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-65 | EM-71 | EM-79 | EM-88 | EM-119 | EM-193 | EM-260 | EM-392 | EM-414 | EM-454 | EM-455 | EM-462 |
EM-496 ![]() |
EM-590 | EM-846 | EM-848 | EM-892 | EM-941 | EM-1007 |
EM Short Name
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Green biomass production, Central French Alps | Community flowering date, Central French Alps | Divergence in flowering date, Central French Alps | Area and hotspots of carbon storage, South Africa | Landscape importance for wildlife products, Europe | Cultural ecosystem services, Bilbao, Spain | Coral taxa and land development, St.Croix, VI, USA | EPA H2O, Tampa Bay Region, FL,USA | SAV occurrence, St. Louis River, MN/WI, USA | Reef snorkeling opportunity, St. Croix, USVI | Value of a reef dive site, St. Croix, USVI | Value of finfish, St. Croix, USVI | Sed. denitrification, St. Louis R., MN/WI, USA | Fish species richness, Puerto Rico, USA | Indigo bunting abund, Piedmont region, USA | National invertebrate community rank index | VELMA v. 2.1 contaminant modeling | ESTIMAP - Pollination potential, Iran | N-SPECT land-sea planning submodel |
EM Full Name
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Green biomass production, Central French Alps | Community weighted mean flowering date, Central French Alps | Functional divergence in flowering date, Central French Alps | Area and hotspots of carbon storage, South Africa | Landscape importance for wildlife products, Europe | Cultural ecosystem services, Bilbao, Spain | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | EPA H2O, Tampa Bay Region, FL, USA | Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA | Relative snorkeling opportunity (in reef), St. Croix, USVI | Value of a dive site (reef), St. Croix, USVI | Relative value of finfish (on reef), St. Croix, USVI | Sediment denitrification, St. Louis River, MN/WI, USA | Fish species richness, Puerto Rico, USA | Indigo bunting abundance, Piedmont ecoregion, USA | National invertebrate community ranking index (NICRI) | VELMA (Visualizing Ecosystem Land Management Assessments) v. 2.1 contaminant modeling | ESTIMAP - Pollination potential, Iran | A technical guide to the integrated land-sea planning toolkit |
EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | EU Biodiversity Action 5 |
None ?Comment:EU Mapping Studies |
US EPA | US EPA | US EPA | US EPA | US EPA | US EPA | US EPA | None | None | None | US EPA | None | None |
EM Source Document ID
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260 | 260 | 260 | 271 | 228 | 191 | 96 | 321 | 330 | 335 | 335 | 335 | 333 | 355 | 405 | 407 |
423 ?Comment:Document #430 is an additional source for this EM. Document #423 has been imcorporated into the more recently published document #430. |
434 | 473 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | 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. | Haines-Young, R., Potschin, M. and Kienast, F. | Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Ranade, P., Soter, G., Russell, M., Harvey, J., and K. Murphy | Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | 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 | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Riffel, S., Scognamillo, D., and L. W. Burger | Cuffney, Tom | McKane | Rahimi, E., Barghjelveh, S., and P. Dong | Crist, P., Madden, K., Varley, I., Eslinger, D., Walker, D., Anderson, A., Morehead, S. and Dunton, K., |
Document Year
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2011 | 2011 | 2011 | 2008 | 2012 | 2013 | 2011 | 2015 | 2013 | 2014 | 2014 | 2014 | 2014 | 2007 | 2008 | 2003 | None | 2020 | 2009 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Mapping ecosystem services for planning and management | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | EPA H20 User Manual | Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | 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 | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Invertebrate Status Index | Tutorial A.1 – Contaminant Fate and Transport Modeling Concepts; VELMA 2.1 “How To” Documentation | Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran) | Integrated Land-Sea Planning: A Technical Guide to the Integrated Land-Sea Planning Toolkit. |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Other or unclear (explain in Comment) | 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 | Published journal manuscript | Published journal manuscript | Published EPA report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published EPA report | Published journal manuscript | Published report |
EM ID
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EM-65 | EM-71 | EM-79 | EM-88 | EM-119 | EM-193 | EM-260 | EM-392 | EM-414 | EM-454 | EM-455 | EM-462 |
EM-496 ![]() |
EM-590 | EM-846 | EM-848 | EM-892 | EM-941 | EM-1007 |
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://www.epa.gov/ged/tbes/EPAH2O | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=354355 | Not applicable | https://repositories.lib.utexas.edu/bitstreams/3dee92a8-9373-4bcc-be25-eda74e81fabf/download | |
Contact Name
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Sandra Lavorel | Sandra Lavorel | Sandra Lavorel | Benis Egoh | Marion Potschin | Izaskun Casado-Arzuaga | Leah Oliver | Marc J. Russell, Ph.D. | Ted R. Angradi | Susan H. Yee | 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. |
Simon Pittman | Sam Riffell | Tom Cuffney | Robert B. McKane | Ehsan Rahini |
Patrick Crist ?Comment:No contact information provided |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | 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 | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain | National Health and Environmental Research Effects Laboratory | USEPA GED, One Sabine Island Dr., Gulf Breeze, FL 32561 | 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 | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | 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 | 1305 East-West Highway, Silver Spring, MD 20910, USA | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | 3916 Sunset Ridge Rd, Raleigh, NC 27607 | US EPA, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon 97333 | Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran | None provided |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | Not reported | marion.potschin@nottingham.ac.uk | izaskun.casado@ehu.es | leah.oliver@epa.gov | russell.marc@epa.gov | angradi.theodore@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | bellinger.brent@epa.gov | simon.pittman@noaa.gov | sriffell@cfr.msstate.edu | tcuffney@usgs.gov | mckane.bob@epa.gov | ehsanrahimi666@gmail.com | patrick@planitfwd.com |
EM ID
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EM-65 | EM-71 | EM-79 | EM-88 | EM-119 | EM-193 | EM-260 | EM-392 | EM-414 | EM-454 | EM-455 | EM-462 |
EM-496 ![]() |
EM-590 | EM-846 | EM-848 | EM-892 | EM-941 | EM-1007 |
Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)." | 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: "Community-weighted mean date of flowering onset 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." | 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…In this study, only carbon storage was mapped because of a lack of data on the other functions related to the regulation of global climate such as carbon sequestration and the effects of changes in albedo. Carbon is stored above or below the ground and South African studies have found higher levels of carbon storage in thicket than in savanna, grassland and renosterveld (Mills et al., 2005). This information was used by experts to classify vegetation types (Mucina and Rutherford, 2006), according to their carbon storage potential, into three categories: low to none (e.g. desert), medium (e.g. grassland), high (e.g. thicket, forest) (Rouget et al., 2004). All vegetation types with medium and high carbon storage potential were identified as the range of carbon storage. Areas of high carbon storage potential where it is essential to retain this store were mapped as the carbon storage hotspot." | 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 methods are explored in relation to mapping and assessing … “Wildlife Products” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain, adjacency to coastal and wetland ecosystems, as well as adjacency to landscape protection zones." AUTHOR'S DESCRIPTION: "Wildlife Products…includes the provisioning of all non-edible raw material products that are gained through non-agriculutural 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." | ABSTRACT "This paper presents a method to quantify cultural ecosystem services (ES) and their spatial distribution in the landscape based on ecological structure and social evaluation approaches. The method aims to provide quantified assessments of ES to support land use planning decisions. A GIS-based approach was used to estimate and map the provision of recreation and aesthetic services supplied by ecosystems in a peri-urban area located in the Basque Country, northern Spain. Data of two different public participation processes (frequency of visits to 25 different sites within the study area and aesthetic value of different landscape units) were used to validate the maps. Three maps were obtained as results: a map showing the provision of recreation services, an aesthetic value map and a map of the correspondences and differences between both services. The data obtained in the participation processes were found useful for the validation of the maps. A weak spatial correlation was found between aesthetic quality and recreation provision services, with an overlap of the highest values for both services only in 7.2 % of the area. A consultation with decision-makers indicated that the results were considered useful to identify areas that can be targeted for improvement of landscape and recreation management." | AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | AUTHORS DESCRIPTION: "EPA H2O is a GIS based demonstration tool for assessing ecosystem goods and services (EGS). It was developed as a preliminary assessment tool in support of research being conducted in the Tampa Bay watershed. It provides information, data, approaches and guidance that communities can use to examine alternative land use scenarios in the context of nature’s benefits to the human community. . . EPA H2O allows users for the Tampa Bay estuary and its watershed to: • Gain a greater understanding of the significance of EGS, • Explore the spatial distribution of EGS and other ecosystem features, • Obtain map and summary statistics of EGS production's potential value, • Analyze and compare potential impacts from predicted development scenarios or user specified changes in land use patterns on EGS production's potential value EPA H2O is designed for analyzing data at neighborhood to regional scales.. . The tool is transportable to other locations if the required data are available. . . . | 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: "...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: snorkeling opportunity" | 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)…Another method to quantify recreational opportunities is to use survey data of tourists and recreational visitors to the reefs to generate statistical models to quantify the link between reef condition and production of recreation-related ecosystem services. Wielgus et al. (2003) used interviews with SCUBA divers in Israel to derive coefficients for a choice model in which willingness to pay for higher quality dive sites was determined in part by a weighted combination of factors identified with dive quality: Relative value of dive site = 0.1227(Scoral+Sfish+Acoral+Afish)+0.0565V where Scoral, Sfish are coral and fish richness, Acoral, Afish are abundances of fish and coral per square meter, and V is water visibility (meters)." | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(5) value of finfish," | 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: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems." | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds." | ABSTRACT: "The Invertebrate Status Index is a multimetric index that was derived for the NAWQA Program to provide a simple national characterization of benthic invertebrate communities. This index— referred to here as the National Invertebrate Community Ranking Index (NICRI)—provides a simple method of placing community conditions within the context of all sites sampled by the NAWQA Program. The multimetric index approach is the most commonly used method of characterizing biological conditions within the U.S. (Barbour and others, 1999). Using this approach, communities may be compared by considering how individual metrics vary among sites or by combining individual metrics into a single composite (i.e., multimetric) index and examining how this single index varies among sites. Combining metrics into a single multimetric index simplifies the presentation of results (Barbour and others, 1999) and minimizes weaknesses that may be associated with individual metrics (Ohio EPA, 1987a,b). The NICRI is a multimetric index that combines 11 metrics (RICH, EPTR, CG_R, PR_R, EPTRP, CHRP, V2DOMP, EPATOLR, EPATOLA, DIVSHAN, and EVEN; Table 1) into a single, nationally consistent, composite index. The NICRI was used to rank 140 sites of the FY94 group of study units, with median values used for sites where data were available for multiple reaches and(or) multiple years. Average metric scores were then rescaled using the PERCENTRANK function and multiplied by 100 to produce a final NICRI score that ranged from 0 (low ranking relative to other NAWQA Program sites and presumably diminished community conditions) to 100 (high ranking relative to other NAWQA Program sites and presumably excellent community conditions). " | ABSTRACT: "This document describes the conceptual framework underpinning the use of VELMA 2.1 to model fate and transport of organic contaminants within watersheds. We review how VELMA 2.1 simulates contaminant fate and transport within soils and hillslopes as a function of two processes: (1) the partitioning of the total amount of a contaminant between sorbed (immobile) and aqueous (mobile) phases; and (2) the vertical and lateral transport of the contaminant’s aqueous phase within surface and subsurface waters." | Abstract: ". ..we used the ESTIMAP model to improve the results of the Lonsdorf model. For this, we included the effects of roads, railways, rivers, wetlands, lakes, altitude, climate, and ecosystem boundaries in the ESTIMAP modeling and compared the results with the Lonsdorf model. The results of the Lonsdorf model showed that the majority of Iran had a very low potential for providing pollination service and only three percent of the northern and western parts of Iran had high potential. However, the results of the ESTIMAP model showed that 16% of Iran had a high potential to provide pollination that covers most of the northern and southern parts of the country. The results of the ESTIMAP model for pollination mapping in Iran showed the Lonsdorf model of estimating pollination service can be improved through considering other relevant factors." | The Nonpoint-Source Pollution and Erosion Comparison Tool (N-SPECT) is a screening tool developed to help land use planners and mangers understand the potential impacts of land use change decisions on erosion and water quality. The tool runs as an extension within the ESRI ArcGIS software package. It utilizes digital elevation maps, soils and precipitation information from data sets that are available nationally. However, it also lets users take advantage of local higher resolution and/or more accurate data sets when available. For example, the N-SPECT pollution coefficients used are similar to those in the EPA’s BASINS suite of tools and provide a good starting point for quick comparisons between management scenarios, but the coefficients can still be easily customized as users develop more localized data. The real utility of N-SPECT does not lie in the user’s ability to examine the accuracy of any particular run’s results, but in the comparison of runs between different development (or restoration) scenarios. By allowing users to modify multiple land uses and providing the results of those changes in a GIS environment, N-SPECT enables managers to quickly understand the overall consequences of different land use scenarios. The primary role of N-SPECT in this toolkit is to predict sedimentation and pollution changes from different land use scenarios and identify areas that are key contributors of these inputs. |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified | None identified | Land management, ecosystem management, response to EU 2020 Biodiversity Strategy | Not applicable | None reported | None identified | None identified | None identified | None identified | None identified | None provided | None reported | None Identified | None identified | None reported | None provided |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | No additional description provided | Northern Spain; Bizkaia region | nearshore; <1.5 km offshore; <12 m depth | Not applicable | submerged aquatic vegetation | No additional description provided | No additional description provided | No additional description provided | No additional description provided | Hard and soft benthic habitat types approximately to the 33m isobath | Conservation Reserve Program lands left to go fallow | Streams and Rivers | No additional description provided | None additional | Not applicable |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Not applicable | Land Use, EGS algorithm values, | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | N/A | N/A | No scenarios presented | N/A | No scenarios presented |
EM ID
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EM-65 | EM-71 | EM-79 | EM-88 | EM-119 | EM-193 | EM-260 | EM-392 | EM-414 | EM-454 | EM-455 | EM-462 |
EM-496 ![]() |
EM-590 | EM-846 | EM-848 | EM-892 | EM-941 | EM-1007 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method Only | Method + Application | Method Only |
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 | 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 Modeling Approach
EM ID
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EM-65 | EM-71 | EM-79 | EM-88 | EM-119 | EM-193 | EM-260 | EM-392 | EM-414 | EM-454 | EM-455 | EM-462 |
EM-496 ![]() |
EM-590 | EM-846 | EM-848 | EM-892 | EM-941 | EM-1007 |
EM Temporal Extent
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2007-2009 | 2007-2008 | 2007-2008 | Not reported | 2000 | 2000 - 2007 | 2006-2007 | Not applicable | 2010 - 2012 | 2006-2007, 2010 | 2006-2007, 2010 | 2006-2007, 2010 |
July 2011 to September 2012 ?Comment:All sampling performed July 2011, and May-September 2012. |
2000-2005 | 2008 | 1991-1994 | Not applicable | 2020 | Not applicable |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent |
EM Time Reference (Future/Past)
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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 | Not applicable | Not applicable |
EM Time Continuity
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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 | discrete | Not applicable | other or unclear (comment) |
EM Temporal Grain Size Value
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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 | 1 | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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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 | Day | Not applicable | Not applicable |
EM ID
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EM-65 | EM-71 | EM-79 | EM-88 | EM-119 | EM-193 | EM-260 | EM-392 | EM-414 | EM-454 | EM-455 | EM-462 |
EM-496 ![]() |
EM-590 | EM-846 | EM-848 | EM-892 | EM-941 | EM-1007 |
Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Geopolitical | Geopolitical | Physiographic or Ecological |
Geopolitical ?Comment:Extent was Tampa Bay area in example, but boundary can be geopolitical or watershed derived. |
Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Other | Not applicable | Geopolitical | Not applicable |
Spatial Extent Name
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Central French Alps | Central French Alps | Central French Alps | South Africa | The EU-25 plus Switzerland and Norway | Bilbao Metropolitan Greenbelt | St.Croix, U.S. Virgin Islands | Tampa Bay region | St. Louis River Estuary | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | St. Louis River Estuary (of western Lake Superior) | SW Puerto Rico, | Piedmont Ecoregion | Not applicable | Not applicable | Iran | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 10-100 km^2 | 1000-10,000 km^2. | 10-100 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 10-100 km^2 | 100-1000 km^2 | 100,000-1,000,000 km^2 | Not applicable | Not applicable | >1,000,000 km^2 | Not applicable |
EM ID
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EM-65 | EM-71 | EM-79 | EM-88 | EM-119 | EM-193 | EM-260 | EM-392 | EM-414 | EM-454 | EM-455 | EM-462 |
EM-496 ![]() |
EM-590 | EM-846 | EM-848 | EM-892 | EM-941 | EM-1007 |
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 lumped (in all cases) | spatially distributed (in at least some cases) |
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 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) ?Comment:Varies by inputs, but results are for areas of country |
other or unclear (comment) |
Spatial Grain Type
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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 | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | volume, for 3-D feature | area, for pixel or radial feature | Not applicable |
Spatial Grain Size
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20 m x 20 m | 20 m x 20 m | 20 m x 20 m | Distributed across catchments with average size of 65,000 ha | 1 km x 1 km | 2 m x 2 m | Not applicable | 30m x 30m | 0.07 m^2 to 0.70 m^2 | 10 m x 10 m | 10 m x 10 m | 10 m x 10 m | 35 km river estuary reach, 0 to 5 m depth by 1 m increment | not reported | Not applicable | stream reach | user defined | ha^2 | Not applicable |
EM ID
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EM-65 | EM-71 | EM-79 | EM-88 | EM-119 | EM-193 | EM-260 | EM-392 | EM-414 | EM-454 | EM-455 | EM-462 |
EM-496 ![]() |
EM-590 | EM-846 | EM-848 | EM-892 | EM-941 | EM-1007 |
EM Computational Approach
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Analytic | Analytic | Analytic | Analytic | Logic- or rule-based | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | 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-65 | EM-71 | EM-79 | EM-88 | EM-119 | EM-193 | EM-260 | EM-392 | EM-414 | EM-454 | EM-455 | EM-462 |
EM-496 ![]() |
EM-590 | EM-846 | EM-848 | EM-892 | EM-941 | EM-1007 |
Model Calibration Reported?
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No | No | No | No | No | No | Yes | No | Yes | Yes | Yes | Yes | Yes | No | Yes | Not applicable | Not applicable | No | Not applicable |
Model Goodness of Fit Reported?
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Yes | Yes | Yes | No | No | No | Yes | No | Yes | No | No | No | Yes | Yes | No | Not applicable | Not applicable | No | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None | None |
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None |
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None | None | None |
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None | None | None | None | None |
Model Operational Validation Reported?
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Yes | No | No | No | Yes | Yes | No | No | Yes | Yes | Yes | Yes | No | Yes | No | No | Not applicable | No | Not applicable |
Model Uncertainty Analysis Reported?
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No | No | No | No | No | No | Yes | No | No | No | No | No | No | No | No | Yes | Not applicable | No | Not applicable |
Model Sensitivity Analysis Reported?
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No | No | No | No | No | No | No | No | No | No | No | No | No | Yes | Yes | Yes | Not applicable | No | Not applicable |
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 | No | Not applicable | Yes | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-65 | EM-71 | EM-79 | EM-88 | EM-119 | EM-193 | EM-260 | EM-392 | EM-414 | EM-454 | EM-455 | EM-462 |
EM-496 ![]() |
EM-590 | EM-846 | EM-848 | EM-892 | EM-941 | EM-1007 |
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None |
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None | None | None |
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None |
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Comment:No specific location but developed in United States |
None |
Comment:Model for Iran - no form preset id for country |
None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-65 | EM-71 | EM-79 | EM-88 | EM-119 | EM-193 | EM-260 | EM-392 | EM-414 | EM-454 | EM-455 | EM-462 |
EM-496 ![]() |
EM-590 | EM-846 | EM-848 | EM-892 | EM-941 | EM-1007 |
None | None | None | None | None | None |
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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-65 | EM-71 | EM-79 | EM-88 | EM-119 | EM-193 | EM-260 | EM-392 | EM-414 | EM-454 | EM-455 | EM-462 |
EM-496 ![]() |
EM-590 | EM-846 | EM-848 | EM-892 | EM-941 | EM-1007 |
Centroid Latitude
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45.05 | 45.05 | 45.05 | -30 | 50.53 | 43.25 | 17.75 | 28.05 | 46.72 | 17.73 | 17.73 | 17.73 | 46.74 | 17.9 | 36.23 | Not applicable | Not applicable | 32.29 | Not applicable |
Centroid Longitude
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6.4 | 6.4 | 6.4 | 25 | 7.6 | -2.92 | -64.75 | -82.52 | -96.13 | -64.77 | -64.77 | -64.77 | -96.13 | 67.11 | -81.9 | Not applicable | Not applicable | 53.68 | Not applicable |
Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | NAD83 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | Not applicable | WGS84 | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Provided | Provided | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Not applicable | Estimated | Not applicable |
EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-79 | EM-88 | EM-119 | EM-193 | EM-260 | EM-392 | EM-414 | EM-454 | EM-455 | EM-462 |
EM-496 ![]() |
EM-590 | EM-846 | EM-848 | EM-892 | EM-941 | EM-1007 |
EM Environmental Sub-Class
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Rivers and Streams | Inland Wetlands | Near Coastal Marine and Estuarine | Grasslands | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Not applicable |
Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Subalpine terraces, grasslands, and meadows | Not applicable | Not applicable | none | stony coral reef | All terestrial landcover and waterbodies | Freshwater estuarine system | Coral reefs | Coral reefs | Coral reefs | River and riverine estuary (lake) | shallow coral reefs | grasslands | benthic habitat | Terrestrial environment | terrestrial land types | None |
EM Ecological Scale
em.detail.ecoScaleHelp
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Not applicable | Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-65 | EM-71 | EM-79 | EM-88 | EM-119 | EM-193 | EM-260 | EM-392 | EM-414 | EM-454 | EM-455 | EM-462 |
EM-496 ![]() |
EM-590 | EM-846 | EM-848 | EM-892 | EM-941 | EM-1007 |
EM Organismal Scale
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Community | Community | Community | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Guild or Assemblage | Guild or Assemblage | Guild or Assemblage | Not applicable | Guild or Assemblage | Species |
Other (Comment) ?Comment:Community metrics of tolerance, food groups, sensitivity, taxa richness, diversity |
Not applicable | Not applicable | Community |
Taxonomic level and name of organisms or groups identified
EM-65 | EM-71 | EM-79 | EM-88 | EM-119 | EM-193 | EM-260 | EM-392 | EM-414 | EM-454 | EM-455 | EM-462 |
EM-496 ![]() |
EM-590 | EM-846 | EM-848 | EM-892 | EM-941 | EM-1007 |
None Available | None Available | None Available | None Available | None Available | None Available |
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None Available | None Available | None Available | None Available |
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None Available |
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None Available | None Available |
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None Available |
EnviroAtlas URL
EM-65 | EM-71 | EM-79 | EM-88 | EM-119 | EM-193 | EM-260 | EM-392 | EM-414 | EM-454 | EM-455 | EM-462 |
EM-496 ![]() |
EM-590 | EM-846 | EM-848 | EM-892 | EM-941 | EM-1007 |
GAP Ecological Systems | None Available | None Available | None Available | Percent GAP Status 1 & 2 | Percent IUCN Status II, Percent GAP Status 1 & 2 | None Available | The National Hydrography Dataset (NHD), GAP Ecological Systems, Total Annual Reduced Nitrogen Deposition, Waterbody area, Percent GAP Status 1 & 2, Acres of Land Enrolled in the Conservation Reserve Program (CRP), The Watershed Boundary Dataset (WBD), Carbon Storage by Tree Biomass | Average Annual Precipitation | None Available | None Available | None Available | Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition | None Available | GAP Ecological Systems, U.S. EPA (Omernik) ecoregions | None Available | None Available | The National Hydrography Dataset (NHD), GAP Ecological Systems, Average Annual Precipitation, Waterbody area | Average Annual Precipitation |
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-65 | EM-71 | EM-79 | EM-88 | EM-119 | EM-193 | EM-260 | EM-392 | EM-414 | EM-454 | EM-455 | EM-462 |
EM-496 ![]() |
EM-590 | EM-846 | EM-848 | EM-892 | EM-941 | EM-1007 |
None | None | 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-65 | EM-71 | EM-79 | EM-88 | EM-119 | EM-193 | EM-260 | EM-392 | EM-414 | EM-454 | EM-455 | EM-462 |
EM-496 ![]() |
EM-590 | EM-846 | EM-848 | EM-892 | EM-941 | EM-1007 |
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None | None | None |
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None | None |
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None |
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None | None |
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None |