EcoService Models Library (ESML)
loading
Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
    - Time- or Space-Varying Variables
- Constants and Parameters
 
- Intermediate (Computed) Variables
- Response Variables
    - Computed Response Variables
- Measured Response Variables
 
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
EM Identity and Description
| 
             
                
                
                    
                    
                    EM ID
                
                
             
           
     
                            
                                
                                    em.detail.idHelp
                                
                                
                            
                            
                        ? | EM-12   | EM-82 | EM-84 | EM-91 | EM-102   | EM-103 | EM-111   | EM-137 | EM-154 | EM-208   | EM-306 | EM-397   | EM-449 | EM-493 | EM-541   | EM-542   | EM-630 | EM-709   | EM-735   | 
| 
             
                
                
                
                    EM Short Name
                
             
           
     
                            
                            
                                em.detail.shortNameHelp
                            
                        ? | Evoland v3.5 (bounded growth), Eugene, OR, USA | Pollination ES, Central French Alps | ACRU, South Africa | RHyME2, Upper Mississippi River basin, USA | Fish species habitat value, Tampa Bay, FL, USA | Birds in estuary habitats, Yaquina Estuary, WA, USA | InVEST water yield, Hood Canal, WA, USA | i-Tree Hydro v4.0 | Mangrove development, Tampa Bay, FL, USA | FORCLIM v2.9, Santiam watershed, OR, USA | Urban Temperature, Baltimore, MD, USA | Wetland shellfish production, Gulf of Mexico, USA | Decrease in erosion (shoreline), St. Croix, USVI | EnviroAtlas-Carbon sequestered by trees | InVEST fisheries, lobster, South Africa | Coastal protection in Belize | WaterWorld v2, Santa Basin, Peru | Pollinators on landfill sites, United Kingdom | C sequestration in grassland restoration, England | 
| 
             
                
                
                    
                    
                    EM Full Name
                
                
             
           
     
                            
                                
                                    em.detail.fullNameHelp
                                
                                
                            
                            
                        ? | Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA | Pollination ecosystem service estimated from plant functional traits, Central French Alps | ACRU (Agricultural Catchments Research Unit), South Africa | RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | Fish species habitat value, Tampa Bay, FL, USA | Bird use of estuarine habitats, Yaquina Estuary, WA, USA | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) water yield, Hood Canal, WA, USA | i-Tree Hydro v4.0 (default data option) | Mangrove wetland development, Tampa Bay, FL, USA | FORCLIM (FORests in a changing CLIMate) v2.9, Santiam watershed, OR, USA | Urban Air Temperature Change, Baltimore, MD, USA | Wetland shellfish production, Gulf of Mexico, USA | Decrease in erosion (shoreline) by reef, St. Croix, USVI | US EPA EnviroAtlas - Total carbon sequestered by tree cover; Example is shown for Durham NC and vicinity, USA | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa | Coastal Protection provided by Coral, Seagrasses and Mangroves in Belize: | WaterWorld v2, Santa Basin, Peru | Pollinating insects on landfill sites, East Midlands, United Kingdon | Carbon sequestration in grassland diversity restoration, England | 
| 
             
                
                
                
                    EM Source or Collection
                
             
           
     
                            
                            
                                em.detail.emSourceOrCollectionHelp
                            
                        ? | Envision | EU Biodiversity Action 5 | None | US EPA | US EPA | US EPA | InVEST | i-Tree | USDA Forest Service | US EPA | US EPA | i-Tree | USDA Forest Service | US EPA ? Comment:Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science | US EPA | US EPA | EnviroAtlas | i-Tree | InVEST | InVEST | None | None | None | 
| 
             
                
                
                
                    EM Source Document ID
                
             
           
     | 47 ? Comment:Doc 183 is a secondary source for the Evoland model. | 260 | 271 | 123 | 187 | 275 | 205 | 198 | 97 | 23 ? Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. | 217 | 324 | 335 | 223 ? Comment:Additional source: I-tree Eco (doc# 345). | 349 ? Comment:Supplemented with the InVEST Users Guide fisheries. | 350 | 368 | 389 | 396 | 
| 
             
                
                
                    
                    
                    Document Author
                
                
             
           
     
                            
                                
                                    em.detail.documentAuthorHelp
                                
                                
                            
                            
                        ? | Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C. | Fulford, R., Yoskowitz, D., Russell, M., Dantin, D., and Rogers, J. | Frazier, M. R., Lamberson, J. O. and Nelson, W. G. | Toft, J. E., Burke, J. L., Carey, M. P., Kim, C. K., Marsik, M., Sutherland, D. A., Arkema, K. K., Guerry, A. D., Levin, P. S., Minello, T. J., Plummer, M., Ruckelshaus, M. H., and Townsend, H. M. | 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. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Heisler, G. M., Ellis, A., Nowak, D. and Yesilonis, I. | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Yee, S. H., Dittmar, J. A., and L. M. Oliver | US EPA Office of Research and Development - National Exposure Research Laboratory | Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby | Guannel, G., Arkema, K., Ruggiero, P., and G. Verutes | Van Soesbergen, A. and M. Mulligan | Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin | De Deyn, G. B., R. S. Shiel, N. J. Ostle, N. P. McNamara, S. Oakley, I. Young, C. Freeman, N. Fenner, H. Quirk, and R. D. Bardgett | 
| 
             
                
                
                    
                    
                    Document Year
                
                
             
           
     
                            
                                
                                    em.detail.documentYearHelp
                                
                                
                            
                            
                        ? | 2008 | 2011 | 2008 | 2013 | 2016 | 2014 | 2013 | Not Reported | 2012 | 2007 | 2016 | 2012 | 2014 | 2013 | 2018 | 2016 | 2018 | 2013 | 2011 | 
| 
             
                
                
                
                    Document Title
                
             
           
     
                            
                            
                                em.detail.sourceIdHelp
                            
                        ? | Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Mapping ecosystem services for planning and management | Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads | Habitat and recreational fishing opportunity in Tampa Bay: Linking ecological and ecosystem services to human beneficiaries | Intertidal habitat utilization patterns of birds in a Northeast Pacific estuary | From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA | i-Tree Hydro User's Manual v. 4.0 | Ecosystem development after mangrove wetland creation: plant–soil change across a 20-year chronosequence | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Modeling and imaging land-cover influences on air-temperature in and near Baltimore, MD | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | EnviroAtlas - Featured Community | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals | The Power of Three: Coral Reefs, Seagrasses and Mangroves Protect Coastal Regions and Increase Their Resilience | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects | Additional carbon sequestration benefits of grassland diversity restoration | 
| 
             
                
                
                
                    Document Status
                
             
           
     
                            
                            
                                em.detail.statusCategoryHelp
                            
                        ? | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed 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 | 
| 
             
                
                
                
                    Comments on Status
                
             
           
     
                            
                            
                                em.detail.commentsOnStatusHelp
                            
                        ? | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Webpage | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | 
| 
             
                
                
                
                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-12   | EM-82 | EM-84 | EM-91 | EM-102   | EM-103 | EM-111   | EM-137 | EM-154 | EM-208   | EM-306 | EM-397   | EM-449 | EM-493 | EM-541   | EM-542   | EM-630 | EM-709   | EM-735   | 
| http://evoland.bioe.orst.edu/ ? Comment:Software is likely available. | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | http://www.itreetools.org | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.epa.gov/enviroatlas | https://www.naturalcapitalproject.org/invest/ | Not identified in paper | www.policysupport.org/waterworld | Not applicable | Not applicable | |
| 
             
                
                
                    
                    
                    Contact Name
                
                
             
           
     
                            
                                
                                    em.detail.contactNameHelp
                                
                                
                            
                            
                        ? | Michael R. Guzy | Sandra Lavorel | Roland E Schulze | Liem Tran | Richard Fulford | M. R. Frazier ? Comment:Present address: M. R. Frazier National Center for Ecological Analysis and Synthesis, 735 State St. Suite 300, Santa Barbara, CA 93101, USA | J.E. Toft | Not applicable | Michael Osland | Richard T. Busing | Gordon M. Heisler | Stephen J. Jordan | Susan H. Yee | EnviroAtlas Team | Michelle Ward | Greg Guannel | Arnout van Soesbergen | Sam Tarrant | Gerlinde B. De Deyn | 
| 
             
                
                
                
                    Contact Address
                
             
           
     | Oregon State University, Dept. of Biological and Ecological Engineering | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | School of Bioresources Engineering and Environmental Hydrology, University of Natal, South Africa | Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA | USEPA Gulf Ecology Division, Gulf Breeze, FL 32561 | Western Ecology Division, Office of Research and Development, U.S. Environmental Protection Agency, Pacific coastal Ecology Branch, 2111 SE marine Science Drive, Newport, OR 97365 | The Natural Capital Project, Stanford University, 371 Serra Mall, Stanford, CA 94305-5020, USA | Not applicable | U.S. Environmental Protection Agency, Gulf Ecology Division, gulf Breeze, FL 32561 | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | 5 Moon Library, c/o SUNY-ESF, Syracuse, NY 13210 | U.S. Environmental Protection Agency, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Not reported | ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia | The Nature Conservancy, Coral Gables, FL. USA | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K. | Dept. of Terrestrial Ecology, Netherlands Institute of Ecology, P O Box 40, 6666 ZG Heteren, The Netherlands | 
| 
             
                
                
                
                    Contact Email
                
             
           
     | Not reported | sandra.lavorel@ujf-grenoble.fr | schulzeR@nu.ac.za | ltran1@utk.edu | Fulford.Richard@epa.gov | frazier@nceas.ucsb.edu | jetoft@stanford.edu | Not applicable | mosland@usgs.gov | rtbusing@aol.com | gheisler@fs.fed.us | jordan.steve@epa.gov | yee.susan@epa.gov | enviroatlas@epa.gov | m.ward@uq.edu.au | greg.guannel@gmail.com | arnout.van_soesbergen@kcl.ac.uk | sam.tarrant@rspb.org.uk | g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com | 
| 
             
                
                
                
                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-12   | EM-82 | EM-84 | EM-91 | EM-102   | EM-103 | EM-111   | EM-137 | EM-154 | EM-208   | EM-306 | EM-397   | EM-449 | EM-493 | EM-541   | EM-542   | EM-630 | EM-709   | EM-735   | 
| 
             
                
                
                    
                    
                    Summary Description
                
                
             
           
     
                            
                                
                                    em.detail.summaryDescriptionHelp
                                
                                
                            
                            
                        ? | **Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The pollination ecosystem service map was a simple sums of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to pollination ecosystem services are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions." | AUTHOR'S DESCRIPTION (Doc ID 272): "ACRU is a daily timestep, physical conceptual and multipurpose model structured to simulate impacts of land cover/ use change. The model can output, inter alia, components of runoff, irrigation supply and demand, reservoir water budgets as well as sediment and crop yields." AUTHOR'S DESCRIPTION (Doc ID 271): "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…The total benefit to people of water supply is a function of both the quantity and quality with the ecosystem playing a key role in the latter. However, due to the lack of suitable national scale data on water quality for quantifying the service, runoff was used as an estimate of the benefit where runoff is the total water yield from a watershed including surface and subsurface flow. This assumes that runoff is positively correlated with quality, which is the case in South Africa (Allanson et al., 1990)…In South Africa, water resources are mapped in water management areas called catchments (vs. watersheds) where a catchment is defined as the area of land that is drained by a single river system, including its tributaries (DWAF, 2004). There are 1946 quaternary (4th order) catchments in South Africa, the smallest is 4800 ha and the average size is 65,000 ha. Schulze (1997) modelled annual runoff for each quaternary catchment. During modelling of runoff, he used rainfall data collected over a period of more than 30 years, as well as data on other climatic factors, soil characteristics and grassland as the land cover. In this study, median annual simulated runoff was used as a measure of surface water supply. The volume of runoff per quaternary catchment was calculated for surface water supply. The range (areas with runoff of 30 million m^3 or more) and hotspots (areas with runoff of 70 million m^3 or more) were defined using a combination of statistics and expert inputs due to a lack of published thresholds in the literature." | ABSTRACT: "We describe a framework called Regional Hydrologic Modeling for Environmental Evaluation (RHyME2) for hydrologic modeling across scales. Rooted from hierarchy theory, RHyME2 acknowledges the rate-based hierarchical structure of hydrological systems. Operationally, hierarchical constraints are accounted for and explicitly described in models put together into RHyME2. We illustrate RHyME2with a two-module model to quantify annual nutrient loads in stream networks and watersheds at regional and subregional levels. High values of R2 (>0.95) and the Nash–Sutcliffe model efficiency coefficient (>0.85) and a systematic connection between the two modules show that the hierarchy theory-based RHyME2 framework can be used effectively for developing and connecting hydrologic models to analyze the dynamics of hydrologic systems." Two EMs will be entered in EPF-Library: 1. Regional scale module (Upper Mississippi River Basin) - this entry 2. Subregional scale module (St. Croix River Basin) | ABSTRACT: "Estimating value of estuarine habitat to human beneficiaries requires that we understand how habitat alteration impacts function through both production and delivery of ecosystem goods and services (EGS). Here we expand on the habitat valuation technique of Bell (1997) with an estimate of recreational angler willingness-to-pay combined with estimates of angler effort, fish population size, and fish and angler distribution. Results suggest species-specific fishery value is impacted by angler interest and stock status, as the most targeted fish (spotted seatrout) did not have the highest specific value (fish−1). Reduced population size and higher size at capture resulted in higher specific value for common snook. Habitat value estimated from recreational fishing value and fish-angler distributions supported an association between seagrass and habitat value, yet this relationship was also impacted by distance to access points. This analysis does not provide complete valuation of habitat as it considers only one service (fishing), but demonstrates a methodology to consider functional equivalency of all habitat features as a part of a habitat mosaic rather than in isolation, as well as how to consider both EGS production and delivery to humans (e.g., anglers) in any habitat valuation, which are critical for a transition to ecosystem management." | AUTHOR'S DESCRIPTION: "To describe bird utilization patterns of intertidal habitats within Yaquina estuary, Oregon, we conducted censuses to obtain bird species and abundance data for the five dominant estuarine intertidal habitats: Zostera marina (eelgrass), Upogebia (mud shrimp)/ mudflat, Neotrypaea (ghost shrimp)/sandflat, Zostera japonica (Japanese eelgrass), and low marsh. EPFs were developed for the following metrics of bird use: standardized species richness; Shannon diversity; and density for the following four groups: all birds, all birds excluding gulls, waterfowl (ducks and geese), and shorebirds." | InVEST Water Yield and Scarcity Model Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "We modelled discharge and total nitrogen for the 153 perennial sub- watersheds in Hood Canal based on spatial variation in hydrological factors, land and water use, and vegetation.To do this, we reparame-terized a set of fresh water models available in the InVEST tool (Tallis and Polasky, 2009; Kareiva et al., 2011)… We modelled discharge using the InVESTWater Yield and Scarcity model. The model estimates discharge for user-defined subwatersheds based on the average annual precipitation, annual reference evapotranspiration, and a correction factor for vegetation type, soil depth, plant available water content, land use and land cover, root depth, elevation, saturated hydraulic conductivity, and consumptive water use" (2) | 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." | ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range. It was then applied to simulate present and future (1990-2050) forest landscape dynamics of a watershed in the west Cascades. Various regimes of climate change and harvesting in the watershed were considered in the landscape application." AUTHOR'S DESCRIPTION: "Effects of different management histories on the landscape were incorporated using the land management (conservation, plan, or development trend) and forest age categories…the plan trend was an intermediate alternative, representing the continuation of current policies and trends, whereas the conservation and development trends were possible alternatives…Non-forested areas were given a forest age of zero; forested areas were assigned to one of eight forest age classes: >0-20 yr, 21-40 yr, 41-60 yr, 61-80 yr, 81-200 yr, 201-400 yr, and >600 yr in 1990…two climate change scenarios were used, representing lower and upper extremes projected by a set of global climate models: (1) minor warming with drier summers, and (2) major warming with wetter conditions…For the first scenario, temperature was increased by 0.5°C in 2025 and by 1.5°C in 2045. Precipitation from October to March was increased 2% in 2025 and decreased 2% in 2045. Precipitation from April to September was decreased 4% in 2025 and 7% in 2045. For the second scenario, temperature was by increased 2.6°C in 2025 and by 3.2°C in 2045. Precipitation from October to March was increased 18% in 2025 and 22% in 2045. Precipitation from April to September was increased 14% in 2025 and 9% in 2045. | An empirical model for predicting below-canopy air temperature differences is developed for evaluating urban structural and vegetation influences on air temperature in and near Baltimore, MD. AUTHOR'S DESCRIPTION: "The study . . . Developed an equation for predicting air temperature at the 1.5m height as temperature difference, T, between a reference weather station and other stations in a variety of land uses. Predictor variables were derived from differences in land cover and topography along with forcing atmospheric conditions. The model method was empirical multiple linear regression analysis.. . Independent variables included remotely sensed tree cover, impervious cover, water cover, descriptors of topography, an index of thermal stability, vapor pressure deficit, and antecedent precipitation." | ABSTRACT: "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 … commercial blue crab Callinectes sapidus and penaeid shrimp fisheries in the Gulf of Mexico." | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef." | The Total carbon sequestered by tree cover model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. DATA FACT SHEET: "This EnviroAtlas community map estimates the total metric tons (mt) of carbon that are removed annually from the atmosphere and sequestered in the above-ground biomass of trees in each census block group. The data for this map were derived from a high-resolution tree cover map developed by EPA. Within each census block group derived from U.S. Census data, the total amount of tree cover (m2) was determined using this remotely-sensed land cover data. The USDA Forest Service i-Tree model was used to estimate the annual carbon sequestration rate from state-based rates of kgC/m2 of tree cover/year. The state rates vary based on length of growing season and range from 0.168 kgC/m2 of tree cover/year (Alaska) to 0.581 kgC/m2 of tree cover/year (Hawaii). The national average rate is 0.306 kgC/m2 of tree cover/year. These national and state values are based on field data collected and analyzed in several cities by the U.S. Forest Service. These values were converted to metric tons of carbon removed and sequestered per year by census block group." | AUTHOR'S DESCRIPTION: "Here we develop a method for assessing future scenarios of environmental management change that improve coastal ecosystem services and thereby, support the success of the SDGs. We illustrate application of the method using a case study of South Africa’s West Coast Rock Lobster fishery within the Table Mountain National Park (TMNP) Marine Protected Area...We calculated the retrospective and current value of the West Coast Rock Lobster fishery using published and unpublished data from various sources and combined the market worth of landed lobster from recreational fishers, small-scale fisheries (SSF), large-scale fisheries (LSF) and poachers. Then using the InVEST tool, we combined data to build scenarios that describe possible futures for the West Coast Rock Lobster fishery (see Table 1). The first scenario, entitled ‘Business as Usual’ (BAU), takes the current situation and most up-to-date data to model the future if harvest continues at the existing rate. The second scenario is entitled ‘Redirect the Poachers’ (RP), which attempts to model implementation of strict management, whereby poaching is minimised from the Marine Protected Area and other economic and nutritional sources are made available through government initiatives. The third scenario, entitled ‘Large Scale Cutbacks’ (LSC), excludes large-scale fisheries from harvesting West Coast Rock Lobster within the TMNP Marine Protected Area." | AUTHOR'S DESCRIPTION: "Natural habitats have the ability to protect coastal communities against the impacts of waves and storms, yet it is unclear how different habitats complement each other to reduce those impacts. Here, we investigate the individual and combined coastal protection services supplied by live corals on reefs, seagrass meadows, and mangrove forests during both non-storm and storm conditions, and under present and future sea-level conditions. Using idealized profiles of fringing and barrier reefs, we quantify the services supplied by these habitats using various metrics of inundation and erosion. We find that, together, live corals, seagrasses, and mangroves supply more protection services than any individual habitat or any combination of two habitats. Specifically, we find that, while mangroves are the most effective at protecting the coast under non-storm and storm conditions, live corals and seagrasses also moderate the impact of waves and storms, thereby further reducing the vulnerability of coastal regions. Also, in addition to structural differences, the amount of service supplied by habitats in our analysis is highly dependent on the geomorphic setting, habitat location and forcing conditions: live corals in the fringing reef profile supply more protection services than seagrasses; seagrasses in the barrier reef profile supply more protection services than live corals; and seagrasses, in our simulations, can even compensate for the long-term degradation of the barrier reef. Results of this study demonstrate the importance of taking integrated and place-based approaches when quantifying and managing for the coastal protection services supplied by ecosystems." | ABSTRACT: "Water resources in the Santa basin in the Peruvian Andes are increasingly under pressure from climate change and population increases. Impacts of temperature-driven glacier retreat on stream flow are better studied than those from precipitation changes, yet present and future water resources are mostly dependent on precipitation which is more difficult to predict with climate models. This study combines a broad range of projections from climate models with a hydrological model (WaterWorld), showing a general trend towards an increase in water availability due to precipitation increases over the basin. However, high uncertainties in these projections necessitate the need for basin-wide policies aimed at increased adaptability." AUTHOR'S DESCRIPTION: "WaterWorld is a fully distributed, process-based hydrological model that utilises remotely sensed and globally available datasets to support hydrological analysis and decision-making at national and local scales globally, with a particular focus on un-gauged and/or data-poor environments, which makes it highly suited to this study. The model (version 2) currently runs on either 10 degree tiles, large river basins or countries at 1-km2 resolution or 1 degree tiles at 1-ha resolution utilising different datasets. It simulates a hydrological baseline as a mean for the period 1950-2000 and can be used to calculate the hydrological impact of scenarios of climate change, land use change, land management options, impacts of extractives (oil & gas and mining) and impacts of changes in population and demography as well as combinations of these. The model is ‘self parameterising’ (Mulligan, 2013a) in the sense that all data required for model application anywhere in the world is provided with the model, removing a key barrier to model application. However, if users have better data than those provided, it is possible to upload these to WaterWorld as GIS files and use them instead. Results can be viewed visually within the web browser or downloaded as GIS maps. The model’s equations and processes are described in more detail in Mulligan and Burke (2005) and Mulligan (2013b). The model parameters are not routinely calibrated to observed flows as it is designed for hydrological scenario analysis in which the physical basis of its parameters must be retained and the model is also often used in un-gauged basins. Calibration is inappropriate under these circumstances (Sivapalan et al., 2003). The freely available nature of the model means that anyone can apply it and replicate the results shown here. WaterWorld’s (V2) snow and ice module is capable of simulating the processes of melt water production, snow fall and snow pack, making this version highly suited to the current application. The model component is based on a full energy-balance for snow accumulation and melting based on Walter et al., (2005) with input data provided globally by the SimTerra database (Mulligan, 2011) upon which the model r | ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level…In the first year of study, plants in flower and flower visitors were surveyed using the same transects as for the floral resources surveys. The transect was left undisturbed for 20 minutes following the initial plant survey to allow the flower visitors to return. Each transect was surveyed at a rate of approximately 3m/minute for 30 minutes. All insects observed to touch the sexual parts of flowers were either captured using a butterfly net and transferred into individually labeled specimen jars, or directly captured into the jars. After the survey was completed, those insects that could be identified in the field were recorded and released. The flower-visitor surveys were conducted in the morning, within 1 hour of midday, and in the afternoon to sample those insects active at different times. Insects that could not be identified in the field were collected as voucher specimens for later identification. Identifications were verified using reference collections and by taxon specialists. Relatively low capture rates in the first year led to methods being altered in the second year when surveying followed a spiral pattern from a randomly determined point on the sites, at a standard pace of 10 m/minute for 30 minutes, following Nielsen and Bascompte (2007) and Kalikhman (2007). Given a 2-m wide transect, an area of approximately 600m2 was sampled in each | ABSTRACT: "A major aim of European agri-environment policy is the management of grassland for botanical diversity conservation and restoration, together with the delivery of ecosystem services including soil carbon (C) sequestration. To test whether management for biodiversity restoration has additional benefits for soil C sequestration, we investigated C and nitrogen (N) accumulation rates in soil and C and N pools in vegetation in a long-term field experiment (16 years) in which fertilizer application and plant seeding were manipulated. In addition, the abundance of the legume Trifolium pratense was manipulated for the last 2 years. To unravel the mechanisms underlying changes in soil C and N pools, we also tested for effects of diversity restoration management on soil structure, ecosystem respiration and soil enzyme activities…" AUTHOR'S DESCRIPTION: "Measurements were made on 36 plots of 3 x 3 m comprising two management treatments (and their controls) in a long-term multifactorial grassland restoration experiment which have successfully increased plant species diversity, namely the cessation of NPK fertilizer application and the addition of seed mixtures…" | 
| 
             
                
                
                    
                    
                    Specific Policy or Decision Context Cited
                
                
             
           
     
                            
                                
                                    em.detail.policyDecisionContextHelp
                                
                                
                            
                            
                        ? | Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None identified | None identified | Not reported | None identifed | None identified | Land use change | None identified | Not applicable | None identified | None identified | None identified | None identified | None identified | Future rock lobster fisheries management | Future rock lobster fisheries management | None identified | None identified | None identified | 
| 
             
                
                
                    
                    
                    Biophysical Context
                
                
             
           
     | No additional description provided | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | No additional description provided | shallow bay (mean 3.7m), transition zone between warm temperate and tropical biogeographic provinces. Highly urbanized watershed | Estuarine intertidal, eelgrass, mudflat, sandflat and low marsh | Not additional description provided | No additional description provided | mangrove forest,Salt marsh, estuary, sea level, | No additional description provided | One airport site, one urban site, one site in deciduous leaf litter, and four sites in short grass ground cover. Measured sky view percentages ranged from 6% at the woods site, to 96% at the rural open site. | Estuarine environments and marsh-land interfaces | No additional description provided | No additional description provided | No additional description provided | barrier reef and fringing reef in nearshore coastal marine system | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | No additional description provided | Lolium perenne-Cynosorus cristatus grassland; The soil is a shallow brown-earth (average depth 28 cm) over limestone of moderate-high residual fertility. | 
| 
             
                
                
                    
                    
                    EM Scenario Drivers
                
                
             
           
     
                            
                                
                                    em.detail.scenarioDriverHelp
                                
                                
                            
                            
                        ? | Five scenarios that include urban growth boundaries and various combinations of unconstrainted development, fish conservation, agriculture and forest reserves. ? Comment:Additional alternatives included adding agricultural and forest reserves, and adding or removing urban growth boundaries to the three main scenarios. | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Future land use and land cover; climate change | No scenarios presented | Not applicable | Land Management (3); Climate Change (3) | No scenarios presented | Shellfish type; Changes to submerged aquatic vegetation (SAV) | No scenarios presented | No scenarios presented | Fisheries exploitation; fishing vulnerability (of age classes) | Reef type, Sea level increase, storm conditions, seagrass conditions, coral conditions, vegetation types and conditions | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | No scenarios presented | Additional benefits due to biodiversity restoration practices | 
| 
             
                
                
                
                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-12   | EM-82 | EM-84 | EM-91 | EM-102   | EM-103 | EM-111   | EM-137 | EM-154 | EM-208   | EM-306 | EM-397   | EM-449 | EM-493 | EM-541   | EM-542   | EM-630 | EM-709   | EM-735   | 
| 
             
                
                
                    
                    
                    Method Only, Application of Method or Model Run
                
                
             
           
     
                            
                                
                                    em.detail.methodOrAppHelp
                                
                                
                            
                            
                        ? | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application | Method + Application (multiple runs exist)  	     View EM Runs ? Comment:Runs differentiated by scenario combination. | Method + Application | Method + Application (multiple runs exist)  	     View EM Runs ? Comment:Ten runs; blue crab and penaeid shrimp, each combined with five different submerged aquatic vegetation habitat areas. | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | 
| 
             
                
                
                    
                    
                    New or Pre-existing EM?
                
                
             
           
     
                            
                                
                                    em.detail.newOrExistHelp
                                
                                
                            
                            
                        ? | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | New or revised model | Application of existing model | New or revised model | New or revised model | 
Related EMs (for example, other versions or derivations of this EM) described in ESML
| 
             
                
                
                    
                    
                    EM ID
                
                
             
           
     
                            
                                
                                    em.detail.idHelp
                                
                                
                            
                            
                        ? | EM-12   | EM-82 | EM-84 | EM-91 | EM-102   | EM-103 | EM-111   | EM-137 | EM-154 | EM-208   | EM-306 | EM-397   | EM-449 | EM-493 | EM-541   | EM-542   | EM-630 | EM-709   | EM-735   | 
| 
             
                
                
                    
                    
                    Document ID for related EM
                
                
             
           
     
                            
                                
                                    em.detail.relatedEmDocumentIdHelp
                                
                                
                            
                            
                        ? | Doc-47 | Doc-313 | Doc-314 ? Comment:Doc 183 is a secondary source for the Evoland model. | Doc-260 | Doc-272 ? Comment:Doc ID 272 was also used as a source document for this EM | Doc-123 | None | None | Doc-280 | Doc-307 | Doc-311 | Doc-338 | Doc-190 | Doc-223 | None | Doc-22 | Doc-23 ? Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. | Doc-220 | Doc-219 | Doc-218 | None | Doc-335 | Doc-345 | None | None | None | Doc-389 | None | 
| 
             
                
                
                    
                    
                    EM ID for related EM
                
                
             
           
     
                            
                                
                                    em.detail.relatedEmEmIdHelp
                                
                                
                            
                            
                        ? | EM-333 | EM-369 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-83 | None | None | None | None | EM-148 | EM-344 | EM-368 | EM-437 | EM-109 | EM-142 | EM-51 | None | EM-146 | EM-186 | EM-224 | None | EM-604 | EM-603 | EM-447 | EM-448 | None | None | None | None | EM-697 | None | 
EM Modeling Approach
| 
             
                
                
                
                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-12   | EM-82 | EM-84 | EM-91 | EM-102   | EM-103 | EM-111   | EM-137 | EM-154 | EM-208   | EM-306 | EM-397   | EM-449 | EM-493 | EM-541   | EM-542   | EM-630 | EM-709   | EM-735   | 
| 
             
                
                
                    
                    
                    EM Temporal Extent
                
                
             
           
     
                            
                                
                                    em.detail.tempExtentHelp
                                
                                
                            
                            
                        ? | 1990-2050 | Not reported | 1950-1993 | 1987-1997 | 2006-2011 | December 2007 - November 2008 | 2005-7; 2035-45 | Not applicable | 1990-2010 | 1990-2050 | May 5-Sept 30 2006 | 1950 - 2050 | 2006-2007, 2010 | 2010-2013 | 1986-2115 | 2005-2013 | 1950-2071 | 2007-2008 | 1990-2007 | 
| 
             
                
                
                    
                    
                    EM Time Dependence
                
                
             
           
     
                            
                                
                                    em.detail.timeDependencyHelp
                                
                                
                            
                            
                        ? | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | 
| 
             
                
                
                    
                    
                    EM Time Reference (Future/Past)
                
                
             
           
     
                            
                                
                                    em.detail.futurePastHelp
                                
                                
                            
                            
                        ? | future time | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | future time | future time | future time | Not applicable | Not applicable | future time | Not applicable | both | Not applicable | Not applicable | 
| 
             
                
                
                    
                    
                    EM Time Continuity
                
                
             
           
     
                            
                                
                                    em.detail.continueDiscreteHelp
                                
                                
                            
                            
                        ? | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | continuous | discrete | discrete | discrete | Not applicable | Not applicable | discrete | discrete | discrete | Not applicable | Not applicable | 
| 
             
                
                
                    
                    
                    EM Temporal Grain Size Value
                
                
             
           
     
                            
                                
                                    em.detail.tempGrainSizeHelp
                                
                                
                            
                            
                        ? | 2 | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | 1 | 1 | Varies by Run | Not applicable | Not applicable | 1 | 1 | 1 | Not applicable | Not applicable | 
| 
             
                
                
                    
                    
                    EM Temporal Grain Size Unit
                
                
             
           
     
                            
                                
                                    em.detail.tempGrainSizeUnitHelp
                                
                                
                            
                            
                        ? | Year | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Hour | Not applicable | Year | Hour | Year | Not applicable | Not applicable | Year | Second | Month | Not applicable | Not applicable | 
| 
             
                
                
                
                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-12   | EM-82 | EM-84 | EM-91 | EM-102   | EM-103 | EM-111   | EM-137 | EM-154 | EM-208   | EM-306 | EM-397   | EM-449 | EM-493 | EM-541   | EM-542   | EM-630 | EM-709   | EM-735   | 
| 
             
                
                
                
                    Bounding Type
                
             
           
     
                            
                            
                                em.detail.boundingTypeHelp
                            
                        ? | Geopolitical | Physiographic or Ecological | Geopolitical | Watershed/Catchment/HUC | Physiographic or Ecological | Physiographic or ecological | Watershed/Catchment/HUC | Not applicable | Physiographic or Ecological | Watershed/Catchment/HUC | Geopolitical | Physiographic or ecological | Physiographic or ecological | Geopolitical | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Other | 
| 
             
                
                
                
                    Spatial Extent Name
                
             
           
     
                            
                            
                                em.detail.extentNameHelp
                            
                        ? | Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Central French Alps | South Africa | Upper Mississippi River basin; St. Croix River Watershed | Tampa Bay | Yaquina Estuary (intertidal), Oregon, USA | Hood Canal | Not applicable | Tampa Bay | South Santiam watershed | Baltimore, MD | Gulf of Mexico (estuarine and coastal) | Coastal zone surrounding St. Croix | Durham NC and vicinity | Table Mountain National Park Marine Protected Area | Coast of Belize | Santa Basin | East Midlands | Colt Park meadows, Ingleborough National Nature Reserve, northern England | 
| 
             
                
                
                
                    Spatial Extent Area (Magnitude)
                
             
           
     
                            
                            
                                em.detail.extentAreaHelp
                            
                        ? | 10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | 1-10 km^2 | 100,000-1,000,000 km^2 | Not applicable | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | <1 ha | 
| 
             
                
                
                
                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-12   | EM-82 | EM-84 | EM-91 | EM-102   | EM-103 | EM-111   | EM-137 | EM-154 | EM-208   | EM-306 | EM-397   | EM-449 | EM-493 | EM-541   | EM-542   | EM-630 | EM-709   | EM-735   | 
| 
             
                
                
                    
                    
                    EM Spatial Distribution
                
                
             
           
     
                            
                                
                                    em.detail.distributeLumpHelp
                                
                                
                            
                            
                        ? | spatially distributed (in at least some cases) ? Comment:Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. | 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) ? Comment:Computations at this pixel scale pertain to certain variables specific to Mobile Bay. | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) ? Comment:Census block groups | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | 
| 
             
                
                
                
                    Spatial Grain Type
                
             
           
     
                            
                            
                                em.detail.spGrainTypeHelp
                            
                        ? | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | NHDplus v1 | area, for pixel or radial feature | other (habitat type) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | 
| 
             
                
                
                
                    Spatial Grain Size
                
             
           
     
                            
                            
                                em.detail.spGrainSizeHelp
                            
                        ? | varies | 20 m x 20 m | Distributed by catchments with average size of 65,000 ha | NHDplus v1 | 1 km^2 | 0.87-104.29 ha | 30 m x 30 m | 30 x 30 m | m^2 | 0.08 ha | 10m x 10m | 55.2 km^2 | 10 m x 10 m | irregular | Not applicable | 1 meter | 1 km2 | multiple unrelated locations | 3 m x 3 m | 
| 
             
                
                
                
                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-12   | EM-82 | EM-84 | EM-91 | EM-102   | EM-103 | EM-111   | EM-137 | EM-154 | EM-208   | EM-306 | EM-397   | EM-449 | EM-493 | EM-541   | EM-542   | EM-630 | EM-709   | EM-735   | 
| 
             
                
                
                    
                    
                    EM Computational Approach
                
                
             
           
     
                            
                                
                                    em.detail.emComputationalApproachHelp
                                
                                
                            
                            
                        ? | Numeric | Analytic | Numeric | Numeric | Analytic | Analytic | Analytic | Numeric | Analytic | Numeric | Analytic | Numeric | Analytic | Numeric | Numeric | Analytic | * | Analytic | Analytic | 
| 
             
                
                
                    
                    
                    EM Determinism
                
                
             
           
     
                            
                                
                                    em.detail.deterStochHelp
                                
                                
                            
                            
                        ? | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | 
| 
             
                
                
                
                    Statistical Estimation of EM
                
             
           
     
                            
                            
                                em.detail.statisticalEstimationHelp
                            
                        ? | 
 Comment:Agent based modeling results in response indices. | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | None | 
 | 
 | 
| 
             
                
                
                
                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-12   | EM-82 | EM-84 | EM-91 | EM-102   | EM-103 | EM-111   | EM-137 | EM-154 | EM-208   | EM-306 | EM-397   | EM-449 | EM-493 | EM-541   | EM-542   | EM-630 | EM-709   | EM-735   | 
| 
             
                
                
                
                    Model Calibration Reported?
                
             
           
     
                            
                            
                                em.detail.calibrationHelp
                            
                        ? | Unclear | No | No | Yes | No | Unclear | Yes | Not applicable | No | No | Yes | Yes | Yes | No | No | No | No | Not applicable | Not applicable | 
| 
             
                
                
                    
                    
                    Model Goodness of Fit Reported?
                
                
             
           
     
                            
                                
                                    em.detail.goodnessFitHelp
                                
                                
                            
                            
                        ? | No | No | No | Yes | No | No | No | Not applicable | No | No | Yes | No | No | No | No | No | No | Not applicable | Not applicable | 
| 
             
                
                
                    
                    
                    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 | 
| 
             
                
                
                    
                    
                    Model Operational Validation Reported?
                
                
             
           
     
                            
                                
                                    em.detail.validationHelp
                                
                                
                            
                            
                        ? | No | No | No | No | No | No | Yes | Not applicable | No | No | No | No | Yes | No | Yes ? Comment:A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data. | No ? Comment:Used the SWAN model (see below for referenece) with Generation 1 or 2 wind-wave formulations to validate the wave development portion of the model. Booij N, Ris RC, Holthuijsen LH. A third-generation wave model for coastal regions 1. Model description and validation. J Geophys Res. American Geophysical Union; 1999;104: 7649?7666. | Yes | Not applicable | No | 
| 
             
                
                
                    
                    
                    Model Uncertainty Analysis Reported?
                
                
             
           
     
                            
                                
                                    em.detail.uncertaintyAnalysisHelp
                                
                                
                            
                            
                        ? | No | No | No | No | No | No | No | Not applicable | Yes | No | No | No | No | No | No | No | No | Not applicable | No | 
| 
             
                
                
                    
                    
                    Model Sensitivity Analysis Reported?
                
                
             
           
     
                            
                                
                                    em.detail.sensAnalysisHelp
                                
                                
                            
                            
                        ? | No ? Comment:Sensitivity analysis performed for agent values only. | No | No | No ? Comment:Some model coefficients serve, by their magnitude, to indicate the proportional impact on the final result of variation in the parameters they modify. | No | No | Yes | Not applicable | Yes | No | No | No | No | No | No | No | No | Not applicable | No | 
| 
             
                
                
                
                    Model Sensitivity Analysis Include Interactions?
                
             
           
     
                            
                            
                                em.detail.interactionConsiderHelp
                            
                        ? | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | No | N/A | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-12   | EM-82 | EM-84 | EM-91 | EM-102   | EM-103 | EM-111   | EM-137 | EM-154 | EM-208   | EM-306 | EM-397   | EM-449 | EM-493 | EM-541   | EM-542   | EM-630 | EM-709   | EM-735   | 
| 
 | 
 | 
 | 
 | 
 | 
 | 
 | None | 
 | 
 | 
 | 
 | None | 
 | None | 
 | None | 
 | 
 | 
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-12   | EM-82 | EM-84 | EM-91 | EM-102   | EM-103 | EM-111   | EM-137 | EM-154 | EM-208   | EM-306 | EM-397   | EM-449 | EM-493 | EM-541   | EM-542   | EM-630 | EM-709   | EM-735   | 
| None | None | None | None | 
 | 
 | 
 | None | 
 Comment:Realm: Tropical Atlantic Region: West Tropical Atlantic Province: Tropical Northwestern Atlantic Ecoregion: Floridian | None | None | 
 | 
 | None | 
 | 
 | None | None | None | 
Centroid Lat/Long (Decimal Degree)
| 
             
                
                
                
                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-12   | EM-82 | EM-84 | EM-91 | EM-102   | EM-103 | EM-111   | EM-137 | EM-154 | EM-208   | EM-306 | EM-397   | EM-449 | EM-493 | EM-541   | EM-542   | EM-630 | EM-709   | EM-735   | 
| 
             
                
                
                    
                    
                    Centroid Latitude
                
                
             
           
     
                            
                                
                                    em.detail.ddLatHelp
                                
                                
                            
                            
                        ? | 44.11 | 45.05 | -30 | 42.5 | 27.74 | 44.62 | 47.8 | -9999 | 27.8 | 44.24 | 39.28 | 30.44 | 17.73 | 35.99 | -34.18 | 18.63 | -9.05 | 52.22 | 54.2 | 
| 
             
                
                
                    
                    
                    Centroid Longitude
                
                
             
           
     
                            
                                
                                    em.detail.ddLongHelp
                                
                                
                            
                            
                        ? | -123.09 | 6.4 | 25 | -90.63 | -82.57 | -124.06 | -122.7 | -9999 | -82.4 | -122.24 | -76.62 | -87.99 | -64.77 | -78.96 | 18.35 | -88.22 | -77.81 | -0.91 | -2.35 | 
| 
             
                
                
                    
                    
                    Centroid Datum
                
                
             
           
     
                            
                                
                                    em.detail.datumHelp
                                
                                
                            
                            
                        ? | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 | Not applicable | WGS84 | None provided | WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | 
| 
             
                
                
                    
                    
                    Centroid Coordinates Status
                
                
             
           
     
                            
                                
                                    em.detail.coordinateStatusHelp
                                
                                
                            
                            
                        ? | Estimated | Provided | Estimated | Estimated | Estimated | Provided | Estimated | Not applicable | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Provided | 
| 
             
                
                
                
                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-12   | EM-82 | EM-84 | EM-91 | EM-102   | EM-103 | EM-111   | EM-137 | EM-154 | EM-208   | EM-306 | EM-397   | EM-449 | EM-493 | EM-541   | EM-542   | EM-630 | EM-709   | EM-735   | 
| 
             
                
                
                    
                    
                    EM Environmental Sub-Class
                
                
             
           
     
                            
                                
                                    em.detail.emEnvironmentalSubclassHelp
                                
                                
                            
                            
                        ? | Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Agroecosystems | Grasslands | Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Rivers and Streams | Ground Water | Created Greenspace | Near Coastal Marine and Estuarine | Forests | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Atmosphere | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Created Greenspace | Atmosphere | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | None | Created Greenspace | Grasslands | Agroecosystems | Grasslands | 
| 
             
                
                
                    
                    
                    Specific Environment Type
                
                
             
           
     
                            
                                
                                    em.detail.specificEnvTypeHelp
                                
                                
                            
                            
                        ? | Agricultural-urban interface at river junction | Subalpine terraces, grasslands, and meadows. | Not reported | None | Habitat Zones (Low, Med, High, Optimal) around seagrass and emergent marsh | Estuarine intertidal | glacier-carved saltwater fjord | Urban watersheds | Created Mangrove wetlands | primarily Conifer Forest | Urban landscape and surrounding area | Submerged aquatic vegetation in estuaries and coastal lagoons | Coral reefs | Urban and vicinity | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | coral reefs | tropical, coastal to montane | restored landfills and grasslands | fertilized grassland (historically hayed) | 
| 
             
                
                
                    
                    
                    EM Ecological Scale
                
                
             
           
     
                            
                                
                                    em.detail.ecoScaleHelp
                                
                                
                            
                            
                        ? | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecosystem | Zone within an ecosystem | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Other or unclear (comment) | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | 
Scale of differentiation of organisms modeled
| 
             
                
                
                
                    EM ID
                
             
           
     
                            
                            
                                em.detail.idHelp
                            
                        ? | EM-12   | EM-82 | EM-84 | EM-91 | EM-102   | EM-103 | EM-111   | EM-137 | EM-154 | EM-208   | EM-306 | EM-397   | EM-449 | EM-493 | EM-541   | EM-542   | EM-630 | EM-709   | EM-735   | 
| 
             
                
                
                    
                    
                    EM Organismal Scale
                
                
             
           
     
                            
                                
                                    em.detail.orgScaleHelp
                                
                                
                            
                            
                        ? | Not applicable | Community | Not applicable | Not applicable | Species | Guild or Assemblage | Not applicable | Community | Not applicable | Species | Not applicable | Species | Not applicable | Not applicable | Individual or population, within a species | Guild or Assemblage | Not applicable | Individual or population, within a species | Community | 
Taxonomic level and name of organisms or groups identified
| EM-12   | EM-82 | EM-84 | EM-91 | EM-102   | EM-103 | EM-111   | EM-137 | EM-154 | EM-208   | EM-306 | EM-397   | EM-449 | EM-493 | EM-541   | EM-542   | EM-630 | EM-709   | EM-735   | 
| 
 | 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-12   | EM-82 | EM-84 | EM-91 | EM-102   | EM-103 | EM-111   | EM-137 | EM-154 | EM-208   | EM-306 | EM-397   | EM-449 | EM-493 | EM-541   | EM-542   | EM-630 | EM-709   | EM-735   | 
| 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | 
 | None | 
 | 
 | 
<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-12   | EM-82 | EM-84 | EM-91 | EM-102   | EM-103 | EM-111   | EM-137 | EM-154 | EM-208   | EM-306 | EM-397   | EM-449 | EM-493 | EM-541   | EM-542   | EM-630 | EM-709   | EM-735   | 
| 
 | None | 
 | None | 
 | 
 | 
 | 
 | 
 | None | 
 | 
 | 
 | None | 
 | 
 | None | None | None | 
 
    
         Home
Home Search EMs
Search EMs My
                                EMs
My
                                EMs  Learn about
                            ESML
Learn about
                            ESML View ESML Data Map
View ESML Data Map Show Criteria
Show Criteria Hide Criteria
Hide Criteria