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-63 | EM-66 | EM-79 | EM-81 | EM-93 | EM-94 |
EM-98 ![]() |
EM-124 | EM-184 | EM-320 |
EM-369 ![]() |
EM-444 | EM-449 | EM-463 | EM-464 | EM-590 | EM-629 | EM-683 | EM-706 |
EM-728 ![]() |
EM-734 ![]() |
EM-880 ![]() |
EM-939 | EM-941 | EM-944 |
EM Short Name
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EnviroAtlas - Natural biological nitrogen fixation | Litter biomass production, Central French Alps | Divergence in flowering date, Central French Alps | Cultural ES and plant traits, Central French Alps | Stream nitrogen removal, Mississippi R. basin, USA | Reduction in pesticide runoff risk, Europe | PATCH, western USA | Land-use change and habitat diversity, Europe | ROS (Recreation Opportunity Spectrum), Europe | Coastal protection, Europe | Envision, Puget Sound, WA, USA | State of the reef index, St. Croix, USVI | Decrease in erosion (shoreline), St. Croix, USVI | Curios/jewelry production, St. Croix, USVI | Mangrove connectivity, St. Croix, USVI | Fish species richness, Puerto Rico, USA | SolVES, Pike & San Isabel NF, WY | Estuary visitation, Cape Cod, MA | WESP Method | Seed mix and mowing in prairie reconstruction, USA | WESP: Marsh and open water, ID, USA | Human well-being index, Pensacola Bay, Florida | ESTIMAP- Recreation, Europe | ESTIMAP - Pollination potential, Iran | COBRA v 4.1 |
EM Full Name
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US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Litter biomass production, Central French Alps | Functional divergence in flowering date, Central French Alps | Cultural ecosystem service estimated from plant functional traits, Central French Alps | Stream nitrogen removal, Upper Mississippi, Ohio and Missouri River sub-basins, USA | Reduction in pesticide runoff risk, Europe | PATCH (Program to Assist in Tracking Critical Habitat), western USA | Land-use change effects on habitat diversity, Europe | ROS (Recreation Opportunity Spectrum), Europe | Coastal protection, Europe | Envision, Puget Sound, WA, USA | State of the reef index, St. Croix, USVI | Decrease in erosion (shoreline) by reef, St. Croix, USVI | Relative curios/jewelry production (on reef), St. Croix, USVI | Mangrove connectivity (of reef), St. Croix, USVI | Fish species richness, Puerto Rico, USA | SolVES, Social Values for Ecosystem Services, Pike and San Isabel National Forest, CO | Value of recreational use of an estuary, Cape Cod, Massachusetts | Method for the Wetland Ecosystem Services Protocol (WESP) | Seed mix design and first year management in prairie reconstruction, IA, USA | WESP: Deepwater marsh and open Water waterfowl habitat, Idaho, USA | Human well-being index (HWBI), Pensacola Bay, Florida | ESTIMAP- Recreation, Europe | ESTIMAP - Pollination potential, Iran | COBRA (CO–Benefits Risk Assessment) v 4.1 |
EM Source or Collection
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US EPA | EnviroAtlas | EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | US EPA | None | US EPA | EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | Envision | US EPA | US EPA | US EPA | US EPA | None | None | US EPA | None | None | None | US EPA | None | None | US EPA |
EM Source Document ID
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262 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
260 | 260 | 260 | 52 | 255 | 2 | 228 | 293 | 296 |
313 ?Comment:Doc 314 is a secondary source. It is a webpage guide intended to provide support for developing an application using ENVISION. |
335 | 335 | 335 | 335 | 355 | 369 | 387 | 390 | 395 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
418 | 432 | 434 |
437 ?Comment:User's manual is provided at the webpage. |
Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Hill, B. and Bolgrien, D. | Lautenbach, S., Maes, J., Kattwinkel, M., Seppelt, R., Strauch, M., Scholz, M., Schulz-Zunkel, C., Volk, M., Weinert, J. and Dormann, C. | Carroll, C, Phillips, M. K. , Lopez-Gonzales, C. A and Schumaker, N. H. | Haines-Young, R., Potschin, M. and Kienast, F. | Paracchini, M.L., Zulian, G., Kopperoinen, L., Maes, J., Schägner, J.P., Termansen, M., Zandersen, M., Perez-Soba, M., Scholefield, P.A., and Bidoglio, G. | Liquete, C., Zulian, G., Delgado, I., Stips, A., and Maes, J. | Bolte, J. and Vache, K. | 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 | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | Mulvaney, K K., Atkinson, S.F., Merrill, N.H., Twichell, J.H., and M.J. Mazzotta | Adamus, P. R. | Meissen, J. C., A. J. Glidden, M. E. Sherrard, K. J. Elgersma, and L. L. Jackson | Murphy, C. and T. Weekley | Yee, S.H., Paulukonis, E., Simmons, C., Russell, M., Fullford, R., Harwell, L., and L.M. Smith | Zulian, G., Parrachini, M.L., Maes, J., | Rahimi, E., Barghjelveh, S., and P. Dong | US EPA |
Document Year
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2013 | 2011 | 2011 | 2011 | 2011 | 2012 | 2006 | 2012 | 2014 | 2013 | 2010 | 2014 | 2014 | 2014 | 2014 | 2007 | 2014 | 2019 | 2016 | 2019 | 2012 | 2021 | 2013 | 2020 | 2021 |
Document Title
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EnviroAtlas - National | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Nitrogen removal by streams and rivers of the Upper Mississippi River basin | Mapping water quality-related ecosystem services: concepts and applications for nitrogen retention and pesticide risk reduction | Defining recovery goals and strategies for endangered species: The wolf as a case study | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Mapping cultural ecosystem services: A framework to assess the potential for outdoor recreation across the EU | Assessment of coastal protection as an ecosystem service in Europe | Envisioning Puget Sound Alternative Futures: PSNERP Final Report | 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 | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | Quantifying Recreational Use of an Estuary: A case study of three bays, Cape Cod, USA | Manual for the Wetland Ecosystem Services Protocol (WESP) v. 1.3. | Seed mix design and first year management influence multifunctionality and cost-effectiveness in prairie reconstruction | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Projecting effects of land use change on human well being through changes in ecosystem services | ESTIMAP: Ecosystem services mapping at the European scale | Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran) | CO-Benefits Risk Assessment Health Impacts Screening and Mapping Tool (COBRA) |
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 | Documentation is peer-reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed but unpublished (explain in Comment) | Peer reviewed and published | Peer reviewed and published | 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 on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | 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 | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Draft manuscript-work progressing | Published report | Published journal manuscript | Published report | Published journal manuscript | Published report | Published journal manuscript | Webpage |
EM ID
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EM-63 | EM-66 | EM-79 | EM-81 | EM-93 | EM-94 |
EM-98 ![]() |
EM-124 | EM-184 | EM-320 |
EM-369 ![]() |
EM-444 | EM-449 | EM-463 | EM-464 | EM-590 | EM-629 | EM-683 | EM-706 |
EM-728 ![]() |
EM-734 ![]() |
EM-880 ![]() |
EM-939 | EM-941 | EM-944 |
https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://envision.bioe.orst.edu | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
http://people.oregonstate.edu/~adamusp/WESP/ ?Comment:This is an Excel spreadsheet calculator |
Not applicable | Not applicable | Not applicable | N.A. | Not applicable | https://www.epa.gov/cobra | |
Contact Name
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EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Sandra Lavorel | Sandra Lavorel | Sandra Lavorel | Brian Hill | Sven Lautenbach | Carlos Carroll | Marion Potschin | Maria Luisa Paracchini | Camino Liquete |
John Bolte ?Comment:Phone# 541-737-2041 |
Susan H. Yee | Susan H. Yee | Susan H. Yee | Susan H. Yee | Simon Pittman | Benson Sherrouse | Mulvaney, Kate | Paul R. Adamus | Justin Meissen | Chris Murphy | Susan Yee | Grazia Zulian | Ehsan Rahini | Emma Zinsmeister |
Contact Address
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Not reported | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Mid-Continent Ecology Division NHEERL, ORD. USEPA 6201 Congdon Blvd. Duluth, MN 55804, USA | Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany | Klamath Center for Conservation Research, Orleans, CA 95556 | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Joint Research Centre, Institute for Environment and Sustainability, Via E.Fermi, 2749, I-21027 Ispra (VA), Italy | European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, I-21027 Ispra, VA, Italy | Oregon State University, Dept. of Biological & Ecological Engineering, 116C Gilmore Hall, Corvallis, OR 97333 | 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 | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | 1305 East-West Highway, Silver Spring, MD 20910, USA | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | US EPA, ORD, NHEERL, Atlantic Ecology Division, Narragansett, RI | 6028 NW Burgundy Dr. Corvallis, OR 97330 | Tallgrass Prairie Center, 2412 West 27th Street, Cedar Falls, IA 50614-0294, USA | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Gulf Ecosystem Measurement and Modeling Division, Center for Environmental Measurement and Modeling, US Environmental Prntection Agency, Gulf Breeze, FL 32561, USA | Joint Research Centre, Via Enrico Fermi 2749, TP 272, 21027 Ispra (VA), Italy | Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran | EPA’s Office of Atmospheric Programs’ Climate Protection Partnerships Division |
Contact Email
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enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | hill.brian@epa.gov | sven.lautenbach@ufz.de | carlos@cklamathconservation.org | marion.potschin@nottingham.ac.uk | luisa.paracchini@jrc.ec.europa.eu | camino.liquete@gmail.com | boltej@engr.orst.edu | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | simon.pittman@noaa.gov | bcsherrouse@usgs.gov | None reported | adamus7@comcast.net | justin.meissen@uni.edu | chris.murphy@idfg.idaho.gov | yee.susan@epa.gov | grazia.zulian@jrc.ec.europa.e | ehsanrahimi666@gmail.com | zinsmeister.emma@epa.gov |
EM ID
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EM-63 | EM-66 | EM-79 | EM-81 | EM-93 | EM-94 |
EM-98 ![]() |
EM-124 | EM-184 | EM-320 |
EM-369 ![]() |
EM-444 | EM-449 | EM-463 | EM-464 | EM-590 | EM-629 | EM-683 | EM-706 |
EM-728 ![]() |
EM-734 ![]() |
EM-880 ![]() |
EM-939 | EM-941 | EM-944 |
Summary Description
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DATA FACT SHEET: "This EnviroAtlas national map displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., litter biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in litter biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy…Litter biomass production for each pixel was calculated and mapped using model estimates...This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on litter mass. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use." | ABSTRACT: "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." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The Cultural ecosystem service map was a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to cultural ecosystem services were based on stakeholders’ perceptions, given positive or negative contributions." | ABSTRACT: "We used stream chemistry and hydrogeomorphology data from 549 stream and 447 river sites to estimate NO3–N removal in the Upper Mississippi, Missouri, and Ohio Rivers. We used two N removal models to predict NO3–N input and removal. NO3–N input ranged from 0.01 to 338 kg/km*d in the Upper Mississippi River to 0.01–54 kg/ km*d in the Missouri River. Cumulative river network NO3–N input was 98700–101676 Mg/year in the Ohio River, 85,961–89,288 Mg/year in the Upper Mississippi River, and 59,463–61,541 Mg/year in the Missouri River. NO3–N output was highest in the Upper Mississippi River (0.01–329 kg/km*d ), followed by the Ohio and Missouri Rivers (0.01–236 kg/km*d ) sub-basins. Cumulative river network NO3–N output was 97,499 Mg/year for the Ohio River, 84,361 Mg/year for the Upper Mississippi River, and 59,200 Mg/year for the Missouri River. Proportional NO3–N removal (PNR) based on the two models ranged from 0.01 to 0.28. NO3–N removal was inversely correlated with stream order, and ranged from 0.01 to 8.57 kg/km*d in the Upper Mississippi River to 0.001–1.43 kg/km*d in the Missouri River. Cumulative river network NO3–N removal predicted by the two models was: Upper Mississippi River 4152 and 4152 Mg/year, Ohio River 3743 and 378 Mg/year, and Missouri River 2,277 and 197 Mg/year. PNR removal was negatively correlated with both stream order (r = −0.80–0.87) and the percent of the catchment in agriculture (r = −0.38–0.76)." | AUTHOR'S DESCRIPTION: "We used a spatially explicit model to predict the potential exposure of small streams to insecticides (run-off potential – RP) as well as the resulting ecological risk (ER) for freshwater fauna on the European scale (Schriever and Liess 2007; Kattwinkel et al. 2011)...The recovery of community structure after exposure to insecticides is facilitated by the presence of undisturbed upstream stretches that can act as sources for recolonization (Niemi et al. 1990; Hatakeyama and Yokoyama 1997). In the absence of such sources for recolonization, the structure of the aquatic community at sites that are exposed to insecticides differs significantly from that of reference sites (Liess and von der Ohe 2005)...Hence, we calculated the ER depending on RP for insecticides and the amount of recolonization zones. ER gives the percentage of stream sites in each grid cell (10 × 10 km) in which the composition of the aquatic community deviated from that of good ecological status according to the WFD. In a second step, we estimated the service provided by the environment comparing the ER of a landscape lacking completely recolonization sources with that of the actual landscape configuration. Hence, the ES provided by non-arable areas (forests, pastures, natural grasslands, moors and heathlands) was calculated as the reduction of ER for sensitive species. The service can be thought of as a habitat provisioning/nursery service that leads to an improvement of ecological water quality." | **Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** AUTHORS' DESCRIPTION: "PATCH (program to assist in tracking critical habitat), the SEPM used here, is designed for studying territorial vertebrates. It links the survival and fecundity of individual animals to geographic information system (GIS) data on mortality risk and habitat productivity at the scale of an individual or pack territory. Territories are allocated by intersecting the GIS data with an array of hexagonal cells. The different habitat types in the GIS maps are assigned weights based on the relative levels of fecundity and survival expected in those habitat classes. Base survival and reproductive rates, derived from published field studies, are then supplied to the model as a population projection matrix. The model scales these base matrix values using the mean of the habitat weights within each hexagon, with lower means translating into lower survival rates or reproductive output. Each individual in the population is tracked through a yearly cycle of survival, fecundity, and dispersal events. Environmental stochasticity is incorporated by drawing each year’s base population matrix from a randomized set of matrices whose elements were drawn from a beta (survival) or normal (fecundity) distribution. Adult organisms are classified as either territorial or floaters. The movement of territorial individuals is governed by a parameter for site fidelity, but floaters must always search for available breeding sites. As pack size increases, pack members in the model have a greater tendency to disperse and search for new available breeding sites. Movement decisions use a directed random walk that combines varying proportions of randomness, correlation, and attraction to higher-quality habitat (Schumaker 1998)." | 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 (Habitat diversity); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000." AUTHOR'S DESCRIPTION: "The analysis for the regulating service “Habitat diversity” seeks to identify all the areas with potential to support biodiversity…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." | ABSTRACT: "Research on ecosystem services mapping and valuing has increased significantly in recent years. However, compared to provisioning and regulating services, cultural ecosystem services have not yet beenfully integrated into operational frameworks. One reason for this is that transdisciplinarity is required toaddress the issue, since by definition cultural services (encompassing physical, intellectual, spiritual inter-actions with biota) need to be analysed from multiple perspectives (i.e. ecological, social, behavioural).A second reason is the lack of data for large-scale assessments, as detailed surveys are a main sourceof information. Among cultural ecosystem services, assessment of outdoor recreation can be based ona large pool of literature developed mostly in social and medical science, and landscape and ecologystudies. This paper presents a methodology to include recreation in the conceptual framework for EUwide ecosystem assessments (Maes et al., 2013), which couples existing approaches for recreation man-agement at country level with behavioural data derived from surveys and population distribution data.The proposed framework is based on three components: the ecosystem function (recreation potential),the adaptation of the Recreation Opportunity Spectrum framework to characterise the ecosystem serviceand the distribution of potential demand in the EU." | ABSTRACT: "Mapping and assessment of ecosystem services is essential to provide scientific support to global and EU biodiversity policy. Coastal protection has been mostly analysed in the frame of coastal vulnerability studies or in local, habitat-specific assessments. This paper provides a conceptual and methodological approach to assess coastal protection as an ecosystem service at different spatial–temporal scales, and applies it to the entire EU coastal zone. The assessment of coastal protection incorporates 14 biophysical and socio-economic variables from both terrestrial and marine datasets. Those variables define three indicators: coastal protection capacity, coastal exposure and human demand for protection. A questionnaire filled by coastal researchers helped assign ranks to categorical parameters and weights to the individual variables. The three indicators are then framed into the ecosystem services cascade model to estimate how coastal ecosystems provide protection, in particular describing the service function, flow and benefit. The results are comparative and aim to support integrated land and marine spatial planning. The main drivers of change for the provision of coastal protection come from the widespread anthropogenic pressures in the European coastal zone, for which a short quantitative analysis is provided." | SUMMARY: "...the Puget Sound Nearshore Ecosystem Restoration Project, completed an analysis of alternative future regional trajectories of landscape change for the Puget Sound region. This effort developed three scenarios of change: 1) Status Quo, reflecting a continuation of current trends in the region, 2) Managed Growth, reflecting the adoption of an aggressive set of land use management policies focusing on protecting and restoring ecosystem function and concentrating growth within Urban Growth Areas (UGA) and near regional growth centers, and 3) Unmanaged Growth, reflecting a relaxation of land use restrictions with limited protection of ecosystem functions. Analyses assumed a fixed population growth rate across all three scenarios, defined by the Washington Office of Financial Management county level growth estimates. Scenarios were generated using a spatially- and temporally-explicit alternative futures analysis model, Envision, previously developed by Oregon State University researchers. The model accepts as input a vector-based representation of the landscape and associated datasets describing relevant landscape characteristics, descriptors of various processes influencing landscape change, and a set of policies, or decision alternatives, which reflect scenario-specific land management alternatives. The model generates 1) a set of spatial coverages (maps) reflecting scenario outcomes of a variety of landscape variables, most notably land use/land cover, shoreline modifications, and population projections, and 2) a set of summary statistics describing landscape change variables summarized across spatial reporting units. Analyses were run on each of such sub-basins in the Puget Sound, and aggregated to providing Sound-wide results. This information is being used by PSNERP to project future impairment of ecosystem functions, goods, and services. The Puget Sound Nearshore Ecosystem project data also provide inputs to calculate aspects of future nearshore process degradation. Impairment and degradation are primary factors being used to define future conditions for the PSNERP General Investigation Study." AUTHOR'S DESCRIPTION: "In this report, we document the application of an alternative futures analysis framework that incorporates these capabilities to the analysis of alternative future trajectories in the Puget Sound region. This framework, Envision (Bolte et al, 2007; Hulse et al. 2008) is a spatially and temporally explicit, standards-based, open source toolset specifically designed to facilitate alternative futures analyses. It employs a multiagent-based modeling approach that contains a robust capability for defining alternative management strategies and scenarios, incorporating a variety of landscape change processes, and creating maps of alternative landscape trajectories, expressed though a variety of metrics defined in an application-specific way." ABOUT ENVISION (ENVISION WEBSITE): "Central to Envision, and conceived at the s | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of indicators have been proposed for measuring reef integrity, defined as the capacity to maintain healthy function and retention of diversity (Turner et al., 2000)...for reef ecological integrity (van Beukering and Cesar, 2004) defines the state of the reef as State of the Reef =ΣiwiRi where the Ri are the relative quantity of coral cover, macro-algal cover, fish richness, coral richness, and fish abundance, standardized to reflect the range of conditions at the location being evaluated (in this case, St. Croix). The wi give the weighted contribution of each attribute to reef condition based on expert judgment, originally developed for Hawaii, which were wcoral_cover=0.30, walgae_cover= 0.15, wfish_richness=0.15, wcoral_richness=0.20, and wfish_abundance=0.20 (van Beukering and Cesar, 2004). Ideally, these values would be developed to reflect local knowledge and concerns for the Caribbean or St. Croix. For a number of coral reef condition attributes, including fish richness, coral richness, and reef structural complexity, available data were point surveys from field monitoring by the US Environmental Protection Agency (see Oliver et al. (2011)) or the NOAA Caribbean Coral Reef Ecosystem Monitoring Program (see Pittman et al. (2008)). To generate continuous maps of coral condition for St. Croix, we fitted regression tree models to point survey data for St. Croix and then used models t | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef." | 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:...(6) production of curios and jewelry associated with each habitat." | 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…An alternative method to estimate potential fisheries production is to quantify not just the percent coverages of key habitats (F1)–(F6), but the degree of connectivity among those habitats. Many species that utilize coral reef habitat as adults are dependent on mangrove or seagrass nursery habitats as juveniles (Nagelkerken et al., 2000; Dorenbosch et al., 2006). In the Caribbean, the community structure or adult biomass of more than 150 reef fish species was affected by the presence of mangroves in the vicinity of reefs (Mumby et al., 2004). The value of habitat for fish production will therefore depend on the degree of connectivity between reefs and nearby mangroves (Mumby, 2006) and can be estimated as Cij = D - √(mix-rix)2+(mjy-rjy)2 where Cij is the connectivity between each reef cell i and nearby mangrove cell j, and D is the maximum migratory distance between mangroves and reefs (assumed to be 10 km), weighted by the distance between cells (x,y coordinates) such that shorter distances result in greater connectivity. The row sums then give the total connectivity of each reef cell to mangroves." | 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: " "Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders.With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems. | [ABSTRACT: "Estimates of the types and number of recreational users visiting an estuary are critical data for quantifying the value of recreation and how that value might change with variations in water quality or other management decisions. However, estimates of recreational use are minimal and conventional intercept surveys methods are often infeasible for widespread application to estuaries. Therefore, a practical observational sampling approach was developed to quantify the recreational use of an estuary without the use of surveys. Designed to be simple and fast to allow for replication, the methods involved the use of periodic instantaneous car counts multiplied by extrapolation factors derived from all-day counts. This simple sampling approach can be used to estimate visitation to diverse types of access points on an estuary in a single day as well as across multiple days. Evaluation of this method showed that when periodic counts were taken within a preferred time window (from 11am-4:30pm), the estimates were within 44 percent of actual daily visitation. These methods were applied to the Three Bays estuary system on Cape Cod, USA. The estimated combined use across all its public access sites is similar to the use at a mid-sized coastal beach, demonstrating the value of estuarine systems. Further, this study is the first to quantify the variety and magnitude of recreational uses at several different types of access points throughout the estuary using observational methods. This work can be transferred to the many small coastal access points used for recreation across New England and beyond." ] | Author Description: " The Wetland Ecosystem Services Protocol (WESP) is a standardized template for creating regionalized methods which then can be used to rapid assess ecosystem services (functions and values) of all wetland types throughout a focal region. To date, regionalized versions of WESP have been developed (or are ongoing) for government agencies or NGOs in Oregon, Alaska, Alberta, New Brunswick, and Nova Scotia. WESP also may be used directly in its current condition to assess these services at the scale of an individual wetland, but without providing a regional context for interpreting that information. Nonetheless, WESP takes into account many landscape factors, especially as they relate to the potential or actual benefits of a wetland’s functions. A WESP assessment requires completing a single three-part data form, taking about 1-3 hours. Responses to questions on that form are based on review of aerial imagery and observations during a single site visit; GIS is not required. After data are entered in an Excel spreadsheet, the spreadsheet uses science-based logic models to automatically generate scores intended to reflect a wetland’s ability to support the following functions: Water Storage and Delay, Stream Flow Support, Water Cooling, Sediment Retention and Stabilization, Phosphorus Retention, Nitrate Removal and Retention, Carbon Sequestration, Organic Nutrient Export, Aquatic Invertebrate Habitat, Anadromous Fish Habitat, Non-anadromous Fish Habitat, Amphibian & Reptile Habitat, Waterbird Feeding Habitat, Waterbird Nesting Habitat, Songbird, Raptor and Mammal Habitat, Pollinator Habitat, and Native Plant Habitat. For all but two of these functions, scores are given for both components of an ecosystem service: function and benefit. In addition, wetland Ecological Condition (Integrity), Public Use and Recognition, Wetland Sensitivity, and Stressors are scored. Scores generated by WESP may be used to (a) estimate a wetland’s relative ecological condition, stress, and sensitivity, (b) compare relative levels of ecosystem services among different wetland types, or (c) compare those in a single wetland before and after restoration, enhancement, or loss."] | ABSTRACT: "Agricultural intensification continues to diminish many ecosystem services in the North American Corn Belt. Conservation programs may be able to combat these losses more efficiently by developing initiatives that attempt to balance multiple ecological benefits. In this study, we examine how seed mix design and first year management influence three ecosystem services commonly provided by tallgrass prairie reconstructions (erosion control, weed resistance, and pollinator resources). We established research plots with three seed mixes, with and without first year mowing. The grass-dominated “Economy” mix had 21 species and a 3:1 grass-to-forb seeding ratio. The forb-dominated “Pollinator”mix had 38 species and a 1:3 grass-to-forb seeding ratio. The grass:forb balanced “Diversity” mix, which was designed to resemble regional prairie remnants, had 71 species and a 1:1 grass-to-forb ratio. To assess ecosystem services, we measured native stem density, cover, inflorescence production, and floral richness from 2015 to 2018. The Economy mix had high native cover and stem density, but produced few inflorescences and had low floral richness. The Pollinator mix had high inflorescence production and floral richness, but also had high bare ground and weed cover. The Diversity mix had high inflorescence production and floral richness (comparable to the Pollinator mix) and high native cover and stem density (comparable to the Economy mix). First year mowing accelerated native plant establishment and inflorescence production, enhancing the provisioning of ecosystem services during the early stages of a reconstruction. Our results indicate that prairie reconstructions with thoughtfully designed seed mixes can effectively address multiple conservation challenges." | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | ABSTRACT: "Changing patterns of land use, temperature, and precipitation are expected to impact ecosystem se1vices, including water quality and quantity, buffering of extreme events, soil quality, and biodiversity. Scenario ana lyses that link such impacts on ecosystem se1vices to human well-being may be valuable in anticipating potential consequences of change that are meaningful to people living in a community. Ecosystem se1vices provide munerous benefits to community well-being, including living standards, health, cultural fulfillment, education, and connection to nature. Yet assessments of impacts of ecosystem se1vices on human well-being have largely focused on human health or moneta1y benefits (e.g. market values). This study applies a human well-being modeling framework to demonsffate the potential impacts of alternative land use scenarios on multi-faceted components of human well-being through changes in ecosystem se1vices (i.e., ecological benefits functions). The modeling framework quantitatively defines these relationships in a way that can be used to project the influence of ecosystem se1vice flows on indicators of human well-being, alongside social se1vice flows and economic se1vice flows. Land use changes are linked to changing indicators of ecosystem se1vices through the application of ecological production functions. The approach is demonstrated for two future land use scenarios in a Florida watershed, representing different degrees of population growth and environmental resource protection. Increasing rates of land development were almost universally associated with declines in ecosystem se1vices indicators and associated indicators of well-being, as natural ecosystems were replaced by impe1vious surfaces that depleted the ability of ecosystems to buffer air pollutants, provide habitat for biodiversity, and retain rainwater. Scenarios with increases in indicators of ecosystem se1vices, however, did not necessarily translate into increases in indicators of well-being, due to cova1ying changes in social and economic se1vices indicators. The approach is broadly ffansferable to other communities or decision scenarios and se1ves to illustrate the potential impacts of changing land use on ecosystem se1vices and human well-being. " | AUTHOR Descriptions: "ESTIMAP consists of a set of separate components, each of which can be run separately. The models have been all framed in the ecosystem services cascade model [4] which connects ecosystem structure and functioning to human well-being through the flow of ecosystem services. At present, three modules are operational and described in further detail in this report: pollination, recreation and coastal protectionPeople can benefit from the opportunities provided by nature for recreational activities if they are able to reach them. The Recreation Opportunity spectrum was chosen as a method to map different degrees of service available according to their proximity to the people. Remoteness and proximity have been addressed in the second step of the analysis, in order to assess how the benefit (recreation) can be delivered to people. The proxy that has been identified couples information on both variables and has been mapped by classifying the EU into zones of proximity versus remoteness. From the ROS perspective this part takes into account remoteness and to some extent expected social experience. Distance from roads and residential areas have been used as inputs. The information on the road network is provided by the TeleAtlas database, and covers all paved roads in Europe. Gravel roads have been discarded to ease the processing. Residential areas are extracted from CORINE land cover classes “continuous urban fabric” and “discontinuous urban fabric”, therefore, all urban patches larger than 25 ha are considered in the mapping. In the current exercise there was the necessity to adapt overseas experiences to the peculiarities of the European continent, especially considering that the EU does not contain large wilderness areas like other continents " | 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." | Introduction: "COBRA is a screening tool that provides preliminary estimates of the impact of air pollution emission changes on ambient particulate matter (PM) air pollution concentrations, translates this into health effect impacts, and then monetizes these impacts, as illustrated below. The model does not require expertise in air quality modeling, health effects assessment, or economic valuation. Built into COBRA are emissions inventories, a simplified air quality model, health impact equations, and economic valuations ready for use, based on assumptions that EPA currently uses as reasonable best estimates. COBRA also enables advanced users to import their own datasets of emissions inventories, population, incidence, health impact functions, and valuation functions. Analyses can be performed at the state or county level and across the 14 major emissions categories (these categories are called “tiers”) included in the National Emissions Inventory. COBRA presents results in tabular as well as geographic form, and enables policy analysts to obtain a first-order approximation of the benefits of different mitigation scenarios under consideration. However, COBRA is only a screening tool. More sophisticated, albeit time- and resource-intensive, modeling approaches are currently available to obtain a more refined picture of the health and economic impacts of changes in emissions. EPA initially developed COBRA as a desktop application. In 2021, EPA released a web-based version of the tool, known as the COBRA Web Edition. Although the desktop version and web versions of COBRA both use the same methodology to calculate outdoor air quality and health impacts from changes in air pollution emissions, the desktop version offers additional advanced features that are not included in the more streamlined Web Edition. In particular, the desktop version is preloaded with input data on emissions, population, and baseline health incidence for 2016, 2023, and 2028; the Web Edition includes data only for 2023. Similarly, the desktop version allows users to import custom input datasets, while the Web Edition does not. The Web Edition, however, does not require the user to download or install additional software, and it runs more quickly than the desktop version. Users might choose to use the desktop version if they would like to use advanced features, such as custom input data and/or use the preloaded data for 2016 or 2028. Otherwise, users may choose to use the Web Edition for data analysis relevant to 2023. The process for entering emissions input data into COBRA is very similar for the desktop and web versions of the tool. The remainder of this User’s Manual focuses on the steps required to run the desktop version of the tool. The same general process can be used with the Web Edition." |
Specific Policy or Decision Context Cited
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None Identified | None identified | None identified | None identified | Not applicable | European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | AUTHOR DESCRIPTION: "Comprehensive habitat and viability assessments. . . [more rigoursly defined] can clarify debate of goals for recovery of large carnivores"; Endangered Species Act and related litigation | None identified | None identified | Supports global and EU biodiversity policy | None identified | None identified | None identified | None identified | None identified | None provided | None | None identified | None identified | None identified | None identified | None identified | None | None reported | None identified |
Biophysical Context
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No additional description provided | Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Agricultural landuse , 1st-10th order streams | Not applicable | Great Plains to Pacific Coast, northern Rocky Mountains, Pacific Northwest | No additional description provided | No additional description provided | No additional description provided | No additional description provided | 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 | Rocky mountain conifer forests | None identified | None | The site, located at the Iowa State University Northeast Research and Demonstration Farm near Nashua, Iowa, is relatively level with slopes not exceeding a 5% grade. Soil composition is primarily poorly drained Clyde clay loams with a minor component of somewhat poorly drained Floyd loams. Sub-surface tile drains exist on site and are spaced approximately 18–24m apart. The land was used for corn and soybean production prior to site establishment in 2015. | restored, enhanced and created wetlands | N/A | Continential Scale | None additional | No additional description provided |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Not applicable | No scenarios presented | Population growth, road development (density) on public vs private land | Recent historical land use change from 1990-2000 | No scenarios presented | No scenarios presented | Alternative future land management strategies (status quo, managed growth, unmanaged growth) | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | N/A | N/A | N/A | Seed mix design | Sites, function or habitat focus | N/A | N.A. | N/A | No scenarios presented |
EM ID
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EM-63 | EM-66 | EM-79 | EM-81 | EM-93 | EM-94 |
EM-98 ![]() |
EM-124 | EM-184 | EM-320 |
EM-369 ![]() |
EM-444 | EM-449 | EM-463 | EM-464 | EM-590 | EM-629 | EM-683 | EM-706 |
EM-728 ![]() |
EM-734 ![]() |
EM-880 ![]() |
EM-939 | EM-941 | EM-944 |
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 (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | 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 | Application of existing model | New or revised model | New or revised model | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | WESP Deepwater Marsh | 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-63 | EM-66 | EM-79 | EM-81 | EM-93 | EM-94 |
EM-98 ![]() |
EM-124 | EM-184 | EM-320 |
EM-369 ![]() |
EM-444 | EM-449 | EM-463 | EM-464 | EM-590 | EM-629 | EM-683 | EM-706 |
EM-728 ![]() |
EM-734 ![]() |
EM-880 ![]() |
EM-939 | EM-941 | EM-944 |
Document ID for related EM
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Doc-346 | Doc-347 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
Doc-260 | Doc-260 | Doc-269 | None | Doc-154 | Doc-155 |
Doc-254 | Doc-256 ?Comment:Document 254 was also used as a source document for this EM |
Doc-328 | Doc-337 | Doc-238 | Doc-239 | Doc-240 | Doc-241 | Doc-242 | Doc-228 | Doc-290 | Doc-291 | Doc-289 | None |
Doc-314 | Doc-47 ?Comment:Doc 314 is a secondary source. It is a webpage guide intended to provide support for developing an application using ENVISION. |
None | Doc-335 | None | None | Doc-355 | Doc-369 | None | None | Doc-394 | Doc-390 | None | None | Doc-432 | None |
EM ID for related EM
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None | EM-65 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-82 | EM-83 | None | None | EM-403 | EM-422 | EM-122 | EM-123 | EM-125 | EM-162 | EM-164 | EM-165 | EM-166 | EM-170 | EM-171 | EM-99 | EM-119 | EM-120 | EM-121 | None | None | EM-12 | EM-333 | None | EM-447 | EM-448 | None | None | EM-698 | EM-699 | EM-626 | EM-628 | EM-682 | EM-684 | EM-685 | EM-718 | EM-719 | EM-718 | EM-729 | EM-743 | EM-756 | EM-757 | EM-759 | EM-760 | EM-761 | EM-763 | EM-764 | EM-766 | EM-767 | EM-751 | EM-768 | EM-882 | EM-941 | EM-939 | None |
EM Modeling Approach
EM ID
em.detail.idHelp
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EM-63 | EM-66 | EM-79 | EM-81 | EM-93 | EM-94 |
EM-98 ![]() |
EM-124 | EM-184 | EM-320 |
EM-369 ![]() |
EM-444 | EM-449 | EM-463 | EM-464 | EM-590 | EM-629 | EM-683 | EM-706 |
EM-728 ![]() |
EM-734 ![]() |
EM-880 ![]() |
EM-939 | EM-941 | EM-944 |
EM Temporal Extent
em.detail.tempExtentHelp
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2006-2010 | Not reported | 2007-2008 | Not reported | 2000-2008 | 2000 | 2000-2025 | 1990-2000 | Not reported | 1992-2010 | 2000-2060 | 2006-2007, 2010 | 2006-2007, 2010 | 2006-2007, 2010 | 2006-2007, 2010 | 2000-2005 | 2004-2008 | Summer 2017 | Not applicable | 2015-2018 | 2010-2013 | 2010 | Not applicable | 2020 | Not applicable |
EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-dependent | time-stationary | Not applicable | time-stationary | Not applicable |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-63 | EM-66 | EM-79 | EM-81 | EM-93 | EM-94 |
EM-98 ![]() |
EM-124 | EM-184 | EM-320 |
EM-369 ![]() |
EM-444 | EM-449 | EM-463 | EM-464 | EM-590 | EM-629 | EM-683 | EM-706 |
EM-728 ![]() |
EM-734 ![]() |
EM-880 ![]() |
EM-939 | EM-941 | EM-944 |
Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Watershed/Catchment/HUC | Geopolitical | Physiographic or ecological | Geopolitical | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Geopolitical | Physiographic or ecological | Not applicable | Other | Multiple unrelated locations (e.g., meta-analysis) | Geopolitical | No location (no locational reference given) | Geopolitical | Geopolitical |
Spatial Extent Name
em.detail.extentNameHelp
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counterminous United States | Central French Alps | Central French Alps | Central French Alps | Upper Mississippi, Ohio and Missouri River sub-basins | EU-27 | Western United States | The EU-25 plus Switzerland and Norway | European Union countries | Shoreline of the European Union-27 | Puget Sound watershed | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | SW Puerto Rico, | National Park | Three Bays, Cape Cod | Not applicable | Iowa State University Northeast Research and Demonstration Farm near Nashua, Iowa | Wetlands in Idaho | Pensacola Bay Region | Not applicable | Iran | Not applicable |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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>1,000,000 km^2 | 10-100 km^2 | 10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 10,000-100,000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | Not applicable | <1 ha | 100,000-1,000,000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | Not applicable |
EM ID
em.detail.idHelp
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EM-63 | EM-66 | EM-79 | EM-81 | EM-93 | EM-94 |
EM-98 ![]() |
EM-124 | EM-184 | EM-320 |
EM-369 ![]() |
EM-444 | EM-449 | EM-463 | EM-464 | EM-590 | EM-629 | EM-683 | EM-706 |
EM-728 ![]() |
EM-734 ![]() |
EM-880 ![]() |
EM-939 | EM-941 | EM-944 |
EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) ?Comment:Watersheds (12-digit HUCs). |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Varies by inputs, but results are for areas of country |
spatially distributed (in at least some cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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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 | length, for linear feature (e.g., stream mile) | 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 | Irregular | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | 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 | map scale, for cartographic feature |
Spatial Grain Size
em.detail.spGrainSizeHelp
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irregular | 20 m x 20 m | 20 m x 20 m | 20 m x 20 m | 1 km | 10 km x 10 km | 504 km^2 | 1 km x 1 km | 100 m x 100 m | Irregular | Varies | 10 m x 10 m | 10 m x 10 m | 10 m x 10 m | 10 m x 10 m | not reported | 30m2 | beach length | not reported | 6.1 m x 8.53 m | Not applicable | county | Pixel size | ha^2 | user defined |
EM ID
em.detail.idHelp
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EM-63 | EM-66 | EM-79 | EM-81 | EM-93 | EM-94 |
EM-98 ![]() |
EM-124 | EM-184 | EM-320 |
EM-369 ![]() |
EM-444 | EM-449 | EM-463 | EM-464 | EM-590 | EM-629 | EM-683 | EM-706 |
EM-728 ![]() |
EM-734 ![]() |
EM-880 ![]() |
EM-939 | EM-941 | EM-944 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Logic- or rule-based | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Numeric | Analytic | Numeric | Numeric | Analytic |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | stochastic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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EM ID
em.detail.idHelp
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EM-63 | EM-66 | EM-79 | EM-81 | EM-93 | EM-94 |
EM-98 ![]() |
EM-124 | EM-184 | EM-320 |
EM-369 ![]() |
EM-444 | EM-449 | EM-463 | EM-464 | EM-590 | EM-629 | EM-683 | EM-706 |
EM-728 ![]() |
EM-734 ![]() |
EM-880 ![]() |
EM-939 | EM-941 | EM-944 |
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | No | No | No | No | Unclear | No | No | No | Unclear | Yes | Yes | Yes | Yes | No | No | Yes | Not applicable | Not applicable | No | Unclear | No | No | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | Yes | Yes | No | No | No | No | No | No | No | Not applicable | No | No | No | No | Yes | Yes | No | Not applicable | No | No | Not applicable | Not applicable | No | Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None |
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None | None | None | None | None | None | None | None | None | None | None | None |
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None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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No | Yes | No | No | No | Yes | No | No | No | No | Not applicable | Yes | Yes | Yes | Yes | Yes | No | No | No | No | No | No | Unclear | No | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | Yes | No | No | No | No | No | Not applicable | No | No | No | No | No | No | No | Not applicable | No | No | Yes | No | No | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | No | Unclear | No |
Yes ?Comment:No results reported. Just a general statement was made about PATCH sensitivity and that demographic parameters are more sensitive that variation in other parameters such as dispersadistance . Reference made to another publication Carroll et al. 2003. Use of population viability analysis and reserve slelection algorithms in regional conservation plans. Ecol. App. 13:1773-1789. |
No | No | No | Not applicable | No | No | No | No | Yes | No | No | Not applicable | No | No | Yes | Yes | No | Not applicable |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Unclear | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-63 | EM-66 | EM-79 | EM-81 | EM-93 | EM-94 |
EM-98 ![]() |
EM-124 | EM-184 | EM-320 |
EM-369 ![]() |
EM-444 | EM-449 | EM-463 | EM-464 | EM-590 | EM-629 | EM-683 | EM-706 |
EM-728 ![]() |
EM-734 ![]() |
EM-880 ![]() |
EM-939 | EM-941 | EM-944 |
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None | None | None | None | None |
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None | None |
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Comment:Model for Iran - no form preset id for country |
None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-63 | EM-66 | EM-79 | EM-81 | EM-93 | EM-94 |
EM-98 ![]() |
EM-124 | EM-184 | EM-320 |
EM-369 ![]() |
EM-444 | EM-449 | EM-463 | EM-464 | EM-590 | EM-629 | EM-683 | EM-706 |
EM-728 ![]() |
EM-734 ![]() |
EM-880 ![]() |
EM-939 | EM-941 | EM-944 |
None | None | None | None | None | None | None | None | None |
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None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-63 | EM-66 | EM-79 | EM-81 | EM-93 | EM-94 |
EM-98 ![]() |
EM-124 | EM-184 | EM-320 |
EM-369 ![]() |
EM-444 | EM-449 | EM-463 | EM-464 | EM-590 | EM-629 | EM-683 | EM-706 |
EM-728 ![]() |
EM-734 ![]() |
EM-880 ![]() |
EM-939 | EM-941 | EM-944 |
Centroid Latitude
em.detail.ddLatHelp
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39.5 | 45.05 | 45.05 | 45.05 | 36.98 | 50.53 | 39.88 | 50.53 | 48.2 | 48.2 | 47.58 | 17.73 | 17.73 | 17.73 | 17.73 | 17.9 | 38.7 | 41.62 | Not applicable | 42.93 | 44.06 | 30.05 | Not applicable | 32.29 | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
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-98.35 | 6.4 | 6.4 | 6.4 | -89.13 | 7.6 | -113.81 | 7.6 | 16.35 | 16.35 | -122.32 | -64.77 | -64.77 | -64.77 | -64.77 | 67.11 | 105.89 | -70.42 | Not applicable | -92.57 | -114.69 | -87.61 | Not applicable | 53.68 | Not applicable |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Provided | Provided | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Provided | Estimated | Estimated | Not applicable | Estimated | Not applicable |
EM ID
em.detail.idHelp
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EM-63 | EM-66 | EM-79 | EM-81 | EM-93 | EM-94 |
EM-98 ![]() |
EM-124 | EM-184 | EM-320 |
EM-369 ![]() |
EM-444 | EM-449 | EM-463 | EM-464 | EM-590 | EM-629 | EM-683 | EM-706 |
EM-728 ![]() |
EM-734 ![]() |
EM-880 ![]() |
EM-939 | EM-941 | EM-944 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Rivers and Streams | Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Forests | Near Coastal Marine and Estuarine | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | 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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Terrestrial | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Not applicable | Streams and near upstream environments | Not reported | Not applicable | Not applicable | Coastal zones | Pacific NW US region, coastal to montane, urban to rural | Coral reefs | Coral reefs | Coral reefs | Coral reefs and mangroves | shallow coral reefs | Montain forest | Beaches | Wetlands | prairie/grassland reconstruction at demonstration farm site | created, restored and enhanced wetlands | Mixed | Not applicable | terrestrial land types | Not applicable |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is coarser 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 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 is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-63 | EM-66 | EM-79 | EM-81 | EM-93 | EM-94 |
EM-98 ![]() |
EM-124 | EM-184 | EM-320 |
EM-369 ![]() |
EM-444 | EM-449 | EM-463 | EM-464 | EM-590 | EM-629 | EM-683 | EM-706 |
EM-728 ![]() |
EM-734 ![]() |
EM-880 ![]() |
EM-939 | EM-941 | EM-944 |
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Community | Community | Community | Not applicable | Not applicable | Species | Not applicable | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Community | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Community | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-63 | EM-66 | EM-79 | EM-81 | EM-93 | EM-94 |
EM-98 ![]() |
EM-124 | EM-184 | EM-320 |
EM-369 ![]() |
EM-444 | EM-449 | EM-463 | EM-464 | EM-590 | EM-629 | EM-683 | EM-706 |
EM-728 ![]() |
EM-734 ![]() |
EM-880 ![]() |
EM-939 | EM-941 | EM-944 |
None Available | None Available | None Available | None Available | None Available | None Available |
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None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available |
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None Available | None Available | None Available | None Available | None Available | None Available | None Available |
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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-63 | EM-66 | EM-79 | EM-81 | EM-93 | EM-94 |
EM-98 ![]() |
EM-124 | EM-184 | EM-320 |
EM-369 ![]() |
EM-444 | EM-449 | EM-463 | EM-464 | EM-590 | EM-629 | EM-683 | EM-706 |
EM-728 ![]() |
EM-734 ![]() |
EM-880 ![]() |
EM-939 | EM-941 | EM-944 |
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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-63 | EM-66 | EM-79 | EM-81 | EM-93 | EM-94 |
EM-98 ![]() |
EM-124 | EM-184 | EM-320 |
EM-369 ![]() |
EM-444 | EM-449 | EM-463 | EM-464 | EM-590 | EM-629 | EM-683 | EM-706 |
EM-728 ![]() |
EM-734 ![]() |
EM-880 ![]() |
EM-939 | EM-941 | EM-944 |
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None | None | None |
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None |
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None |
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None |
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None |
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None | None |
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None |
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None |