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-70 | EM-80 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-146 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-363 ![]() |
EM-376 | EM-449 | EM-469 |
EM-496 ![]() |
EM-648 | EM-654 | EM-698 |
EM-784 ![]() |
EM-821 ![]() |
EM-846 | EM-862 | EM-896 |
EM Short Name
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Plant species diversity, Central French Alps | Agronomic ES and plant traits, Central French Alps | Pollination ES, Central French Alps | RHyME2, Upper Mississippi River basin, USA | AnnAGNPS, Kaskaskia River watershed, IL, USA | Land-use change and recreation, Europe | FORCLIM v2.9, Transect in Western OR, USA | Cultural ecosystem services, Bilbao, Spain | Coral and land development, St.Croix, VI, USA | C Sequestration and De-N, Tampa Bay, FL, USA | N removal by wetlands, Contiguous USA | SWAT, Aixola watershed, Spain | InVEST (v1.004) water purification, Indonesia | MIMES: For Massachusetts Ocean (v1.0) | Decrease in erosion (shoreline), St. Croix, USVI | Yasso07 - SOC, Loess Plateau, China | Sed. denitrification, St. Louis R., MN/WI, USA | Bobolink density, CREP, Iowa, USA | Forest recreation, Wisconsin, USA | Fish species richness, St. Croix, USVI | Wildflower mix supporting bees, Florida, USA | Aquatic vertebrate IBI for Western streams, USA | Indigo bunting abund, Piedmont region, USA | Recreational fishery index, USA | Random wave transformation on vegetation fields |
EM Full Name
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Plant species diversity, Central French Alps | Agronomic ecosystem service estimated from plant functional traits, Central French Alps | Pollination ecosystem service estimated from plant functional traits, Central French Alps | RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | Land-use change effects on recreation, Europe | FORCLIM (FORests in a changing CLIMate) v2.9, Western OR, USA | Cultural ecosystem services, Bilbao, Spain | Coral colony density and land development, St.Croix, Virgin Islands, USA | Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA | Nitrogen removal by wetlands as a function of loading, Contiguous USA | SWAT (Soil and Water Assessment Tool), Aixola watershed, Spain | InVEST (Integrated Valuation of Environmental Services and Tradeoffs v1.004) water purification (nutrient retention), Sumatra, Indonesia | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | Decrease in erosion (shoreline) by reef, St. Croix, USVI | Yasso07 - Land Use Effects on Soil Organic Carbon Stocks in the Loess Plateau, China | Sediment denitrification, St. Louis River, MN/WI, USA | Bobolink population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Forest recreation, Wisconsin, USA | Fish Species Richness, Buck Island, St. Croix , USVI | Wildflower planting mix supporting bees in agricultural landscapes, Florida, USA | Development of an aquatic vertebrate index of biotic integrity (IBI) for Western streams, USA | Indigo bunting abundance, Piedmont ecoregion, USA | Recreational fishery index for streams and rivers, USA | Random wave transformation on vegetation fields |
EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | US EPA | US EPA | EU Biodiversity Action 5 | US EPA |
None ?Comment:EU Mapping Studies |
US EPA | US EPA | US EPA | None | InVEST | US EPA | US EPA | None | US EPA | None | None | None | None | None | None | US EPA | None |
EM Source Document ID
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260 | 260 | 260 | 123 | 137 | 228 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
191 | 96 | 186 | 63 | 295 | 309 | 316 | 335 | 344 | 333 | 372 | 376 | 355 | 400 | 404 | 405 | 414 | 424 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C. | Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Haines-Young, R., Potschin, M. and Kienast, F. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Russell, M. and Greening, H. | Jordan, S., Stoffer, J. and Nestlerode, J. | Zabaleta, A., Meaurio, M., Ruiz, E., and Antigüedad, I. | Bhagabati, N. K., Ricketts, T., Sulistyawan, T. B. S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A., Tallis, H., and Wolney, S. | Altman, I., R.Boumans, J. Roman, L. Kaufman | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu | Brent J. Bellinger, Terri M. Jicha, LaRae P. Lehto, Lindsey R. Seifert-Monson, David W. Bolgrien, Matthew A. Starry, Theodore R. Angradi, Mark S. Pearson, Colleen Elonen, and Brian H. Hill | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Qiu, J. and M. G. Turner | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Williams, N.M., Ward, K.L., Pope, N., Isaacs, R., Wilson, J., May, E.A., Ellis, J., Daniels, J., Pence, A., Ullmann, K., and J. Peters | Pont, D., Hughes, R.M., Whittier, T.R., and S. Schmutz. | Riffel, S., Scognamillo, D., and L. W. Burger | Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold | Mendez, F. J. and I. J. Losada |
Document Year
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2011 | 2011 | 2011 | 2013 | 2011 | 2012 | 2007 | 2013 | 2011 | 2013 | 2011 | 2014 | 2014 | 2012 | 2014 | 2015 | 2014 | 2010 | 2013 | 2007 | 2015 | 2009 | 2008 | 2021 | 2004 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads | AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Estimating benefits in a recovering estuary: Tampa Bay, Florida | Wetlands as sinks for reactive nitrogen at continental and global scales: A meta-analysis | Simulation climate change impact on runoff and sediment yield in a small watershed in the Basque Country, Northern Spain | Ecosystem services reinforce Sumatran tiger conservation in land use plans | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement | Sediment nitrification and denitrification in a Lake Superior estuary | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Spatial interactions among ecosystem services in an urbanizing agricultural watershed | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States | A Predictive Index of Biotic Integrity Model for A predictive index of biotic integrity model foraquatic-vertebrate assemblages of Western U.S. Streams | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Correspondence between a recreational fishery index and ecological condition for U.S.A. streams and rivers. | An empirical model to estimate the propagation of random breaking and nonbreaking waves over vegetation fields |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Documented, not peer reviewed | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published 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 report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-70 | EM-80 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-146 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-363 ![]() |
EM-376 | EM-449 | EM-469 |
EM-496 ![]() |
EM-648 | EM-654 | EM-698 |
EM-784 ![]() |
EM-821 ![]() |
EM-846 | EM-862 | EM-896 |
Not applicable | Not applicable | Not applicable | Not applicable | https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://swat.tamu.edu/software/arcswat/ | https://www.naturalcapitalproject.org/invest/ | http://www.afordablefutures.com/orientation-to-what-we-do | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | |
Contact Name
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Sandra Lavorel | Sandra Lavorel | Sandra Lavorel | Liem Tran | Yongping Yuan | Marion Potschin | Richard T. Busing | Izaskun Casado-Arzuaga | Leah Oliver | M. Russell | Steve Jordan | Ane Zabaleta | Nirmal K. Bhagabati | Irit Altman | Susan H. Yee | Xing Wu |
Brent J. Bellinger ?Comment:Ph# +1 218 529 5247. Other current address: Superior Water, Light and Power Company, 2915 Hill Ave., Superior, WI 54880, USA. |
David Otis | Monica G. Turner | Simon Pittman | Neal Williams | Didier Pont | Sam Riffell | Gregg Lomnicky |
F. J. Mendez ?Comment:Tel.: +34-942-201810 |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA | U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain | National Health and Environmental Research Effects Laboratory | US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA | Gulf Ecology Division U.S. Environmental Protection Agency, 1 Sabine Island Drive, Gulf Breeze, Florida 32561 | Hydrogeology and Environment Group, Science and Technology Faculty, University of the Basque Country, 48940 Leioa, Basque Country (Spain) | The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | Boston University, Portland, Maine | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Chinese Academy of Sciences, Beijing 100085, China | U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Not reported | 1305 East-West Highway, Silver Spring, MD 20910, USA | Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616 | Centre d’E´ tude du Machinisme Agricole et du Genie Rural, des Eaux et Foreˆts (Cemagref), Unit HYAX Hydrobiologie, 3275 Route de Ce´zanne, Le Tholonet, 13612 Aix en Provence, France | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | 200 SW 35th St., Corvallis, OR, 97333 | Not reported |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | ltran1@utk.edu | yuan.yongping@epa.gov | marion.potschin@nottingham.ac.uk | rtbusing@aol.com | izaskun.casado@ehu.es | leah.oliver@epa.gov | Russell.Marc@epamail.epa.gov | steve.jordan@epa.gov | ane.zabaleta@ehu.es | nirmal.bhagabati@wwfus.org | iritaltman@bu.edu | yee.susan@epa.gov | xingwu@rceesac.cn | bellinger.brent@epa.gov | dotis@iastate.edu | turnermg@wisc.edu | simon.pittman@noaa.gov | nmwilliams@ucdavis.edu | didier.pont@cemagref.fr | sriffell@cfr.msstate.edu | lomnicky.gregg@epa.gov | mendezf@unican.es |
EM ID
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EM-70 | EM-80 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-146 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-363 ![]() |
EM-376 | EM-449 | EM-469 |
EM-496 ![]() |
EM-648 | EM-654 | EM-698 |
EM-784 ![]() |
EM-821 ![]() |
EM-846 | EM-862 | EM-896 |
Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Simpson species diversity was modelled using the LU + abiotic [land use and all abiotic variables] model given that functional diversity should be a consequence of species diversity rather than the reverse (Lepsˇ et al. 2006)…Species diversity for each pixel was calculated and mapped using model estimates for effects of land use types, and for regression coefficients on abiotic variables. For each pixel these calculations were applied to mapped estimates of 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 Agronomic ecosystem service map is 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 agronomic ecosystem services are based on stakeholders’ perceptions, given positive or negative contributions." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The pollination ecosystem service map was a simple sums of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to pollination ecosystem services are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions." | ABSTRACT: "We describe a framework called Regional Hydrologic Modeling for Environmental Evaluation (RHyME2) for hydrologic modeling across scales. Rooted from hierarchy theory, RHyME2 acknowledges the rate-based hierarchical structure of hydrological systems. Operationally, hierarchical constraints are accounted for and explicitly described in models put together into RHyME2. We illustrate RHyME2with a two-module model to quantify annual nutrient loads in stream networks and watersheds at regional and subregional levels. High values of R2 (>0.95) and the Nash–Sutcliffe model efficiency coefficient (>0.85) and a systematic connection between the two modules show that the hierarchy theory-based RHyME2 framework can be used effectively for developing and connecting hydrologic models to analyze the dynamics of hydrologic systems." Two EMs will be entered in EPF-Library: 1. Regional scale module (Upper Mississippi River Basin) - this entry 2. Subregional scale module (St. Croix River Basin) | AUTHORS' DESCRIPTION: "AnnAGNPS is an advanced simulation model developed by the USDA-ARS and Natural Resource Conservation Services (NRCS) to help evaluate watershed response to agricultural management practices. It is a continuous simulation, daily time step, pollutant loading model designed to simulate water, sediment and chemical movement from agricultural watersheds.p. 198" | 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 for the periods 1990–2000, 2000–2006, and 2000–2030 are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Recreation); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000 and 2000–2006 (LEAC, EEA, 2006) and EURURALIS 2.0 land use scenarios for 2000–2030. The results are reported at three spatial reporting units, i.e. (1) the NUTS-X regions, (2) the bioclimatic regions, and (3) the dominant landscape types." AUTHOR'S DESCRIPTION: " 'Recreation' is broadly defined as all areas where landscape properties are favourable for active recreation purposes….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…The futures component of the work was based on EURURALIS 2.0 land use scenarios for 2000–2030, which are based on the four IPCC SRES land use scenarios." | ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA." Author's Description: "The first set of tests involved eight sites on western Oregon transect from west to east… Individual sites were chosen to represent a particular type of potential natural vegetation as described by Franklin and Dyrness (1988)." | ABSTRACT "This paper presents a method to quantify cultural ecosystem services (ES) and their spatial distribution in the landscape based on ecological structure and social evaluation approaches. The method aims to provide quantified assessments of ES to support land use planning decisions. A GIS-based approach was used to estimate and map the provision of recreation and aesthetic services supplied by ecosystems in a peri-urban area located in the Basque Country, northern Spain. Data of two different public participation processes (frequency of visits to 25 different sites within the study area and aesthetic value of different landscape units) were used to validate the maps. Three maps were obtained as results: a map showing the provision of recreation services, an aesthetic value map and a map of the correspondences and differences between both services. The data obtained in the participation processes were found useful for the validation of the maps. A weak spatial correlation was found between aesthetic quality and recreation provision services, with an overlap of the highest values for both services only in 7.2 % of the area. A consultation with decision-makers indicated that the results were considered useful to identify areas that can be targeted for improvement of landscape and recreation management." | AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | AUTHOR'S DESCRIPTION: "...we examine the change in the production of ecosystem goods produced as a result of restoration efforts and potential relative cost savings for the Tampa Bay community from seagrass expansion (more than 3,100 ha) and coastal marsh and mangrove restoration (∼600 ha), since 1990… The objectives of this article are to explore the roles that ecological processes and resulting ecosystem goods have in maintaining healthy estuarine systems by (1) quantifying the production of specific ecosystem goods in a subtropical estuarine system and (2) determining potential cost savings of improved water quality and increased habitat in a recovering estuary." (pp. 2) | ABSTRACT: "We compiled published data from wetland studies worldwide to estimate total Nr removal and to evaluate factors that influence removal rates. Over several orders of magnitude in wetland area and Nr loading rates, there is a positive, near-linear relationship between Nr removal and Nr loading. The linear model (null hypothesis) explains the data better than either a model of declining Nr removal efficiency with increasing Nr loading, or a Michaelis–Menten (saturation) model." | ABSTRACT: "We explored the potential impact of climate change on runoff and sediment yield for the Aixola watershed using the Soil and Water Assessment Tool (SWAT). The model calibration (2007–2010) and validation (2005–2006) results were rated as satisfactory. Subsequently, simulations were run for four climate change model–scenario combinations based on two general circulation models (CGCM2 and ECHAM4) under two emissions scenarios (A2 and B2) from 2011 to 2100." AUTHOR'S DESCRIPTION: "The results were grouped into three consecutive 30-yr periods (2011-2040, 2041-2070, and 2071-2100) and compared with the values simulated for the baseline period (1961-1990)." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "...Here we use simple spatial analyses on readily available datasets to compare the distribution of five ecosystem services with tiger habitat in central Sumatra. We assessed services and habitat in 2008 and the changes in these variables under two future scenarios: a conservation-friendly Green Vision, and a Spatial Plan developed by the Indonesian government..." AUTHOR'S DESCRIPTION: "We used a modeling tool, InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004; Tallis et al., 2010), to map and quantify tiger habitat quality and five ecosystem services. InVEST maps ecosystem services and the quality of species habitat as production functions of LULC using simple biophysical models. Models were parameterized using data from regional agencies, literature surveys, global databases, site visits and prior field experience (Table 1)... Our nutrient retention model estimates nitrogen and phosphorus loading (kg y^-1), leading causes of water pollution from fertilizer application and other activities, using the export coefficient approach of Reckhow et al. (1980). The model routes nutrient runoff from each land parcel downslope along the flow path, with some of the nutrient that originated upstream being retained by the parcel according to its retention efficiency. For assessing variation within the same LULC map (2008 and each scenario), we compared sediment and nutrient retention across the landscape. However, for assessing change to scenarios, we compared sediment and nutrient export between the relevant LULC maps, as the change in export (rather than in retention) better reflects the change in service experienced downstream. ...Although InVEST reports ecosystem services in biophysical units, its simple models are best suited to understanding broad patterns of spatial variation (Tallis and Polasky, 2011), rather than for precise quantification. Additionally, we lacked field measurements against which to calibrate our outputs. Therefore, we focused on relative spatial distribution across the landscape, and relative change to scenarios." | AUTHORS DESCRIPTION: "MIMES uses a systems approach to model ecosystem dynamics across a spatially explicit environment. The modeling platform used by this work is a commercially available, object-based modeling and simulation software. This model, referred to as Massachusetts Ocean MIMES, was applied to a selected area of Massachusetts’ coastal waters and nearshore waters. The model explores the implications of management decisions on select marine resources and economic production related to a suite of marine based economic sectors. | 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: "Land use changes are known to significantly affect the soil C balance by altering both C inputs and losses. Since the late 1990s, a large area of the Loess Plateau has undergone intensive land use changes during several ecological restoration projects to control soil erosion and combat land degradation, especially in the Grain for Green project. By using remote sensing techniques and the Yasso07 model, we simulated the dynamics of soil organic carbon (SOC) stocks in the Yangjuangou catchment of the Loess Plateau. The performance of the model was evaluated by comparing the simulated results with the intensive field measurements in 2006 and 2011 throughout the catchment. SOC stocks and NPP values of all land use types had generally increased during our study period. The average SOC sequestration rate in the upper 30 cm soil from 2006 to 2011 in the Yangjuangou catchment was approximately 44 g C m-2 yr-1, which was comparable to other studies in the Loess Plateau. Forest and grassland showed a more effective accumulation of SOC than the other land use types in our study area. The Yasso07 model performed reasonably well in predicting the overall dynamics of SOC stock for different land use change types at both the site and catchment scales. The assessment of the model performance indicated that the combination of Yasso07 model and remote sensing data could be used for simulating the effect of land use changes on SOC stock at catchment scale in the Loess Plateau." | ABSTRACT: "Inorganic nitrogen (N) transformations and removal in aquatic sediments are microbially mediated, and rates influence N-transport. In this study we related physicochemical properties of a large Great Lakes embayment, the St. Louis River Estuary (SLRE) of western Lake Superior, to sediment N-transformation rates. We tested for associations among rates and N-inputs, vegetation biomass, and temperature. We measured rates of nitrification (NIT), unamended base denitrification (DeNIT), and potential denitrification [denitrifying enzyme activity (DEA)] in 2011 and 2012 across spatial and depth zones…Nitrogen cycling rates were spatially and temporally variable, but we modeled how alterations to water depth and N-inputs may impact DeNIT rates." AUTHOR'S DESCRIPTION: "We used different survey designs in 2011 and 2012. Both designs were based on area-weighted probability sampling methods, similar to those developed for EPA's Environmental Monitoring and Assessment Program (EMAP) (Crane et al., 2005; Stevens and Olsen, 2003, 2004). Sampling sites were assigned to spatial zones: “harbor” (river km 0–13), “bay” (river km 13–24), or “river” (river km 24–35) (Fig. 1). Sites were also grouped by depth zones (“shallow,” <1 m; “intermediate,” 1–2 m; and “deep,” >2 m). In 2011 (“vegetated-habitat survey”), the sample frame consisted of areas of emergent and submergent vegetation in the SLRE… The resulting sample frame included 2370 ha of potentially vegetated area out of a total SLRE area of 4378 ha. Sixty sites were distributed across the total vegetated area in each spatial zone using an uneven spatially balanced probabilistic design. Vegetated areas were more prevalent, and thus had greater sampling effort, in the bay (n = 33) and river (n = 17) than harbor (n=10) zones, and in the shallow (n=44) and intermediate (n =14) than deep (n =2) zones. All sampling was done in July. In 2012 a probabilistic sampling design (“estuary-wide survey”) was implemented to determine N-cycling rates for the entire SLRE (not just vegetated areas as in 2011). Thirty sites unevenly distributed across spatial and depth zones were sampled monthly in May–September (Fig. 1). Area weighting for each sampled site reflects the SLRE area attributable to each sample by month, spatial zone, and depth zone." "…we were able to create significant predictive models for NIT and DeNIT rates using linear combinations of physiochemical parameters…" "…Simulations of changes in DeNIT rates in response to altered water depth and surface NOx-N concentration for spring (Fig. 4A) and summer (Fig. 4B) show that for a given season, altering water depths would have a greater influence on DeNIT than rising NO3- concentration." | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Bobolink (Dolichonyx oryzivorus)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: BOBO density = e^(-0.8696546 + 0.0180943 * grass400) | AUTHOR'S DESCRIPTION (from Supporting Information): "Forest recreation service as a function of the amount of forest habitat, recreational opportunities provided, proximity to population center, and accessibility of the area. Several assumptions were made for this assessment approach: larger areas and places with more recreational opportunities would provide more recreational service; areas near large population centers would be visited and used more than remote areas; and proximity to major roads would increase access and thus recreational use of an area… we quantified forest recreation service for each 30-m grid cells using the equation below: FRSi = Ai Σ(Oppti + Popi +Roadi), where FRS is forest recreation score, A is the area of forest habitat, Oppt represents the recreation opportunities, Pop is the proximity to population centers, and Road stands for the distance to major roads. To simplify interpretation, we rescaled the original forest recreation score (ranging from 0 to 5,200) to a range of 0–100, with 0 representing no forest recreation service and 100 representing highest service. | 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: " Global trends in pollinator-dependent crops have raised awareness of the need to support managed and wild bee populations to ensure sustainable crop production. Provision of sufficient forage resources is a key element for promoting bee populations within human impacted landscapes, particularly those in agricultural lands where demand for pollination service is high and land use and management practices have reduced available flowering resources. Recent government incentives in North America and Europe support the planting of wildflowers to benefit pollinators; surprisingly, in North America there has been almost no rigorous testing of the performance of wildflower mixes, or their ability to support wild bee abundance and diversity. We tested different wildflower mixes in a spatially replicated, multiyear study in three regions of North America where production of pollinatordependent crops is high: Florida, Michigan, and California. In each region, we quantified flowering among wildflower mixes composed of annual and perennial species, and with high and low relative diversity. We measured the abundance and species richness of wild bees, honey bees, and syrphid flies at each mix over two seasons. In each region, some but not all wildflower mixes provided significantly greater floral display area than unmanaged weedy control plots. Mixes also attracted greater abundance and richness of wild bees, although the identity of best mixes varied among regions. By partitioning floral display size from mix identity we show the importance of display size for attracting abundant and diverse wild bees. Season-long monitoring also revealed that designing mixes to provide continuous bloom throughout the growing season is critical to supporting the greatest pollinator species richness. Contrary to expectation, perennials bloomed in their first season, and complementarity in attraction of pollinators among annuals and perennials suggests that inclusion of functionally diverse species may provide the greatest benefit. Wildflower mixes may be particularly important for providing resources for some taxa, such as bumble bees, which are known to be in decline in several regions of North America. No mix consistently attained the full diversity that was planted. Further study is needed on how to achieve the desired floral display and diversity from seed mixes. " Additional information in supplemental Appendices online: http://dx.doi.org/10.1890/14-1748.1.sm | ABSTRACT: "Because of natural environmental and faunal differences and scientific perspectives, numerous indices of biological integrity (IBIs) have been developed at local, state, and regional scales in the USA. These multiple IBIs, plus different criteria for judging impairment, hinder rigorous national and multistate assessments. Many IBI metrics are calibrated for water body size, but none are calibrated explicitly for other equally important natural variables such as air temperature, channel gradient, or geology. We developed a predictive aquatic-vertebrate IBI model using a total of 871 stream sites (including 162 least-disturbed and 163 most-disturbed sites) sampled as part of the U.S. Environmental Protection Agency’s Environmental Monitoring and Assessment Program survey of 12 conterminous western U.S. states. The selected IBI metrics (calculated from both fish and aquatic amphibians) were vertebrate species richness, benthic native species richness, assemblage tolerance index, proportion of invertivore–piscivore species, and proportion of lithophilic-reproducing species. Mean model IBI scores differed significantly between least-disturbed and most-disturbed sites as well as among ecoregions. Based on a model IBI impairment criterion of 0.44 (risks of type I and II errors balanced), an estimated 34.7% of stream kilometers in the western USA were deemed impaired, compared with 18% for a set of traditional IBIs. Also, the model IBI usually displayed less variability than the traditional IBIs, presumably because it was better calibrated for natural variability. " | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds." | ABSTRACT: [Sport fishing is an important recreational and economic activity, especially in Australia, Europe and North America, and the condition of sport fish populations is a key ecological indicator of water body condition for millions of anglers and the public. Despite its importance as an ecological indicator representing the status of sport fish populations, an index for measuring this ecosystem service has not been quantified by analyzing actual fish taxa, size and abundance data across the U.S.A. Therefore, we used game fish data collected from 1,561 stream and river sites located throughout the conterminous U.S.A. combined with specific fish species and size dollar weights to calculate site-specific recreational fishery index (RFI) scores. We then regressed those scores against 38 potential site-specific environmental predictor variables, as well as site-specific fish assemblage condition (multimetric index; MMI) scores based on entire fish assemblages, to determine the factors most associated with the RFI scores. We found weak correlations between RFI and MMI scores and weak to moderate correlations with environmental variables, which varied in importance with each of 9 ecoregions. We conclude that the RFI is a useful indicator of a stream ecosystem service, which should be of greater interest to the U.S.A. public and traditional fishery management agencies than are MMIs, which tend to be more useful for ecologists, environmentalists and environmental quality agencies.] | ASTRACT: "In this work, a model for wave transformation on vegetation fields is presented. The formulation includes wave damping and wave breaking over vegetation fields at variable depths. Based on a nonlinear formulation of the drag force, either the transformation of monochromatic waves or irregular waves can be modelled considering geometric and physical characteristics of the vegetation field. The model depends on a single parameter similar to the drag coefficient, which is parameterized as a function of the local Keulegan–Carpenter number for a specific type of plant. Given this parameterization, determined with laboratory experiments for each plant type, the model is able to reproduce the root-mean-square wave height transformation observed in experimental data with reasonable accuracy." ENTERER'S COMMENT: Random wave transformation model; equations 31 and 32. |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | Not reported | Not reported | None identified | None Identified | Land management, ecosystem management, response to EU 2020 Biodiversity Strategy | Not applicable | Restoration of seagrass | None identified | Transport of solids for characterizing rivers in the European Water Framework Directive (WFD) | This analysis provided input to government-led spatial planning and strategic environmental assessments in the study area. This region contains some of the last remaining forest habitat of the critically endangered Sumatran tiger, Panthera tigris sumatrae. | None identified | None identified | None | None identified | None identified | None identified | None provided | None identrified | None reported | None reported | None identified | None identified |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | No additional description provided | Upper Mississipi River basin, elevation 142-194m, | No additional description provided | Coastal to montane | Northern Spain; Bizkaia region | nearshore; <1.5 km offshore; <12 m depth | Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove | Estuarine Emergent; Agricultural; Salt Marsh; Palustrine Emergent; Palustrine Forested | The Aixola watershed drains into the Aixola reservoir, which has a cpacity of 2.73 x 10^6 m^3, and is used for water supply. The elevation ranges from 340 m at the outlet of the watershed to 750 m at the highest peak, with a mean elevation of 511 m a.s.l. Most slopes in the watershed are less than 30%. The region is characterized by a humid and temperate climate. The mean annual precipitation is about 1480 mm, distributed fairly evenly throughout the year.; the mean annual temperature is 12 degrees C; and the mean annual discharge is 600 mm (around 0.092 m^3 s^−1). Autochthonus vegetation is limited to small patches, and commercial foresty, mostly evergreen stands composed mainly of Pinus radiata (Monterey pine), occupies more than 80% of the watershed. The lithology is highly homogenous, with most of the bedrock (94%) consisting of impervious Upper Cretaceous Calcareous Flysch. The main types of soils are relatively deep cambisols and regosols, with depths ranging from 0.8 to 10 m and a silt-loam texture. During the 2003-2008 period, mean suspended sediment yield calculated for the watershed was 36 t km^-2. | Six watersheds in central Sumatra covering portions of Riau, Jambi and West Sumatra provinces. The Barisan mountain range comprises the western edge of the watersheds, while peat swamps predominate in the east. | No additional description provided | No additional description provided | Agricultural plain, hills, gulleys, forest, grassland, Central China | No additional description provided | Prairie pothole region of north-central Iowa | No additional description provided | Hard and soft benthic habitat types approximately to the 33m isobath | field plots near agricultural fields (mixed row crop, almond, walnuts), central valley, Ca | Wadeable and boatable streams in 12 western USA states | Conservation Reserve Program lands left to go fallow | None | No additional description provided |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use change (2000-2030) | No scenarios presented | No scenarios presented | Not applicable | Habitat loss or restoration in Tampa Bay Estuary | No scenarios presented | Four future climate change scenarios combining two IPCC SRES scenarios and two GCMs | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | No scenarios presented | No scenarios presented | Land use change | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Varied wildflower planting mixes of annuals and perennials | not applicable | N/A | N/A | No scenarios presented |
EM ID
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EM-70 | EM-80 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-146 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-363 ![]() |
EM-376 | EM-449 | EM-469 |
EM-496 ![]() |
EM-648 | EM-654 | EM-698 |
EM-784 ![]() |
EM-821 ![]() |
EM-846 | EM-862 | EM-896 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs |
Method + Application (multiple runs exist) View EM Runs ?Comment:Each of the seven runs represents a different site (ecoregion) along a west to east Oregon transect. An eighth site was not forested and its results were not included. |
Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | 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 (multiple runs exist) View EM Runs | Method + Application View EM Runs | Method + Application | Method + Application | Method Only |
New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | New or revised model | Application of existing model | Application of existing model | New or revised model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
New or revised 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 |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
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EM-70 | EM-80 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-146 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-363 ![]() |
EM-376 | EM-449 | EM-469 |
EM-496 ![]() |
EM-648 | EM-654 | EM-698 |
EM-784 ![]() |
EM-821 ![]() |
EM-846 | EM-862 | EM-896 |
EM Temporal Extent
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2007-2009 | Not reported | Not reported | 1987-1997 | 1980-2006 | 1990-2030 | 1500 yrs | 2000 - 2007 | 2006-2007 | 1982-2010 | 2004 | 1961-2100 | 2008-2020 | Not applicable | 2006-2007, 2010 | 1969-2011 |
July 2011 to September 2012 ?Comment:All sampling performed July 2011, and May-September 2012. |
2002-2007 | 2000-2006 | 2000-2005 | 2011-2012 | 2004-2005 | 2008 | 2013-2014 | Not applicable |
EM Time Dependence
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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-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | future time | Not applicable | past time | Not applicable | Not applicable | Not applicable | Not applicable | past time | past time | Not applicable | past time | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | continuous | Not applicable | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | continuous |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | 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 | 1 | Not applicable | Not applicable | 1 | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Year | Not applicable |
EM ID
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EM-70 | EM-80 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-146 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-363 ![]() |
EM-376 | EM-449 | EM-469 |
EM-496 ![]() |
EM-648 | EM-654 | EM-698 |
EM-784 ![]() |
EM-821 ![]() |
EM-846 | EM-862 | EM-896 |
Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Geopolitical | Physiographic or ecological | Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Multiple unrelated locations (e.g., meta-analysis) | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Watershed/Catchment/HUC | Physiographic or ecological |
Point or points ?Comment:This is a guess based on information in the document. 3 field sites were separated by up to 9km |
Geopolitical | Physiographic or ecological | Geopolitical | Not applicable |
Spatial Extent Name
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Central French Alps | Central French Alps | Central French Alps | Upper Mississippi River basin; St. Croix River Watershed | East Fork Kaskaskia River watershed basin | The EU-25 plus Switzerland and Norway | Western Oregon transect | Bilbao Metropolitan Greenbelt | St. Croix, U.S. Virgin Islands | Tampa Bay Estuary | Contiguous U.S. | Aixola watershed | central Sumatra | Massachusetts Ocean | Coastal zone surrounding St. Croix | Yangjuangou catchment | St. Louis River Estuary (of western Lake Superior) | CREP (Conservation Reserve Enhancement Program) wetland sites | Yahara Watershed, Wisconsin | SW Puerto Rico, | Agricultural plots | Western 12 states | Piedmont Ecoregion | United States | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 10-100 km^2 | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | 1-10 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 1-10 km^2 | 10-100 km^2 | 1-10 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 10-100 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | >1,000,000 km^2 | Not applicable |
EM ID
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EM-70 | EM-80 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-146 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-363 ![]() |
EM-376 | EM-449 | EM-469 |
EM-496 ![]() |
EM-648 | EM-654 | EM-698 |
EM-784 ![]() |
EM-821 ![]() |
EM-846 | EM-862 | EM-896 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially lumped (in all cases) ?Comment:Computations performed at the area size of 0.08 ha. |
spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially 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) ?Comment:871 total sites surveyed for this work |
spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | NHDplus v1 | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | length, for linear feature (e.g., stream mile) | length, for linear feature (e.g., stream mile) |
Spatial Grain Size
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20 m x 20 m | 20 m x 20 m | 20 m x 20 m | NHDplus v1 | 1 km^2 | 1 km x 1 km | Not applicable | 2 m x 2 m | Not applicable | 1 ha | Not applicable | Average size 0.2 km^2 | 30 m x 30 m | 1 km x1 km | 10 m x 10 m | 30m x 30m | 35 km river estuary reach, 0 to 5 m depth by 1 m increment | multiple, individual, irregular shaped sites | 30m x 30m | not reported | Not applicable | stream reach | Not applicable | stream reach (site) | 1m |
EM ID
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EM-70 | EM-80 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-146 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-363 ![]() |
EM-376 | EM-449 | EM-469 |
EM-496 ![]() |
EM-648 | EM-654 | EM-698 |
EM-784 ![]() |
EM-821 ![]() |
EM-846 | EM-862 | EM-896 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Analytic | Analytic | Numeric | Numeric | Logic- or rule-based | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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EM ID
em.detail.idHelp
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EM-70 | EM-80 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-146 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-363 ![]() |
EM-376 | EM-449 | EM-469 |
EM-496 ![]() |
EM-648 | EM-654 | EM-698 |
EM-784 ![]() |
EM-821 ![]() |
EM-846 | EM-862 | EM-896 |
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | No | Yes | No | No | No | No | Yes | Yes | Yes | Yes | No | No | Yes | Yes | Yes | Unclear | No | No | No | No | Yes | No | No |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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Yes | No | No | Yes | No | No | No | No | Yes | No | Yes | No | No | No | No |
Yes ?Comment:For the year 2006 and 2011 |
Yes | No | No | Yes | No | No | No | No | Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None |
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None | None | None | None |
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None | None | None | None |
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None | None |
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None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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No | No | No | No | Yes | No | Yes | Yes | No | No | No | Yes | No | No | Yes | No | No | Unclear | No | Yes | No |
Yes ?Comment:Compared to another journal manuscript IBI scores (Whittier et al) |
No | No | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | Yes | No | No | No | Yes | No | Yes | No | No | No | No | No | No | No | No | No | No | No | No | No | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No |
No ?Comment:Some model coefficients serve, by their magnitude, to indicate the proportional impact on the final result of variation in the parameters they modify. |
Unclear | No | No | No | No | No | Yes | Yes | No | No | No | No | No | No | No | Yes | 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 | Not applicable | Not applicable | Not applicable | Not applicable | Yes | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Yes | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-70 | EM-80 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-146 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-363 ![]() |
EM-376 | EM-449 | EM-469 |
EM-496 ![]() |
EM-648 | EM-654 | EM-698 |
EM-784 ![]() |
EM-821 ![]() |
EM-846 | EM-862 | EM-896 |
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None | None | None |
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None | None |
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None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-70 | EM-80 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-146 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-363 ![]() |
EM-376 | EM-449 | EM-469 |
EM-496 ![]() |
EM-648 | EM-654 | EM-698 |
EM-784 ![]() |
EM-821 ![]() |
EM-846 | EM-862 | EM-896 |
None | None | None | None | None | None | None | None |
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None | None | None |
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None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-70 | EM-80 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-146 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-363 ![]() |
EM-376 | EM-449 | EM-469 |
EM-496 ![]() |
EM-648 | EM-654 | EM-698 |
EM-784 ![]() |
EM-821 ![]() |
EM-846 | EM-862 | EM-896 |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | 45.05 | 45.05 | 42.5 | 38.69 | 50.53 | 44.13 | 43.25 | 17.75 | 27.95 | -9999 | 43 | 0 | 41.72 | 17.73 | 36.7 | 46.74 | 42.62 | 43.1 | 17.79 | 29.4 | 44.2 | 36.23 | 36.21 | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
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6.4 | 6.4 | 6.4 | -90.63 | -89.1 | 7.6 | -122.5 | -2.92 | -64.75 | -82.47 | -9999 | -1 | 102 | -69.87 | -64.77 | 109.52 | -96.13 | -93.84 | -89.4 | -64.62 | -82.18 | -113.07 | -81.9 | -113.76 | Not applicable |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | NAD83 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Provided | Provided | Estimated | Provided | Estimated | Provided | Provided | Estimated | Estimated | Not applicable | Provided | Provided | Estimated | Estimated | Provided | Estimated | Estimated | Provided | Estimated | Provided | Estimated | Estimated | Estimated | Not applicable |
EM ID
em.detail.idHelp
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EM-70 | EM-80 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-146 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-363 ![]() |
EM-376 | EM-449 | EM-469 |
EM-496 ![]() |
EM-648 | EM-654 | EM-698 |
EM-784 ![]() |
EM-821 ![]() |
EM-846 | EM-862 | EM-896 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Agroecosystems | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Forests | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Inland Wetlands | Near Coastal Marine and Estuarine | Rivers and Streams | Forests | Barren | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Rivers and Streams | Inland Wetlands | Inland Wetlands | Agroecosystems | Grasslands | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Near Coastal Marine and Estuarine | Agroecosystems | Rivers and Streams | Grasslands | Rivers and Streams | Near Coastal Marine and Estuarine |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Subalpine terraces, grasslands, and meadows. | None | Row crop agriculture in Kaskaskia river basin | Not applicable | Primarily conifer forest | none | stony coral reef | Subtropical Estuary | Wetlands (multiple types) | Forested watershed used for commercial forestry | 104 land use land cover classes | None identified | Coral reefs | Loess plain | River and riverine estuary (lake) | Grassland buffering inland wetlands set in agricultural land | Mixed environment watershed of prairie converted to predominantly agriculture and urban landscape | shallow coral reefs | Agricultural landscape | wadeable and boatable streams | grasslands | reach | Near coastal marine and estuarine |
EM Ecological Scale
em.detail.ecoScaleHelp
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Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecosystem | 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 is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-70 | EM-80 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-146 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-363 ![]() |
EM-376 | EM-449 | EM-469 |
EM-496 ![]() |
EM-648 | EM-654 | EM-698 |
EM-784 ![]() |
EM-821 ![]() |
EM-846 | EM-862 | EM-896 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Community | Community | Not applicable | Not applicable | Not applicable | Species | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Community | Species | Not applicable | Not applicable | Not applicable | Species | Not applicable | Guild or Assemblage | Species | Guild or Assemblage | Species | Guild or Assemblage | Species |
Taxonomic level and name of organisms or groups identified
EM-70 | EM-80 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-146 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-363 ![]() |
EM-376 | EM-449 | EM-469 |
EM-496 ![]() |
EM-648 | EM-654 | EM-698 |
EM-784 ![]() |
EM-821 ![]() |
EM-846 | EM-862 | EM-896 |
None Available | None Available | None Available | None Available | None Available | None Available |
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None Available |
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None Available | None Available | None Available | None Available |
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None Available | None Available | None Available |
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None Available |
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None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-70 | EM-80 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-146 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-363 ![]() |
EM-376 | EM-449 | EM-469 |
EM-496 ![]() |
EM-648 | EM-654 | EM-698 |
EM-784 ![]() |
EM-821 ![]() |
EM-846 | EM-862 | EM-896 |
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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-70 | EM-80 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-146 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-275 ![]() |
EM-363 ![]() |
EM-376 | EM-449 | EM-469 |
EM-496 ![]() |
EM-648 | EM-654 | EM-698 |
EM-784 ![]() |
EM-821 ![]() |
EM-846 | EM-862 | EM-896 |
None |
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None | None |
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None |
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None |
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None | None |
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None |