EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
EM Identity and Description
EM ID
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EM-79 | EM-86 |
EM-102 ![]() |
EM-123 |
EM-333 ![]() |
EM-367 |
EM-397 ![]() |
EM-414 | EM-415 | EM-432 | EM-452 |
EM-496 ![]() |
EM-604 | EM-703 | EM-837 | EM-849 | EM-858 | EM-885 |
EM Short Name
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Divergence in flowering date, Central French Alps | Area and hotspots of soil retention, South Africa | Fish species habitat value, Tampa Bay, FL, USA | Land-use change and wildlife products, Europe | Evoland v3.5 (unbounded growth), Eugene, OR, USA | InVEST Coastal Blue Carbon | Wetland shellfish production, Gulf of Mexico, USA | SAV occurrence, St. Louis River, MN/WI, USA | Esocid spawning, St. Louis River, MN/WI, USA | Nitrogen fixation rates, Guánica Bay, Puerto Rico | Relative reef sand generation, St. Croix, USVI | Sed. denitrification, St. Louis R., MN/WI, USA | Chinook salmon value (household), Yaquina Bay, OR | Gadwall duck recruits, CREP wetlands, Iowa, USA | Bird species diversity on restored landfills, UK | InVEST Coastal Vulnerability | ARIES Flood Reg, Santa Fe, NM | Regional Human well being index for U.S. |
EM Full Name
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Functional divergence in flowering date, Central French Alps | Area and hotspots of soil retention, South Africa | Fish species habitat value, Tampa Bay, FL, USA | Land-use change effects on wildlife products, Europe | Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA | InVEST v3.0 Coastal Blue Carbon | Wetland shellfish production, Gulf of Mexico, USA | Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA | Esocid spawning, St. Louis River estuary, MN & WI, USA | Nitrogen fixation rates, Guánica Bay, Puerto Rico, USA | Relative sand generation (of reef), St. Croix, USVI | Sediment denitrification, St. Louis River, MN/WI, USA | Economic value of Chinook salmon per household method, Yaquina Bay, OR | Gadwall duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Bird species diversity on restored landfills compared to paired reference sites, East Midlands, UK | InVEST Coastal Vulnerability | ARIES Flood regulation, Santa Fe, New Mexico | Human well being index for geographic regions, United States |
EM Source or Collection
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EU Biodiversity Action 5 | None | US EPA | EU Biodiversity Action 5 | Envision | InVEST |
US EPA ?Comment:Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science |
US EPA | US EPA | US EPA | US EPA | US EPA | US EPA | None | None | InVEST | None | US EPA |
EM Source Document ID
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260 | 271 | 187 | 228 |
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
310 | 324 | 330 | 332 |
338 ?Comment:WE received a draft copy prior to journal publication that was agency reviewed. |
335 | 333 | 324 |
372 ?Comment:Document 373 is a secondary source for this EM. |
406 | 408 | 411 | 421 |
Document Author
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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. | Fulford, R., Yoskowitz, D., Russell, M., Dantin, D., and Rogers, J. | Haines-Young, R., Potschin, M. and Kienast, F. | Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Natural Capital Project | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke | Ted R. Angradi, David W. Bolgrien, Jonathon J. Launspach, Brent J. Bellinger, Matthew A. Starry, Joel C. Hoffman, Mike E. Sierszen, Anett S. Trebitz, and Tom P. Hollenhorst | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Brent J. Bellinger, Terri M. Jicha, LaRae P. Lehto, Lindsey R. Seifert-Monson, David W. Bolgrien, Matthew A. Starry, Theodore R. Angradi, Mark S. Pearson, Colleen Elonen, and Brian H. Hill | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Rahman, M. L., S. Tarrant, D. McCollin, and J. Ollerton | The Natural Capital Project.org | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. | Smith, L.M., Harwell, L.C., Summers, J.K., Smith, H.M., Wade, C.M., Straub, K.R. and J.L. Case |
Document Year
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2011 | 2008 | 2016 | 2012 | 2008 | 2014 | 2012 | 2013 | 2016 | 2017 | 2014 | 2014 | 2012 | 2010 | 2011 | None | 2018 | 2014 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Mapping ecosystem services for planning and management | Habitat and recreational fishing opportunity in Tampa Bay: Linking ecological and ecosystem services to human beneficiaries | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Blue Carbon model - InVEST (v3.0) | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary | Mapping ecosystem service indicators of a Great Lakes estuarine Area of Concern | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Sediment nitrification and denitrification in a Lake Superior estuary | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | 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 | InVEST Coastal Vulnerability | Towards globally customizable ecosystem service models | A U.S. Human Well-being index (HWBI) for multiple scales: linking service provisioning to human well-being endpoints (2000-2010) |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | 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 |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | other | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Website users guide | Published journal manuscript | Published EPA report |
EM ID
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EM-79 | EM-86 |
EM-102 ![]() |
EM-123 |
EM-333 ![]() |
EM-367 |
EM-397 ![]() |
EM-414 | EM-415 | EM-432 | EM-452 |
EM-496 ![]() |
EM-604 | EM-703 | EM-837 | EM-849 | EM-858 | EM-885 |
Not applicable | Not applicable | Not applicable | Not applicable | http://evoland.bioe.orst.edu/ | http://ncp-dev.stanford.edu/~dataportal/invest-releases/documentation/current_release/blue_carbon.html#running-the-model | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://naturalcapitalproject.stanford.edu/software/invest |
https://integratedmodelling.org/hub/#/register ?Comment:Need to set up an account first and then can access the main integrated modelling hub page: |
Not applicable | |
Contact Name
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Sandra Lavorel | Benis Egoh | Richard Fulford | Marion Potschin | Michael R. Guzy | Gregg Verutes | Stephen J. Jordan | Ted R. Angradi | Ted R. Angradi | Susan H. Yee | Susan H. Yee |
Brent J. Bellinger ?Comment:Ph# +1 218 529 5247. Other current address: Superior Water, Light and Power Company, 2915 Hill Ave., Superior, WI 54880, USA. |
Stephen Jordan | David Otis | Lutfor Rahman | Not applicable | Javier Martinez-Lopez | Lisa Smith |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | USEPA Gulf Ecology Division, Gulf Breeze, FL 32561 | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Oregon State University, Dept. of Biological and Ecological Engineering | Stanford University | U.S. Environmental Protection Agency, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA | U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA | United States Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboraty, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804 USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA | U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Landscape and Biodiversity Research Group, School of Science and Technology, The University of Northampton, Avenue Campus, Northampton NN2 6JD, UK | Not applicable | BC3-Basque Centre for Climate Change, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain | 1 Sabine Island Dr, Gulf Breeze, FL 32561 |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | Not reported | Fulford.Richard@epa.gov | marion.potschin@nottingham.ac.uk | Not reported | gverutes@stanford.edu | jordan.steve@epa.gov | angradi.theodore@epa.gov | angradi.theodore@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | bellinger.brent@epa.gov | jordan.steve@epa.gov | dotis@iastate.edu | lutfor.rahman@northampton.ac.uk | Not applicable | javier.martinez@bc3research.org | smith.lisa@epa.gov |
EM ID
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EM-79 | EM-86 |
EM-102 ![]() |
EM-123 |
EM-333 ![]() |
EM-367 |
EM-397 ![]() |
EM-414 | EM-415 | EM-432 | EM-452 |
EM-496 ![]() |
EM-604 | EM-703 | EM-837 | EM-849 | EM-858 | EM-885 |
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, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Functional divergence of flowering date was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | AUTHOR'S DESCRIPTION: "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…Soil retention was modelled as a function of vegetation or litter cover and soil erosion potential. Schoeman et al. (2002) modelled soil erosion potential and derived eight erosion classes, ranging from low to severe erosion potential for South Africa. The vegetation cover was mapped by ranking vegetation types using expert knowledge of their ability to curb erosion. We used Schulze (2004) index of litter cover which estimates the soil surface covered by litter based on observations in a range of grasslands, woodlands and natural forests. According to Quinton et al. (1997) and Fowler and Rockstrom (2001) soil erosion is slightly reduced with about 30%, significantly reduced with about 70% vegetation cover. The range of soil retention was mapped by selecting all areas that had vegetation or litter cover of more than 30% for both the expert classified vegetation types and litter accumulation index within areas with moderate to severe erosion potential. The hotspot was mapped as areas with severe erosion potential and vegetation/litter cover of at least 70% where maintaining the cover is essential to prevent erosion. An assumption was made that the potential for this service is relatively low in areas with little natural vegetation or litter cover." | 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." | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The novel aspect of this work is an analysis of whether the historical and the projected land use changes…are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Wildlife products); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000." AUTHOR'S DESCRIPTION: "Wildlife products belongs to the service group Biotic Materials in the CICES system; it includes the provisioning of all non-edible raw material products that are gained through non-agricultural practices or which are produced as a by-product of commercial and non-commercial forests, primarily in non-intensively used land or semi-natural and natural areas….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers." | **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." | 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 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: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake." | ABSTRACT: "Estuaries provide multiple ecosystem services from which humans benefit…We described an approach, with examples, for assessing how local-scale actions affect the extent and distribution of coastal ecosystem services, using the St. Louis River estuary (SLRE) of western Lake Superior as a case study. We based our approach on simple models applied to spatially explicity biophysical data that allows us to map the providing area of ecosystem services at high resolution (10-m^2 pixel) across aquatic and riparian habitats…Aspects of our approach can be adapted by communities for use in support of local decision-making." AUTHOR'S DESCRIPTION: "We derived the decision criteria used to map the IEGS habitat proxy of esocid spawning from habitat suitability information for two species that have similar but not identical spawning habitat and behavior." | AUTHOR'S DESCRIPTION: " …In Guánica Bay watershed, Puerto Rico, deforestation and drainage of a large lagoon have led to sediment, contaminant, and nutrient transport into the bay, resulting in declining quality of coral reefs. A watershed management plan is currently being implemented to restore reefs through a variety of proposed actions…After the workshops, fifteen indicators of terrestrial ecosystem services in the watershed and four indicators in the coastal zone were identified to reflect the wide range of stakeholder concerns that could be impacted by management decisions. Ecosystem service production functions were applied to quantify and map ecosystem services supply in the Guánica Bay watershed, as well as an additional highly engineered upper multi-watershed area connected to the lower watershed via a series of reservoirs and tunnels,…” AUTHOR''S DESCRIPTION: "The U.S. Coral Reef Task Force (CRTF), a collaboration of federal, state and territorial agencies, initiated a program in 2009 to better incorporate land-based sources of pollution and socio-economic considerations into watershed strategies for coral reef protection (Bradley et al., 2016)...Baseline measures for relevant ecosystem services were calculated by parameterizing existing methods, largely based on land cover (Egoh et al., 2012; Martinez- Harms and Balvanera, 2012), with relevant rates of ecosystem services production for Puerto Rico, and applying them to map ecosystem services supply for the Guánica Bay Watershed...The Guánica Bay watershed is a highly engineered watershed in southwestern Puerto Rico, with a series of five reservoirs and extensive tunnel systems artificially connecting multiple mountainous sub-watersheds to the lower watershed of the Rio Loco, which itself is altered by an irrigation canal and return drainage ditch that diverts water through the Lajas Valley (PRWRA, 1948)...For each objective, a translator of ecosystem services production, i.e., ecological production function, was used to quantify baseline measurements of ecosystem services supply from land use/land cover (LULC) maps for watersheds across Puerto Rico...Two additional metrics, nitrogen fixation and rates of carbon sequestration into soil and sediment, were also calculated as potential measures of soil quality and agricultural productivity. Carbon sequestration and nitrogen fixation rates were assigned to each land cover class" | 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)…Synthesis of scientific literature and expert opinion can be used to estimate the relative potential for recreational opportunities across different benthic habitat types (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to recreational opportunities as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative recreational opportunity j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A.palmata, Montastraea reef, patch reef, and dense or sparse gorgonians), and Mij is the magnitude associated with each habitat for a given metric j: sand generation" | ABSTRACT: "Inorganic nitrogen (N) transformations and removal in aquatic sediments are microbially mediated, and rates influence N-transport. In this study we related physicochemical properties of a large Great Lakes embayment, the St. Louis River Estuary (SLRE) of western Lake Superior, to sediment N-transformation rates. We tested for associations among rates and N-inputs, vegetation biomass, and temperature. We measured rates of nitrification (NIT), unamended base denitrification (DeNIT), and potential denitrification [denitrifying enzyme activity (DEA)] in 2011 and 2012 across spatial and depth zones…Nitrogen cycling rates were spatially and temporally variable, but we modeled how alterations to water depth and N-inputs may impact DeNIT rates." AUTHOR'S DESCRIPTION: "We used different survey designs in 2011 and 2012. Both designs were based on area-weighted probability sampling methods, similar to those developed for EPA's Environmental Monitoring and Assessment Program (EMAP) (Crane et al., 2005; Stevens and Olsen, 2003, 2004). Sampling sites were assigned to spatial zones: “harbor” (river km 0–13), “bay” (river km 13–24), or “river” (river km 24–35) (Fig. 1). Sites were also grouped by depth zones (“shallow,” <1 m; “intermediate,” 1–2 m; and “deep,” >2 m). In 2011 (“vegetated-habitat survey”), the sample frame consisted of areas of emergent and submergent vegetation in the SLRE… The resulting sample frame included 2370 ha of potentially vegetated area out of a total SLRE area of 4378 ha. Sixty sites were distributed across the total vegetated area in each spatial zone using an uneven spatially balanced probabilistic design. Vegetated areas were more prevalent, and thus had greater sampling effort, in the bay (n = 33) and river (n = 17) than harbor (n=10) zones, and in the shallow (n=44) and intermediate (n =14) than deep (n =2) zones. All sampling was done in July. In 2012 a probabilistic sampling design (“estuary-wide survey”) was implemented to determine N-cycling rates for the entire SLRE (not just vegetated areas as in 2011). Thirty sites unevenly distributed across spatial and depth zones were sampled monthly in May–September (Fig. 1). Area weighting for each sampled site reflects the SLRE area attributable to each sample by month, spatial zone, and depth zone." "…we were able to create significant predictive models for NIT and DeNIT rates using linear combinations of physiochemical parameters…" "…Simulations of changes in DeNIT rates in response to altered water depth and surface NOx-N concentration for spring (Fig. 4A) and summer (Fig. 4B) show that for a given season, altering water depths would have a greater influence on DeNIT than rising NO3- concentration." | 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: "Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2*R*n where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = H*Z*B/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632). | 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)." | Faced with an intensification of human activities and a changing climate, coastal communities need to better understand how modifications of the biological and physical environment (i.e. direct and indirect removal of natural habitats for coastal development) can affect their exposure to storm-induced erosion and flooding (inundation). The InVEST Coastal Vulnerability model produces a qualitative estimate of such exposure in terms of a vulnerability index, which differentiates areas with relatively high or low exposure to erosion and inundation during storms. By coupling these results with global population information, the model can show areas along a given coastline where humans are most vulnerable to storm waves and surge. The model does not take into account coastal processes that are unique to a region, nor does it predict long- or short-term changes in shoreline position or configuration. Model inputs, which serve as proxies for various complex shoreline processes that influence exposure to erosion and inundation, include: a polyline with attributes about local coastal geomorphology along the shoreline, polygons representing the location of natural habitats (e.g., seagrass, kelp, wetlands, etc.), rates of (observed) net sea-level change, a depth contour that can be used as an indicator for surge level (the default contour is the edge of the continental shelf), a digital elevation model (DEM) representing the topography of the coastal area, a point shapefile containing values of observed storm wind speed and wave power, and a raster representing population distribution. Outputs can be used to better understand the relative contributions of these different model variables to coastal exposure and highlight the protective services offered by natural habitats to coastal populations. This information can help coastal managers, planners, landowners and other stakeholders identify regions of greater risk to coastal hazards, which can in turn better inform development strategies and permitting. The results provide a qualitative representation of coastal hazard risks rather than quantifying shoreline retreat or inundation limits. | ABSTRACT: "Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The Artificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multicriteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed. " | 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." |
Specific Policy or Decision Context Cited
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None identified | None identified | None identifed | None identified | 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 | None identified | Federal delisting of an area of concern (AOC) | None provided | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None reported |
Biophysical Context
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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. | shallow bay (mean 3.7m), transition zone between warm temperate and tropical biogeographic provinces. Highly urbanized watershed | No additional description provided | No additional description provided | Land use land class; habitat type | Estuarine environments and marsh-land interfaces | submerged aquatic vegetation | No additional description provided | No additional description provided | No additional description provided | No additional description provided | Yaquina Bay estuary | Prairie Pothole Region of Iowa | 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 | Watersheds surrounding Santa Fe and Albuquerque, New Mexico | Not applicable |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | Recent historical land-use change from 1990-2000 | Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves. | Land use land cover changes; habitat disturbance | Shellfish type; Changes to submerged aquatic vegetation (SAV) | No scenarios presented | The effect of habitat restoration on esocid spawning area was simulated by varying biophysical changes. | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Options for future sea level change and population change | N/A | geographic region |
EM ID
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EM-79 | EM-86 |
EM-102 ![]() |
EM-123 |
EM-333 ![]() |
EM-367 |
EM-397 ![]() |
EM-414 | EM-415 | EM-432 | EM-452 |
EM-496 ![]() |
EM-604 | EM-703 | EM-837 | EM-849 | EM-858 | EM-885 |
Method Only, Application of Method or Model Run
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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 (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 | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method Only | Method Only | Method + Application | Method + Application |
New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised 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 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-79 | EM-86 |
EM-102 ![]() |
EM-123 |
EM-333 ![]() |
EM-367 |
EM-397 ![]() |
EM-414 | EM-415 | EM-432 | EM-452 |
EM-496 ![]() |
EM-604 | EM-703 | EM-837 | EM-849 | EM-858 | EM-885 |
Document ID for related EM
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Doc-260 | Doc-269 |
Doc-271 ?Comment:Document 273 used for source information on soil erosion potential variable |
None | Doc-228 | Doc-238 | Doc-239 | Doc-240 | Doc-241 | Doc-242 |
Doc-183 | Doc-47 | Doc-313 | Doc-314 ?Comment:Doc 183 is a secondary source for the Evoland model. |
None | None | None | None | None | None | None | Doc-324 | Doc-372 | Doc-373 | Doc-406 | Doc-410 | None | Doc-418 |
EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83 | EM-85 | EM-87 | EM-88 | None | EM-122 | EM-124 | EM-125 | EM-162 | EM-164 | EM-165 | EM-166 | EM-170 | EM-171 | EM-99 | EM-119 | EM-120 | EM-121 | EM-12 | EM-369 | None | EM-604 | EM-603 | None | None | None | None | None | EM-603 | EM-397 | EM-705 | EM-704 | EM-702 | EM-701 | EM-700 | EM-632 | EM-836 | EM-851 | EM-859 | None |
EM Modeling Approach
EM ID
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EM-79 | EM-86 |
EM-102 ![]() |
EM-123 |
EM-333 ![]() |
EM-367 |
EM-397 ![]() |
EM-414 | EM-415 | EM-432 | EM-452 |
EM-496 ![]() |
EM-604 | EM-703 | EM-837 | EM-849 | EM-858 | EM-885 |
EM Temporal Extent
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2007-2008 | Not reported | 2006-2011 | 1990-2000 | 1990-2050 | Not applicable | 1950 - 2050 | 2010 - 2012 | 2013 | 1978 - 2009 | 2006-2007, 2010 |
July 2011 to September 2012 ?Comment:All sampling performed July 2011, and May-September 2012. |
2003-2008 | 1987-2007 | Not applicable | Not applicable | 1981-2015 | 2000-2010 |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | 2 | 1 | Varies by Run | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Year | Year | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
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EM-79 | EM-86 |
EM-102 ![]() |
EM-123 |
EM-333 ![]() |
EM-367 |
EM-397 ![]() |
EM-414 | EM-415 | EM-432 | EM-452 |
EM-496 ![]() |
EM-604 | EM-703 | EM-837 | EM-849 | EM-858 | EM-885 |
Bounding Type
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Physiographic or Ecological | Geopolitical | Physiographic or Ecological | Geopolitical | Geopolitical | Not applicable | Physiographic or ecological | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Not applicable | Not applicable | Watershed/Catchment/HUC | Geopolitical |
Spatial Extent Name
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Central French Alps | South Africa | Tampa Bay | The EU-25 plus Switzerland and Norway | Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Not applicable | Gulf of Mexico (estuarine and coastal) | St. Louis River Estuary | St. Louis River estuary | Guanica Bay watershed | Coastal zone surrounding St. Croix | St. Louis River Estuary (of western Lake Superior) | Pacific Northwest | CREP (Conservation Reserve Enhancement Program | Not applicable | Not applicable | Santa Fe Fireshed | Continental U.S. |
Spatial Extent Area (Magnitude)
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10-100 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. | >1,000,000 km^2 | 10-100 km^2 | Not applicable | 10,000-100,000 km^2 | 10-100 km^2 | 10-100 km^2 | 100-1000 km^2 | 100-1000 km^2 | 10-100 km^2 | >1,000,000 km^2 | 10,000-100,000 km^2 | Not applicable | Not applicable | 100-1000 km^2 | >1,000,000 km^2 |
EM ID
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EM-79 | EM-86 |
EM-102 ![]() |
EM-123 |
EM-333 ![]() |
EM-367 |
EM-397 ![]() |
EM-414 | EM-415 | EM-432 | EM-452 |
EM-496 ![]() |
EM-604 | EM-703 | EM-837 | EM-849 | EM-858 | EM-885 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially 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) ?Comment:BH: Each individual transect?s data was parceled into location reports, and that each report?s ?quadrat? area was dependent upon the angle of the hydroacoustic sampling beam. The spatial grain is 0.07 m^2, 0.20 m^2 and 0.70 m^2 for depths of 1 meter, 2 meters and 3 meters, respectively. |
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 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
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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | volume, for 3-D 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) | 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) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
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20 m x 20 m | Distributed across catchments with average size of 65,000 ha | 1 km^2 | 1 km x 1 km | varies | user-specified | 55.2 km^2 | 0.07 m^2 to 0.70 m^2 | 10 m x 10 m | HUC | 10 m x 10 m | 35 km river estuary reach, 0 to 5 m depth by 1 m increment | Not applicable | multiple, individual, irregular sites | multiple unrelated sites | user defined | 30 m | county |
EM ID
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EM-79 | EM-86 |
EM-102 ![]() |
EM-123 |
EM-333 ![]() |
EM-367 |
EM-397 ![]() |
EM-414 | EM-415 | EM-432 | EM-452 |
EM-496 ![]() |
EM-604 | EM-703 | EM-837 | EM-849 | EM-858 | EM-885 |
EM Computational Approach
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Analytic | Analytic | Analytic | Logic- or rule-based | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-79 | EM-86 |
EM-102 ![]() |
EM-123 |
EM-333 ![]() |
EM-367 |
EM-397 ![]() |
EM-414 | EM-415 | EM-432 | EM-452 |
EM-496 ![]() |
EM-604 | EM-703 | EM-837 | EM-849 | EM-858 | EM-885 |
Model Calibration Reported?
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No | No | No | No | Unclear | Not applicable | Yes | Yes | No | No | Yes | Yes | No | Unclear | Not applicable | Not applicable | Unclear | No |
Model Goodness of Fit Reported?
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Yes | No | No | No | No | Not applicable | No | Yes | No | No | No | Yes | No | No | Not applicable | Not applicable | No | No |
Goodness of Fit (metric| value | unit)
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None | None | None | None | None | None |
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None | None | None |
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None | None | None | None | None | None |
Model Operational Validation Reported?
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No | No | No | No | No | Not applicable | No | Yes | No | No | Yes | No | Yes | No | Not applicable | Not applicable | No | No |
Model Uncertainty Analysis Reported?
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No | No | No | No | No | Not applicable | No | No | No | No | No | No | No | No | Not applicable | Not applicable | No | Unclear |
Model Sensitivity Analysis Reported?
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No | No | No | No | No | Not applicable | No | No | No | No | No | No | No | No | Not applicable | Not applicable | No | Yes |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Yes |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-79 | EM-86 |
EM-102 ![]() |
EM-123 |
EM-333 ![]() |
EM-367 |
EM-397 ![]() |
EM-414 | EM-415 | EM-432 | EM-452 |
EM-496 ![]() |
EM-604 | EM-703 | EM-837 | EM-849 | EM-858 | EM-885 |
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None |
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None |
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None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-79 | EM-86 |
EM-102 ![]() |
EM-123 |
EM-333 ![]() |
EM-367 |
EM-397 ![]() |
EM-414 | EM-415 | EM-432 | EM-452 |
EM-496 ![]() |
EM-604 | EM-703 | EM-837 | EM-849 | EM-858 | EM-885 |
None | None |
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None | None | None |
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None | None | None |
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None |
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None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-79 | EM-86 |
EM-102 ![]() |
EM-123 |
EM-333 ![]() |
EM-367 |
EM-397 ![]() |
EM-414 | EM-415 | EM-432 | EM-452 |
EM-496 ![]() |
EM-604 | EM-703 | EM-837 | EM-849 | EM-858 | EM-885 |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | -30 | 27.74 | 50.53 | 44.11 | -9999 | 30.44 | 46.72 | 46.74 | 17.96 | 17.73 | 46.74 | 44.62 | 42.62 | Not applicable | Not applicable | 35.86 | 39.83 |
Centroid Longitude
em.detail.ddLongHelp
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6.4 | 25 | -82.57 | 7.6 | -123.09 | -9999 | -87.99 | -96.13 | -92.14 | -67.02 | -64.77 | -96.13 | -124.02 | -93.84 | Not applicable | Not applicable | -105.76 | -98.58 |
Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | Not applicable | WGS84 | WGS84 |
Centroid Coordinates Status
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Provided | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Not applicable | Estimated | Estimated |
EM ID
em.detail.idHelp
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EM-79 | EM-86 |
EM-102 ![]() |
EM-123 |
EM-333 ![]() |
EM-367 |
EM-397 ![]() |
EM-414 | EM-415 | EM-432 | EM-452 |
EM-496 ![]() |
EM-604 | EM-703 | EM-837 | EM-849 | EM-858 | EM-885 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Inland Wetlands | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Inland Wetlands | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Rivers and Streams | Inland Wetlands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Agroecosystems | Grasslands | Created Greenspace | Grasslands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows | Not reported | Habitat Zones (Low, Med, High, Optimal) around seagrass and emergent marsh | Not applicable | Agricultural-urban interface at river junction | user specified | Submerged aquatic vegetation in estuaries and coastal lagoons | Freshwater estuarine system | freshwater estuary | Tropical terrestrial | Coral reefs | River and riverine estuary (lake) | Yaquina Bay estuary and ocean | Wetlands buffered by grassland within agroecosystems | restored landfills and conserved grasslands | Coastal environments | watersheds | All land of the continental US |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Zone within an ecosystem | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 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 |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-79 | EM-86 |
EM-102 ![]() |
EM-123 |
EM-333 ![]() |
EM-367 |
EM-397 ![]() |
EM-414 | EM-415 | EM-432 | EM-452 |
EM-496 ![]() |
EM-604 | EM-703 | EM-837 | EM-849 | EM-858 | EM-885 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Not applicable | Species | Not applicable | Not applicable | Not applicable | Species | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Other (multiple scales) | Individual or population, within a species | Individual or population, within a species | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-79 | EM-86 |
EM-102 ![]() |
EM-123 |
EM-333 ![]() |
EM-367 |
EM-397 ![]() |
EM-414 | EM-415 | EM-432 | EM-452 |
EM-496 ![]() |
EM-604 | EM-703 | EM-837 | EM-849 | EM-858 | EM-885 |
None Available | None Available |
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None Available |
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None Available |
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None Available |
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None Available | None Available | None Available |
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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-79 | EM-86 |
EM-102 ![]() |
EM-123 |
EM-333 ![]() |
EM-367 |
EM-397 ![]() |
EM-414 | EM-415 | EM-432 | EM-452 |
EM-496 ![]() |
EM-604 | EM-703 | EM-837 | EM-849 | EM-858 | EM-885 |
None |
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None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-79 | EM-86 |
EM-102 ![]() |
EM-123 |
EM-333 ![]() |
EM-367 |
EM-397 ![]() |
EM-414 | EM-415 | EM-432 | EM-452 |
EM-496 ![]() |
EM-604 | EM-703 | EM-837 | EM-849 | EM-858 | EM-885 |
None | None |
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None |
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None |
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None | None |
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None | None | None | None |