucchetti watershed with only minor influence (~8%) from upstream reservoirs. Two decision scenarios were explored by including additional nodes for (1) partial and full conversion of sungrown land use to shadegrown coffee cultivation in the Lucchetti watershed and (2) partial or complete dredging the reservoir. Both management actions were examined singly and in combination for effects on reservoir life expectancy (beyond the year 2000) with varying water capacity targets. Using 50% water storage capacity as a target, model runs for status quo (no decision implemented) resulted in a probability range that averaged 5.57 years (or 50% water storage capacity in the year 2005). Partial and full conversion of land from sungrown to shadegrown coffee cultivation raised the life expectancy to 5.89 and 6.57 years, respectively. Partial and complete dredging of the reservoir resulted in a life expectancy of 37.3 and 40.7 years, and full implementation of both management options resulted in a life expectancy of 44.4 years." | 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...A number of indicators have been proposed for measuring reef integrity, defined as the capacity to maintain healthy function and retention of diversity (Turner et al., 2000)...for reef ecological integrity (van Beukering and Cesar, 2004) defines the state of the reef as State of the Reef =ΣiwiRi where the Ri are the relative quantity of coral cover, macro-algal cover, fish richness, coral richness, and fish abundance, standardized to reflect the range of conditions at the location being evaluated (in this case, St. Croix). The wi give the weighted contribution of each attribute to reef condition based on expert judgment, originally developed for Hawaii, which were wcoral_cover=0.30, walgae_cover= 0.15, wfish_richness=0.15, wcoral_richness=0.20, and wfish_abundance=0.20 (van Beukering and Cesar, 2004). Ideally, these values would be developed to reflect local knowledge and concerns for the Caribbean or St. Croix. For a number of coral reef condition attributes, including fish richness, coral richness, and reef structural complexity, available data were point surveys from field monitoring by the US Environmental Protection Agency (see Oliver et al. (2011)) or the NOAA Caribbean Coral Reef Ecosystem Monitoring Program (see Pittman et al. (2008)). To generate continuous maps of coral condition for St. Croix, we fitted regression tree models to point survey data for St. Croix and then used models t | 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...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…Pendleton (1994) used field observations of dive sites to model potential impacts on local economies due to loss of dive tourism with reef degradation. A key part of the diver choice model is a fitted model of visitation to dive sites described by Visitation to dive sites = 2.897+0.0701creef -0.133D+0.0417τ where creef is percent coral cover, D is the time in hours to the dive site, which we estimate using distance from reef to shore and assuming a boat speed of 5 knots or 2.57ms-1, and τ is a dummy variable for the presence of interesting topographic features. We interpret τ as dramatic changes in bathymetry, quantified as having a standard deviation in depth among grid cells within 30 m that is greater than the75th percentile across all grid cells. Because our interpretation of topography differed from the original usage of “interesting features”, we also calculated dive site visitation assuming no contribution of topography (τ=0). Unsightly coastal development, an additional but non-significant variable in the original model, was assumed to be zero for St. Croix." | 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 | 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." | Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. | ABSTRACT:"Critical habitats for fish and wildlife are often small patches in landscapes, e.g., aquatic vegetation beds, reefs, isolated ponds and wetlands, remnant old-growth forests, etc., yet the same animal populations that depend on these patches for reproduction or survival can be extensive, ranging over large regions, even continents or major ocean basins. Whereas the ecological production functions that support these populations can be measured only at fine geographic scales and over brief periods of time, the ecosystem services (benefits that ecosystems convey to humans by supporting food production, water and air purification, recreational, esthetic, and cultural amenities, etc.) are delivered over extensive scales of space and time. These scale mismatches are particularly important for quantifying the economic values of ecosystem services. Examples can be seen in fish, shellfish, game, and bird populations. Moreover, there can be wide-scale mismatches in management regimes, e.g., coastal fisheries management versus habitat management in the coastal zone. We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for recreational Chinook (Oncorhynchus tshawytscha) salmon fisheries in the U.S. Pacific Northwest (Washington and Oregon) and commercial blue crab (Callinectes sapidus) and penaeid shrimp fisheries in the Gulf of Mexico. | ABSTRACT:"Critical habitats for fish and wildlife are often small patches in landscapes, e.g., aquatic vegetation beds, reefs, isolated ponds and wetlands, remnant old-growth forests, etc., yet the same animal populations that depend on these patches for reproduction or survival can be extensive, ranging over large regions, even continents or major ocean basins. Whereas the ecological production functions that support these populations can be measured only at fine geographic scales and over brief periods of time, the ecosystem services (benefits that ecosystems convey to humans by supporting food production, water and air purification, recreational, esthetic, and cultural amenities, etc.) are delivered over extensive scales of space and time. These scale mismatches are particularly important for quantifying the economic values of ecosystem services. Examples can be seen in fish, shellfish, game, and bird populations. Moreover, there can be wide-scale mismatches in management regimes, e.g., coastal fisheries management versus habitat management in the coastal zone. We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for recreational Chinook (Oncorhynchus tshawytscha) salmon fisheries in the U.S. Pacific Northwest (Washington and Oregon) and commercial blue crab (Callinectes sapidus) and penaeid shrimp fisheries in the Gulf of Mexico. | 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: "...We needed beach visitation estimates to assess the number of people who would be impacted by beach closures. We modeled visits by combining daily parking counts with other factors that help explain variations in attendance, including weather, day of the week or point within a season, and physical differences in sites (Kreitler et al. 2013). We designed the resulting model to estimate visitation for uncounted days as well as for beaches without counts on a given day. When combined with estimates of value per day, the visitation model can be used to value a lost beach day while accounting for beach size, time of season, and other factors...Since our count data of visitation for all four beaches are relatively large numbers (mean = 490, SD = 440), we used a log-linear regression model as opposed to a count data model. We selected a random effects model to account for time invariant variables such as parking spaces, modeling differences across beaches based on this variable…" Equation 2, page 15, provides the econometric regression. | ABSTRACT: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems." | 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: "There has been a rapid decline of grassland bird species in the UK over the last four decades. In order to stem declines in biodiversity such as this, mitigation in the form of newly created habitat and restoration of degraded habitats is advocated in the UK biodiversity action plan. One potential restored habitat that could support a number of bird species is re-created grassland on restored landfill sites. However, this potential largely remains unexplored. In this study, birds were counted using point sampling on nine restored landfill sites in the East Midlands region of the UK during 2007 and 2008. The effects of restoration were investigated by examining bird species composition, richness, and abundance in relation to habitat and landscape structure on the landfill sites in comparison to paired reference sites of existing wildlife value. Twelve bird species were found in total and species richness and abundance on restored landfill sites was found to be higher than that of reference sites. Restored landfill sites support both common grassland bird species and also UK Red List bird species such as skylark Alauda arvensis, grey partridge Perdix perdix, lapwing Vanellus vanellus, tree sparrow, Passer montanus, and starling Sturnus vulgaris. Size of the site, percentage of bare soil and amount of adjacent hedgerow were found to be the most influential habitat quality factors for the distribution of most bird species. Presence of open habitat and crop land in the surrounding landscape were also found to have an effect on bird species composition. Management of restored landfill sites should be targeted towards UK Red List bird species since such sites could potentially play a significant role in biodiversity action planning." AUTHOR'S DESCRIPTION: "Mean number of birds from multiple visits were used for data analysis. To analyse the data generalized linear models (GLMs) were constructed to compare local habitat and landscape parameters affecting different species, and to establish which habitat and landscape characteristics explained significant changes in the frequency of occurrence for each species. To ensure analyses focused on resident species, habitat associations were modelled for those seven bird species which were recorded at least three times in the surveys. The analysis was carried out with the software R (R Development Core Team 2003). Nonsignificant predictors (independent variables) were removed in a stepwise manner (least significant factor first). For distribution pattern of bird species, data were initially analysed using detrended correspondence analysis. Redundancy analysis (RDA) was performed on the same data using CANOCO for Windows version 4.0 (ter Braak and Smilauer 2002)." | Executive summary: "The HWBI is a composite assessment covering 8 domains based on 25 indicators measured using 80 different metrics. Service flow and stock assessments include 7 economic services (23 indicators, 40 metrics), 5 ecosystem services (8 indicators, 24 metrics) and 10 social services (37 indicators, 76 metrics). Data from 64 data sources were included in the HWBI and services provisioning characterizations (Fig. ES-3). For each U.S. county, state, and GSS region, data were acquired or imputed for the 2000-2010 time period resulting in over 1.5 million data points included in the full assessment linking service flows to well-being endpoints. The approaches developed for calculation of the HWBI, use of relative importance values, service stock characterization and functional modeling are transferable to smaller scales and specific population groups. Additionally, tracked over time, the HWBI may be useful in evaluating the sustainability of decisions in terms of EPA’s Total Resources Impact Outcome (TRIO) approaches." | VELMA – Visualizing Ecosystems for Land Management Assessments - is a spatially distributed, eco-hydrological model that links a land surface hydrology model with a terrestrial biogeochemistry model for simulating the integrated responses of vegetation, soil, and water resources to interacting stressors. For example, VELMA can simulate how changes in climate and land use interact to affect soil water storage, surface and subsurface runoff, vertical drainage, evapotranspiration, vegetation and soil carbon and nitrogen dynamics, and transport of nitrate, ammonium, and dissolved organic carbon and nitrogen to water bodies. VELMA differs from other existing eco-hydrology models in its simplicity, flexibility, and theoretical foundation. The model has a user-friendly Graphics User Interface (GUI) for easy input of model parameter values. In addition, advanced visualization of simulation results can enhance understanding of results and underlying concepts. VELMA’s visualization and interactivity features are packaged in an open-source, open-platform programming environment (Java / Eclipse). The development team for VELMA version 2.0 includes Dr. Bob McKane and coworkers at the U.S. Environmental Protection Agency’s Western Ecology Division, Dr. Marc Stieglitz and coworkers at the Georgia Institute of Technology, and Dr. Feifei Pan at the University of North Texas. AUTHOR'S DESCRIPTION: "Understanding how disturbances such as harvest, fire and fertilization affect ecosystem services has been a major motivation in the development of VELMA. For example, how do disturbances such as forest harvest or the application of agronomic fertilizers affect hydrological and biogeochemical processes controlling water quality and quantity, carbon sequestration, production of greenhouse gases, etc.? Abdelnour et al. (2011, 2013) have already demonstrated the use of VELMA v1.0 to simulate the effects of forest clearcutting on ecohydrological processes that regulate a variety of ecosystem services. With the addition of a tissue-specific plant biomass (LSR) simulator and an enhanced GUI, VELMA v2.0 significantly expands the detail, flexibility, and ease of use for simulating disturbance effects. Currently available disturbance models include: - BurnDisturbanceModel, effects of fire. - GrazeDisturbanceModel, effects of grazing. - FertilizeLsrDisturbanceModel, effects of fertilizer applications. - HarvestLsrDisturbanceModel, effects of biomass harvest. Each of these disturbance models specifies where and when a disturbance event will occur. The Burn, Graze and Harvest models have options for specifying how much of each plant tissue and detritus pool (leaves, stems, roots) will be removed and where it goes (offsite and/or to a specified onsite C and N pools). The Fertilize model has options for applying nitrogen as ammonium, nitrate, urea and/or manure." | [Enter up to 65000 characters] | 
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                        ? | None identified | None identified | Not applicable | This analysis provided input to government-led spatial planning and strategic environmental assessments in the study area. This region contains some of the last remaining forest habitat of the critically endangered Sumatran tiger, Panthera tigris sumatrae. | None identified | None identified | None Identified | None reported | None identified | None identified | None identified | None identified | None Identified | Future rock lobster fisheries management | climate change | None reported | None identified | To assess the number of people who would be impacted by beach closures. | None provided | None identified | None identified | None reported | None identified | None | 
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     | Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | No additional description provided | mangrove forest,Salt marsh, estuary, sea level, | Six watersheds in central Sumatra covering portions of Riau, Jambi and West Sumatra provinces. The Barisan mountain range comprises the western edge of the watersheds, while peat swamps predominate in the east. | Land use land class; habitat type | Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter. | Need to fill in | No additional reported | No additional description provided | No additional description provided | 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 | No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Yaquina Bay estuary | Yaquina Bay estuary | Four separate beaches within the community of Barnstable | Hard and soft benthic habitat types approximately to the 33m isobath | restored, enhanced and created wetlands | The study area covered mainly Northamptonshire and parts of Bedfordshire, Buckinghamshire and Warwickshire, ranging from 51o58’44.74” N to 52o26’42.18” N and 0o27’49.94” W to 1o19’57.67” W. This region has countryside of low, undulating hills separated by valleys and lies entirely within the great belt of scarplands formed by rocks of Jurassic age which stretch across England from Yorkshire to Dorset (Beaver 1943; Sutherland 1995; Wilson 1995). | Not applicable | No additional description provided | None | 
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                    EM Scenario Drivers
                
                
             
           
     
                            
                                
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                        ? | No scenarios presented | No scenarios presented | Not applicable | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | Land use land cover changes; habitat disturbance | Forest management (harvest/no harvest) | Planting type, fertilizing rate, harvest rate | Management alternatives | 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 | Fisheries exploitation; fishing vulnerability (of age classes) | fertilization | N/A | No scenarios presented | No scenarios presented | No scenarios presented | Sites, function or habitat focus | No scenarios presented | geographic region | No scenarios presented | None | 
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-66 | EM-137 | EM-154 | EM-349   | EM-367 | EM-380   | EM-417 | EM-421 | EM-444 | EM-457 | EM-467   | EM-485   | EM-492 | EM-541   | EM-598 | EM-603 | EM-604 | EM-684 | EM-699 | EM-718   | EM-837 | EM-885 | EM-887 | EM-961 | 
| 
             
                
                
                    
                    
                    Method Only, Application of Method or Model Run
                
                
             
           
     
                            
                                
                                    em.detail.methodOrAppHelp
                                
                                
                            
                            
                        ? | Method + Application | Method Only | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) | Method + Application | 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 | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application | Method Only | None | 
| 
             
                
                
                    
                    
                    New or Pre-existing EM?
                
                
             
           
     
                            
                                
                                    em.detail.newOrExistHelp
                                
                                
                            
                            
                        ? | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | New or revised model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | None | 
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
| 
             
                
                
                
                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-66 | EM-137 | EM-154 | EM-349   | EM-367 | EM-380   | EM-417 | EM-421 | EM-444 | EM-457 | EM-467   | EM-485   | EM-492 | EM-541   | EM-598 | EM-603 | EM-604 | EM-684 | EM-699 | EM-718   | EM-837 | EM-885 | EM-887 | EM-961 | 
| 
             
                
                
                    
                    
                    EM Temporal Extent
                
                
             
           
     
                            
                                
                                    em.detail.tempExtentHelp
                                
                                
                            
                            
                        ? | Not reported | Not applicable | 1990-2010 | 2008-2020 | Not applicable | 1969-2008 | 1981-2004 | 1962-2006 | 2006-2007, 2010 | 2006-2007, 2010 | 1993-2013 | 2000-2010 | 2006-2013 | 1986-2115 | 1961-1990 | 2003-2008 | 2003-2008 | 2011 - 2016 | 2000-2005 | 2010-2011 | Not applicable | 2000-2010 | Not applicable | None | 
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                    EM Time Dependence
                
                
             
           
     
                            
                                
                                    em.detail.timeDependencyHelp
                                
                                
                            
                            
                        ? | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | None | 
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                    EM Time Reference (Future/Past)
                
                
             
           
     
                            
                                
                                    em.detail.futurePastHelp
                                
                                
                            
                            
                        ? | Not applicable | Not applicable | future time | Not applicable | Not applicable | future time | future time | future time | Not applicable | Not applicable | future time | future time | Not applicable | future time | both | Not applicable | Not applicable | past time | Not applicable | past time | Not applicable | Not applicable | Not applicable | None | 
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                    EM Time Continuity
                
                
             
           
     
                            
                                
                                    em.detail.continueDiscreteHelp
                                
                                
                            
                            
                        ? | Not applicable | discrete | continuous | Not applicable | discrete | discrete | discrete | discrete | Not applicable | Not applicable | discrete | discrete | Not applicable | discrete | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | None | 
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                    EM Temporal Grain Size Value
                
                
             
           
     
                            
                                
                                    em.detail.tempGrainSizeHelp
                                
                                
                            
                            
                        ? | Not applicable | 1 | Not applicable | Not applicable | 1 | 1 | 1 | 1 | Not applicable | Not applicable | 1 | 1 | Not applicable | 1 | 1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | None | 
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                    EM Temporal Grain Size Unit
                
                
             
           
     
                            
                                
                                    em.detail.tempGrainSizeUnitHelp
                                
                                
                            
                            
                        ? | Not applicable | Hour | Not applicable | Not applicable | Year | Day | Day | Year | Not applicable | Not applicable | Year | Year | Not applicable | Year | Day | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Day | None | 
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-66 | EM-137 | EM-154 | EM-349   | EM-367 | EM-380   | EM-417 | EM-421 | EM-444 | EM-457 | EM-467   | EM-485   | EM-492 | EM-541   | EM-598 | EM-603 | EM-604 | EM-684 | EM-699 | EM-718   | EM-837 | EM-885 | EM-887 | EM-961 | 
| 
             
                
                
                
                    Bounding Type
                
             
           
     
                            
                            
                                em.detail.boundingTypeHelp
                            
                        ? | Physiographic or Ecological | Not applicable | Physiographic or Ecological | Watershed/Catchment/HUC | Not applicable | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Geopolitical | Geopolitical | Geopolitical | Geopolitical | Point or points | Geopolitical | Geopolitical | Physiographic or ecological | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Not applicable | Geopolitical | Not applicable | Multiple unrelated locations (e.g., meta-analysis) | 
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                    Spatial Extent Name
                
             
           
     
                            
                            
                                em.detail.extentNameHelp
                            
                        ? | Central French Alps | Not applicable | Tampa Bay | central Sumatra | Not applicable | H. J. Andrews LTER WS10 | Guanica Bay, Puerto Rico watersheds | Guanica Bay watershed | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Switzerland | Switzerland | conterminous United States | Table Mountain National Park Marine Protected Area | Oak Park Research centre | Pacific Northwest | Pacific Northwest | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | SW Puerto Rico, | Wetlands in idaho | Not applicable | Continental U.S. | Not applicable | Western & Eastern Corn Belt Plains | 
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                    Spatial Extent Area (Magnitude)
                
             
           
     
                            
                            
                                em.detail.extentAreaHelp
                            
                        ? | 10-100 km^2 | Not applicable | 100-1000 km^2 | 100,000-1,000,000 km^2 | Not applicable | 10-100 ha | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | 10,000-100,000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 1-10 ha | >1,000,000 km^2 | >1,000,000 km^2 | 10-100 ha | 100-1000 km^2 | 100,000-1,000,000 km^2 | Not applicable | >1,000,000 km^2 | Not applicable | 100,000-1,000,000 km^2 | 
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-66 | EM-137 | EM-154 | EM-349   | EM-367 | EM-380   | EM-417 | EM-421 | EM-444 | EM-457 | EM-467   | EM-485   | EM-492 | EM-541   | EM-598 | EM-603 | EM-604 | EM-684 | EM-699 | EM-718   | EM-837 | EM-885 | EM-887 | EM-961 | 
| 
             
                
                
                    
                    
                    EM Spatial Distribution
                
                
             
           
     
                            
                                
                                    em.detail.distributeLumpHelp
                                
                                
                            
                            
                        ? | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | 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) | spatially lumped (in all cases) | spatially lumped (in all 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 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) | None | 
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                    Spatial Grain Type
                
             
           
     
                            
                            
                                em.detail.spGrainTypeHelp
                            
                        ? | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | volume, for 3-D feature | other (specify), for irregular (e.g., stream reach, lake basin) | 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) | Not applicable | Not applicable | Not applicable | Not applicable | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | Not applicable | 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 | None | 
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                    Spatial Grain Size
                
             
           
     
                            
                            
                                em.detail.spGrainSizeHelp
                            
                        ? | 20 m x 20 m | 30 x 30 m | m^2 | 30 m x 30 m | user-specified | 30 m x 30 m surface pixel and 2-m depth soil column | 30m x 30m | Not reported | 10 m x 10 m | 10 m x 10 m | 5 sites | Not applicable | irregular | Not applicable | Not applicable | Not applicable | Not applicable | by beach site | not reported | Not applicable | multiple unrelated sites | county | user defined | None | 
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-66 | EM-137 | EM-154 | EM-349   | EM-367 | EM-380   | EM-417 | EM-421 | EM-444 | EM-457 | EM-467   | EM-485   | EM-492 | EM-541   | EM-598 | EM-603 | EM-604 | EM-684 | EM-699 | EM-718   | EM-837 | EM-885 | EM-887 | EM-961 | 
| 
             
                
                
                    
                    
                    EM Computational Approach
                
                
             
           
     
                            
                                
                                    em.detail.emComputationalApproachHelp
                                
                                
                            
                            
                        ? | Analytic | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Numeric | Analytic | Analytic | Numeric | Numeric | Analytic | Numeric | Numeric | Numeric | Analytic | Analytic | Analytic | Numeric | Analytic | Numeric | Numeric | * | 
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                    EM Determinism
                
                
             
           
     
                            
                                
                                    em.detail.deterStochHelp
                                
                                
                            
                            
                        ? | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | None | 
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                    Statistical Estimation of EM
                
             
           
     
                            
                            
                                em.detail.statisticalEstimationHelp
                            
                        ? | 
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 | None | 
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-66 | EM-137 | EM-154 | EM-349   | EM-367 | EM-380   | EM-417 | EM-421 | EM-444 | EM-457 | EM-467   | EM-485   | EM-492 | EM-541   | EM-598 | EM-603 | EM-604 | EM-684 | EM-699 | EM-718   | EM-837 | EM-885 | EM-887 | EM-961 | 
| 
             
                
                
                
                    Model Calibration Reported?
                
             
           
     
                            
                            
                                em.detail.calibrationHelp
                            
                        ? | No | Not applicable | No | No | Not applicable | Yes | Yes ? Comment:Used 1981 and 1982 data to calibrate hydrology. | Yes | Yes | Yes | No | No | No | No | Yes | No | No | Yes | No | No | Not applicable | No | Not applicable | None | 
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                    Model Goodness of Fit Reported?
                
                
             
           
     
                            
                                
                                    em.detail.goodnessFitHelp
                                
                                
                            
                            
                        ? | Yes | Not applicable | No | No | Not applicable | No | No ? Comment:Calibration for both the stream flow and Sediment concentration of the mode | No | No | No | No | No | No | No | Yes ? Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed | No | No | No | Yes | No | Not applicable | No | Not applicable | None | 
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                    Goodness of Fit (metric| value | unit)
                
                
             
           
     
                            
                                
                                    em.detail.goodnessFitValuesHelp
                                
                                
                            
                            
                        ? | 
 | None | None | None | None | None | 
 | None | None | None | None | None | None | None | 
 | None | None | None | 
 | None | None | None | None | None | 
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                    Model Operational Validation Reported?
                
                
             
           
     
                            
                                
                                    em.detail.validationHelp
                                
                                
                            
                            
                        ? | Yes | Not applicable | No | No | Not applicable | No | Yes ? Comment:Validation with 1983-1984 data from USGS. Used streamflow and water quality data from two stations | No | Yes | Yes | 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. | Yes | Yes ? Comment:Compared to a second methodological approach | Yes | No | Yes | No | Not applicable | No | Not applicable | None | 
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                    Model Uncertainty Analysis Reported?
                
                
             
           
     
                            
                                
                                    em.detail.uncertaintyAnalysisHelp
                                
                                
                            
                            
                        ? | No | Not applicable | Yes | No | Not applicable | No | Unclear | No | No | No | No | Yes | No | No | No | No | No | No | No | No | Not applicable | Unclear | Not applicable | None | 
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                    Model Sensitivity Analysis Reported?
                
                
             
           
     
                            
                                
                                    em.detail.sensAnalysisHelp
                                
                                
                            
                            
                        ? | No | Not applicable | Yes | No | Not applicable | Yes | Yes ? Comment:Yes for both runoff and sediment | No | No | No | No | No | No | No | No | No | No | Yes | Yes | No | Not applicable | Yes | Not applicable | None | 
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                    Model Sensitivity Analysis Include Interactions?
                
             
           
     
                            
                            
                                em.detail.interactionConsiderHelp
                            
                        ? | Not applicable | Not applicable | No | Not applicable | Not applicable | No | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Yes | Not applicable | None | 
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-66 | EM-137 | EM-154 | EM-349   | EM-367 | EM-380   | EM-417 | EM-421 | EM-444 | EM-457 | EM-467   | EM-485   | EM-492 | EM-541   | EM-598 | EM-603 | EM-604 | EM-684 | EM-699 | EM-718   | EM-837 | EM-885 | EM-887 | EM-961 | 
| 
 | None | 
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 | None | 
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 | None | None | 
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 | None | 
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 | None | 
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-66 | EM-137 | EM-154 | EM-349   | EM-367 | EM-380   | EM-417 | EM-421 | EM-444 | EM-457 | EM-467   | EM-485   | EM-492 | EM-541   | EM-598 | EM-603 | EM-604 | EM-684 | EM-699 | EM-718   | EM-837 | EM-885 | EM-887 | EM-961 | 
| None | None | 
 Comment:Realm: Tropical Atlantic Region: West Tropical Atlantic Province: Tropical Northwestern Atlantic Ecoregion: Floridian | None | None | None | None | None | 
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 | None | None | None | 
 | None | 
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 | None | None | None | None | None | 
Centroid Lat/Long (Decimal Degree)
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-66 | EM-137 | EM-154 | EM-349   | EM-367 | EM-380   | EM-417 | EM-421 | EM-444 | EM-457 | EM-467   | EM-485   | EM-492 | EM-541   | EM-598 | EM-603 | EM-604 | EM-684 | EM-699 | EM-718   | EM-837 | EM-885 | EM-887 | EM-961 | 
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                    Centroid Latitude
                
                
             
           
     
                            
                                
                                    em.detail.ddLatHelp
                                
                                
                            
                            
                        ? | 45.05 | -9999 | 27.8 | 0 | -9999 | 44.25 | 18.19 | 18.09 | 17.73 | 17.73 | 46.82 | 46.82 | 39.5 | -34.18 | 52.86 | 44.62 | 44.62 | 41.64 | 17.79 | 44.06 | Not applicable | 39.83 | Not applicable | None | 
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                    Centroid Longitude
                
                
             
           
     
                            
                                
                                    em.detail.ddLongHelp
                                
                                
                            
                            
                        ? | 6.4 | -9999 | -82.4 | 102 | -9999 | -122.33 | -66.76 | -66.86 | -64.77 | -64.77 | 8.23 | 8.23 | -98.35 | 18.35 | 6.54 | -124.02 | -124.02 | -70.29 | -64.62 | -114.69 | Not applicable | -98.58 | Not applicable | None | 
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                    Centroid Datum
                
                
             
           
     
                            
                                
                                    em.detail.datumHelp
                                
                                
                            
                            
                        ? | WGS84 | Not applicable | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | Not applicable | None | 
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                    Centroid Coordinates Status
                
                
             
           
     
                            
                                
                                    em.detail.coordinateStatusHelp
                                
                                
                            
                            
                        ? | Provided | Not applicable | Estimated | Provided | Not applicable | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Not applicable | None | 
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-66 | EM-137 | EM-154 | EM-349   | EM-367 | EM-380   | EM-417 | EM-421 | EM-444 | EM-457 | EM-467   | EM-485   | EM-492 | EM-541   | EM-598 | EM-603 | EM-604 | EM-684 | EM-699 | EM-718   | EM-837 | EM-885 | EM-887 | EM-961 | 
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                    EM Environmental Sub-Class
                
                
             
           
     
                            
                                
                                    em.detail.emEnvironmentalSubclassHelp
                                
                                
                            
                            
                        ? | Agroecosystems | Grasslands | Rivers and Streams | Ground Water | Created Greenspace | Near Coastal Marine and Estuarine | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Inland Wetlands | Near Coastal Marine and Estuarine | Rivers and Streams | Ground Water | Forests | Terrestrial Environment (sub-classes not fully specified) | Lakes and Ponds | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Forests | Forests | Agroecosystems | Near Coastal Marine and Estuarine | Agroecosystems | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Inland Wetlands | Created Greenspace | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | 
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                    Specific Environment Type
                
                
             
           
     
                            
                                
                                    em.detail.specificEnvTypeHelp
                                
                                
                            
                            
                        ? | Subalpine terraces, grasslands, and meadows | Urban watersheds | Created Mangrove wetlands | 104 land use land cover classes | user specified | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | watershed | Lago Luchetti | Coral reefs | Coral reefs | forests | forests | Terrestrial | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | farm pasture | Yaquina Bay | Yaquina Bay estuary and ocean | Saltwater beach | shallow coral reefs | created, restored and enhanced wetlands | restored landfills and conserved grasslands | All land of the continental US | Terrestrial environment sub-classes | Plains | 
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                    EM Ecological Scale
                
                
             
           
     
                            
                                
                                    em.detail.ecoScaleHelp
                                
                                
                            
                            
                        ? | Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 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 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 | 
Scale of differentiation of organisms modeled
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                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-66 | EM-137 | EM-154 | EM-349   | EM-367 | EM-380   | EM-417 | EM-421 | EM-444 | EM-457 | EM-467   | EM-485   | EM-492 | EM-541   | EM-598 | EM-603 | EM-604 | EM-684 | EM-699 | EM-718   | EM-837 | EM-885 | EM-887 | EM-961 | 
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                    EM Organismal Scale
                
                
             
           
     
                            
                                
                                    em.detail.orgScaleHelp
                                
                                
                            
                            
                        ? | Community | Community | Not applicable | Community | Not applicable | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Community | Community | Not applicable | Individual or population, within a species | Not applicable | Individual or population, within a species | Other (multiple scales) | Not applicable | Guild or Assemblage | Not applicable | Individual or population, within a species | Not applicable | Not applicable | None | 
Taxonomic level and name of organisms or groups identified
| EM-66 | EM-137 | EM-154 | EM-349   | EM-367 | EM-380   | EM-417 | EM-421 | EM-444 | EM-457 | EM-467   | EM-485   | EM-492 | EM-541   | EM-598 | EM-603 | EM-604 | EM-684 | EM-699 | EM-718   | EM-837 | EM-885 | EM-887 | EM-961 | 
| 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 | 
 | None Available | 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-66 | EM-137 | EM-154 | EM-349   | EM-367 | EM-380   | EM-417 | EM-421 | EM-444 | EM-457 | EM-467   | EM-485   | EM-492 | EM-541   | EM-598 | EM-603 | EM-604 | EM-684 | EM-699 | EM-718   | EM-837 | EM-885 | EM-887 | EM-961 | 
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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-66 | EM-137 | EM-154 | EM-349   | EM-367 | EM-380   | EM-417 | EM-421 | EM-444 | EM-457 | EM-467   | EM-485   | EM-492 | EM-541   | EM-598 | EM-603 | EM-604 | EM-684 | EM-699 | EM-718   | EM-837 | EM-885 | EM-887 | EM-961 | 
| None | 
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 | None | None | None | None | 
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 | None | None | None | 
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 | None | 
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 | None | None | None | None | None | 
 
    
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