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-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-321 ![]() |
EM-338 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 | EM-491 | EM-627 | EM-659 |
EM-660 ![]() |
EM-706 | EM-846 | EM-896 |
EM Short Name
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Cultural 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 | 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 | Erosion prevention by vegetation, Portel, Portugal | InVEST crop pollination, California, USA | MIMES: For Massachusetts Ocean (v1.0) | Denitrification rates, Guánica Bay, Puerto Rico | State of the reef index, St. Croix, USVI | Yasso 15 - soil carbon model | Yasso07 - SOC, Loess Plateau, China | EnviroAtlas - Crops with no pollinator habitat | N removal by wetland restoration, Midwest, USA | LUCI, New Zealand | RUM: Valuing fishing quality, Michigan, USA | WESP Method | Indigo bunting abund, Piedmont region, USA | Random wave transformation on vegetation fields |
EM Full Name
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Cultural 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 | 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 | Soil erosion prevention provided by vegetation cover, Portel municipality, Portugal | InVEST crop pollination, California, USA | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | Denitrification rates, Guánica Bay, Puerto Rico, USA | State of the reef index, St. Croix, USVI | Yasso 15 - soil carbon | Yasso07 - Land Use Effects on Soil Organic Carbon Stocks in the Loess Plateau, China | US EPA EnviroAtlas - Acres of pollinated crops with no nearby pollinator habitat, USA | Nitrate removal by potential wetland restoration, Mississippi River subbasins, USA | LUCI (Land Utilisation and Capability Indicator), New Zealand | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | Method for the Wetland Ecosystem Services Protocol (WESP) | Indigo bunting abundance, Piedmont ecoregion, USA | Random wave transformation on vegetation fields |
EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | US EPA | US EPA | EU Biodiversity Action 5 |
None ?Comment:EU Mapping Studies |
US EPA | US EPA | US EPA | EU Biodiversity Action 5 | InVEST | US EPA | US EPA | US EPA | None | None | US EPA | EnviroAtlas | None | None | None | None | None | None |
EM Source Document ID
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260 | 260 | 123 | 137 | 228 | 191 | 96 | 186 | 63 | 281 | 279 | 316 | 338 | 335 |
342 ?Comment:Webpage pdf users manual for model. |
344 | 262 |
370 ?Comment:Final project report to U.S. Department of Agriculture; Project number: IOW06682. December 2006. |
380 ?Comment:Document 381 is an additional source for this EM. |
382 ?Comment:Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010. |
390 | 405 | 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. | 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. | 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. | Guerra, C.A., Pinto-Correia, T., Metzger, M.J. | Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | Altman, I., R.Boumans, J. Roman, L. Kaufman | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Repo, A., Jarvenpaa, M., Kollin, J., Rasinmaki, J. and Liski, J. | Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu | US EPA Office of Research and Development - National Exposure Research Laboratory | Crumpton, W. G., G. A. Stenback, B. A. Miller, and M. J. Helmers | Trodahl, M. I., B. M. Jackson, J. R. Deslippe, and A. K. Metherell | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | Adamus, P. R. | Riffel, S., Scognamillo, D., and L. W. Burger | Mendez, F. J. and I. J. Losada |
Document Year
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2011 | 2011 | 2013 | 2011 | 2012 | 2013 | 2011 | 2013 | 2011 | 2014 | 2009 | 2012 | 2017 | 2014 | 2016 | 2015 | 2013 | 2006 | 2017 | 2014 | 2016 | 2008 | 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 | 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 | 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 | Mapping soil erosion prevention using an ecosystem service modeling framework for integrated land management and policy | Modelling pollination services across agricultural landscapes | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Yasso 15 graphical user-interface manual | Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement | EnviroAtlas - National | Potential benefits of wetland filters for tile drainage systems: Impact on nitrate loads to Mississippi River subbasins | Investigating trade-offs between water quality and agricultural productivity using the Land Utilisation and Capability Indicator (LUCI)-A New Zealand application | Valuing recreational fishing quality at rivers and streams | Manual for the Wetland Ecosystem Services Protocol (WESP) v. 1.3. | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | 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 | Documented, not peer reviewed | Peer reviewed and published | Peer reviewed and published | Other or unclear (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Neither peer reviewed nor published (explain in Comment) | 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 report | Published journal manuscript | Published journal manuscript | Not applicable | Published journal manuscript | Published on US EPA EnviroAtlas website | Published report | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript |
EM ID
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-321 ![]() |
EM-338 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 | EM-491 | EM-627 | EM-659 |
EM-660 ![]() |
EM-706 | EM-846 | EM-896 |
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://www.naturalcapitalproject.org/models/crop_pollination.html | http://www.afordablefutures.com/orientation-to-what-we-do | Not applicable | Not applicable |
http://en.ilmatieteenlaitos.fi/yasso-download-and-support ?Comment:User's manual states that the software will be downloadable at this site. |
http://en.ilmatieteenlaitos.fi/yasso-download-and-support | https://www.epa.gov/enviroatlas | Not applicable |
info@lucitools.org ?Comment:To obtain LUCI, email us your enquiry at info@lucitools.org with information about: The problem you are seeking to solve or your research question. The country and region you wish to apply LUCI in. What data you have with as much detail as possible about the data sources. Your timeframe or deadlines. |
Not applicable |
http://people.oregonstate.edu/~adamusp/WESP/ ?Comment:This is an Excel spreadsheet calculator |
Not applicable | Not applicable | |
Contact Name
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Sandra Lavorel | Sandra Lavorel | Liem Tran | Yongping Yuan | Marion Potschin | Izaskun Casado-Arzuaga | Leah Oliver | M. Russell | Steve Jordan | Carlos A. Guerra | Eric Lonsdorf | Irit Altman | Susan H. Yee | Susan H. Yee | Jari Liski | Xing Wu | EnviroAtlas Team | William G. Crumpton | Martha I. Trodahl | Richard Melstrom | Paul R. Adamus | Sam Riffell |
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 | 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 | 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 | Instituto de Ciências Agrárias e Ambientais Mediterrânicas, Universidade de Évora, Pólo da Mitra, Apartado 94, 7002-554 Évora, Portugal | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | Boston University, Portland, Maine | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki | Chinese Academy of Sciences, Beijing 100085, China | Not reported | Dept. of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA 50011 | School of Geography, Environment & Earth Sciences, Victoria University of Wellington, New Zealand | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | 6028 NW Burgundy Dr. Corvallis, OR 97330 | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Not reported |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | ltran1@utk.edu | yuan.yongping@epa.gov | marion.potschin@nottingham.ac.uk | izaskun.casado@ehu.es | leah.oliver@epa.gov | Russell.Marc@epamail.epa.gov | steve.jordan@epa.gov | cguerra@uevora.pt | ericlonsdorf@lpzoo.org | iritaltman@bu.edu | yee.susan@epa.gov | yee.susan@epa.gov | jari.liski@ymparisto.fi | xingwu@rceesac.cn | enviroatlas@epa.gov | crumpton@iastate.edu | Not reported | melstrom@okstate.edu | adamus7@comcast.net | sriffell@cfr.msstate.edu | mendezf@unican.es |
EM ID
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-321 ![]() |
EM-338 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 | EM-491 | EM-627 | EM-659 |
EM-660 ![]() |
EM-706 | EM-846 | 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: "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: "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 "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 present an integrative conceptual framework to estimate the provision of soil erosion prevention (SEP) by combining the structural impact of soil erosion and the social–ecological processes that allow for its mitigation. The framework was tested and illustrated in the Portel municipality in Southern Portugal, a Mediterranean silvo-pastoral system that is prone to desertification and soil degradation. The results show a clear difference in the spatial and temporal distribution of the capacity for ecosystem service provision and the actual ecosystem service provision." AUTHOR'S DESCRIPTION: "To begin assessing the contribution of SEP we need to identify the structural impact of soil erosion, that is, the erosion that would occur when vegetation is absent and therefore no ES is provided. It determines the potential soil erosion in a given place and time and is related to rainfall erosivity (that is, the erosive potential of rainfall), soil erodibility (as a characteristic of the soil type) and local topography. Although external drivers can have an effect on these variables (for example, climate change), they are less prone to be changed directly by human action. The actual ES provision reduces the total amount of structural impact, and we define the remaining impact as the ES mitigated impact. We can then define the capacity for ES provision as a key component to determine the fraction of the structural impact that is mitigated…Following the conceptual outline, we will estimate the SEP provided by vegetation cover using an adaptation of the Universal Soil Loss Equation (USLE)." | 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: "Background and Aims: Crop pollination by bees and other animals is an essential ecosystem service. Ensuring the maintenance of the service requires a full understanding of the contributions of landscape elements to pollinator populations and crop pollination. Here, the first quantitative model that predicts pollinator abundance on a landscape is described and tested. Methods: Using information on pollinator nesting resources, floral resources and foraging distances, the model predicts the relative abundance of pollinators within nesting habitats. From these nesting areas, it then predicts relative abundances of pollinators on the farms requiring pollination services. Model outputs are compared with data from coffee in Costa Rica, watermelon and sunflower in California and watermelon in New Jersey–Pennsylvania (NJPA). Key Results: Results from Costa Rica and California, comparing field estimates of pollinator abundance, richness or services with model estimates, are encouraging, explaining up to 80 % of variance among farms. However, the model did not predict observed pollinator abundances on NJPA, so continued model improvement and testing are necessary. The inability of the model to predict pollinator abundances in the NJPA landscape may be due to not accounting for fine-scale floral and nesting resources within the landscapes surrounding farms, rather than the logic of our model. Conclusions: The importance of fine-scale resources for pollinator service delivery was supported by sensitivity analyses indicating that the model's predictions depend largely on estimates of nesting and floral resources within crops. Despite the need for more research at the finer-scale, the approach fills an important gap by providing quantitative and mechanistic model from which to evaluate policy decisions and develop land-use plans that promote pollination conservation and service delivery. " | 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. | AUTHOR'S DESCRIPTION: "Improving water quality was an objective of stakeholders in order to improve human health and reduce impacts to coral reef habitats. Four ecosystem services contributing to water quality were identified: denitrification...Denitrification rates were assigned to each land cover class, applying the mean of rates for natural sub-tropical ecosystems obtained from the literature…" | 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 | AUTHOR'S DESCRIPTION: "The Yasso15 calculates the stock of soil organic carbon, changes in the stock of soil organic carbon and heterotrophic soil respiration. Applications the model include, for example, simulations of land use change, ecosystem management, climate change, greenhouse gas inventories and education. The Yasso15 is a relatively simple soil organic carbon model requiring information only on climate and soil carbon input to operate... In the Yasso15 model litter is divided into five soil organic carbon compound groups (Fig. 1). These groups are compounds hydrolysable in acid (denoted with A), compounds soluble in water (W) or in a non-polar solvent, e.g. ethanol or dichloromethane (E), compounds neither soluble nor hydrolysable (N) and humus (H). The AWEN form the group of labile fractions whereas H fraction contains humus, which is more recalcitrant to decomposition. Decomposition of the fractions results in carbon flux out of soil and carbon fluxes between the compartments (Fig. 1). The basic idea of Yasso15 is that the decomposition of different types of soil carbon input depends on the chemical composition of the input types and climate conditions. The effects of the chemical composition are taken into account by dividing carbon input to soil between the four labile compartments explicitly according to the chemical composition (Fig. 1). Decomposition of woody litter depends additionally on the size of the litter. The effects of climate conditions are modelled by adjusting the decomposition rates of the compartments according to air temperature and precipitation. In the Yasso15 model separate decomposition rates are applied to fast-decomposing A, W and E compartments, more slowly decomposing N and very slowly decomposing humus compartment H. The Yasso is a global-level model meaning that the same parameter values are suitable for all applications for accurate predictions. However, the current GUI version also includes possibility to use earlier parameterizations. The parameter values of Yasso15 are based on measurements related to cycling of organic carbon in soil (Table 1). An extensive set of litter decomposition measurements was fundamental in developing the model (Fig. 2). This data set covered, firstly, most of the global climate conditions in terms of temperature precipitation and seasonality (Fig 3.), secondly, different ecosystem types from forests to grasslands and agricultural fields and, thirdly, a wide range of litter types. In addition, a large set of data giving information on decomposition of woody litter (including branches, stems, trunks, roots with different size classes) was used for fitting. In addition to woody and non-woody litter decomposition measurements, a data set on accumulation of soil carbon on the Finnish coast and a large, global steady state data sets were used in the parameterization of the model. These two data sets contain information on the formation and slow decomposition of humus." | 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." | DATA FACT SHEET: "This EnviroAtlas national map estimates the total acres of agricultural crops within each 12-digit hydrologic unit (HUC) that have varying amounts of nearby forested pollinator habitat. The crop types selected from the U.S. Department of Agriculture Cropland Data Layer (CDL) require (or would benefit from) the presence of pollinators, but crops may have no nearby native pollinator habitat. This metric is based on the average flight distance of native bees, both social and solitary, that nest in woodland habitats and forage on native plants and cultivated crops." "The metric was generated using the ESRI ArcMap Neighborhood Distance tool in conjunction with a blended landcover grid, which included the 2006 National Land Cover Dataset (NLCD) and U.S. Department of Agriculture National Agricultural Statistics Service Cropland Data Layer (CDL). Pollinator habitat is defined as trees (fruit, nut, deciduous, and evergreen) for nesting and associated woodland for additional pollen sources. Crops that either require or benefit from pollination were selected and a distance measure of 2.8 kilometers (the average bee species’ foraging distance from the nest4) was used to assess presence or absence of nearby native pollinator habitat. The total area of crops without nearby pollinator habitat was summarized by 12-digit HUC boundaries taken from the NHDPlusV2 Watershed Boundary Dataset (WBD Snapshot)." | ABSTRACT: "The primary objective of this project was to estimate the nitrate reduction that could be achieved using restored wetlands as nitrogen sinks in tile-drained regions of the upper Mississippi River (UMR) and Ohio River basins. This report provides an assessment of nitrate concentrations and loads across the UMR and Ohio River basins and the mass reduction of nitrate loading that could be achieved using wetlands to intercept nonpoint source nitrate loads. Nitrate concentration and stream discharge data were used to calculate stream nitrate loading and annual flow-weighted average (FWA) nitrate concentrations and to develop a model of FWA nitrate concentration based on land use. Land use accounts for 90% of the variation among stations in long term FWA nitrate concentrations and was used to estimate FWA nitrate concentrations for a 100 ha grid across the UMR and Ohio River basins. Annual water yield for grid cells was estimated by interpolating over selected USGS monitoring station water yields across the UMR and Ohio River basins. For 1990 to 1999, mass nitrate export from each grid area was estimated as the product of the FWA nitrate concentration, water yield and grid area. To estimate potential nitrate removal by wetlands across the same grid area, mass balance simulations were used to estimate percent nitrate reduction for hypothetical wetland sites distributed across the UMR and Ohio River basins. Nitrate reduction was estimated using a temperature dependent, area-based, first order model. Model inputs included local temperature from the National Climatic Data Center and water yield estimated from USGS stream flow data. Results were used to develop a nonlinear model for percent nitrate removal as a function of hydraulic loading rate (HLR) and temperature. Mass nitrate removal for potential wetland restorations distributed across the UMR and Ohio River basin was estimated based on the expected mass load and the predicted percent removal. Similar functions explained most of the variability in per cent and mass removal reported for field scale experimental wetlands in the UMR and Ohio River basins. Results suggest that a 30% reduction in nitrate load from the UMR and Ohio River basins could be achieved using 210,000-450,000 ha of wetlands targeted on the highest nitrate contributing areas." AUTHOR'S DESCRIPTION: "Percent nitrate removal was estimated based on HLR functions (Figure 19) spanning a 3 fold range in loss rate coefficient (Crumpton 2001) and encompassing the observed performance reported for wetlands in the UMR and Ohio River basins (Table 2, Figure 7). The nitrate load was multiplied by the expected percent nitrate removal to estimate the mass removal. This procedure was repeated for each restoration scenario each year in the simulation period (1990 to 1999)… for a scenario with a wetland/watershed area ratio of 2%. These results are based on the assumption that the FWA nitrate concentration versus percent row crop r | ABSTRACT: "...The Land Utilisation & Capability Indicator (LUCI) is a GIS framework that considers impacts of land use on multiple ecosystem services in a holistic and spatially explicit manner. Due to its fine spatial scale and focus on the rural environment, LUCI is well-placed to help both farm and catchment managers to explore and quantify spatially explicit solutions to improve water quality while also maintaining or enhancing other ecosystem service outcomes. LUCI water quality and agricultural productivity models were applied to a catchment in the Bay of Plenty, New Zealand. Nitrogen (N) and phosphorus (P) sources, sinks and pathways in the landscape were identified and trade-offs and synergies between water quality and agricultural productivity were investigated. Results indicate that interventions to improve water quality are likely to come at the expense of agriculturally productive land. Nonetheless, loss of agriculturally productive land can be minimised by using LUCI to identify, at a fine spatial scale, the most appropriate locations for nutrient intervention. Spatially targeted and strategic nutrient source management and pathway interception can improve water quality, while minimising negative financial impacts on farms. Our results provide spatially explicit solutions to optimize agricultural productivity and water quality, which will inform better farm, land and catchment management as well as national and international policy." AUTHOR'S DESCRIPTION (of OVERSEER submodel): "Water quality models within LUCI use an enhanced, spatially representative export co-efficient (EC) approach to model total nitrogen (TN) and total phosphorus (TP) exports to water… Here, ECs for pastoral land cover are calculated by LUCI using algorithms derived from a large ( > 14 000 samples), pastorally based national dataset. The dataset consists of detailed farm nutrient input and management variables that have been entered and run using OVERSEER® to generate nutrient loss predictions, which are also included in the dataset." NOTE: The LUCI model, is a second-generation extension and software implementation of the Polyscape framework, as described in EM-658. https://esml.epa.gov/detail/em/658 | ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. " | 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:"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." | 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 | Not reported | Not reported | None identified | Land management, ecosystem management, response to EU 2020 Biodiversity Strategy | Not applicable | Restoration of seagrass | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None | None Identified | None identified | Land management trade off between agricultural productivity and water quality | None identified | None identified | None reported | None identified |
Biophysical Context
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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 | No additional description provided | Upper Mississipi River basin, elevation 142-194m, | No additional description provided | 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 | Open savannah-like forest of cork (Quercus suber) and holm (Quercus ilex) oaks, with trees of different ages randomly dispersed in changing densities, and pastures in the under cover. The pastures are mostly natural in a mosaic with patches of shrubs, which differ in size and the distribution depends mainly on the grazing intensity. Shallow, poor soils are prone to erosion, especially in areas with high grazing pressure. | No additional description provided | No additional description provided | No additional description provided | No additional description provided | Not applicable | Agricultural plain, hills, gulleys, forest, grassland, Central China | No additional description provided | No additional description provided | Groundwater dominated, volcanic caldera catchment, largely comprised of porous allophanic and pumice soils. | stream and river reaches of Michigan | None | Conservation Reserve Program lands left to go fallow | No additional description provided |
EM Scenario Drivers
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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 | Not applicable | Habitat loss or restoration in Tampa Bay Estuary | No scenarios presented | Different land management practices as represented by the comparison of different grazing intensities (i.e., livestock densities) in the whole study area and in three Civil Parishes within the study area | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Land use change | No scenarios presented | More conservative, average and less conservative nitrate loss rate | No scenarios presented | targeted sport fish biomass | N/A | N/A | No scenarios presented |
EM ID
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-321 ![]() |
EM-338 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 | EM-491 | EM-627 | EM-659 |
EM-660 ![]() |
EM-706 | EM-846 | EM-896 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | 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 Only | Method + Application | Method + Application | Method + Application (multiple runs exist) | Method + Application | 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 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | New or revised model | Application of existing 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 |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-321 ![]() |
EM-338 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 | EM-491 | EM-627 | EM-659 |
EM-660 ![]() |
EM-706 | EM-846 | EM-896 |
EM Temporal Extent
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Not reported | Not reported | 1987-1997 | 1980-2006 | 1990-2030 | 2000 - 2007 | 2006-2007 | 1982-2010 | 2004 | January to December 2003 | 2001-2002 | Not applicable |
1989 - 2011 ?Comment:6/21/16 BH - Rates were assigned from literature, ranging from 1989 - 2006, and the denitrification rate for urban lawns comes from 2011 literature. |
2006-2007, 2010 | Not applicable | 1969-2011 | 2001-2015 | 1973-1999 | 1930-2013 | 2008-2010 | Not applicable | 2008 | Not applicable |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | future time | Not applicable | Not applicable | Not applicable | past time | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete | Not applicable | Not applicable | discrete | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | continuous |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | 1 | Not applicable | Not applicable | 1 | 1 | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Month | Not applicable | Year | Not applicable | Not applicable | Year | Year | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-321 ![]() |
EM-338 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 | EM-491 | EM-627 | EM-659 |
EM-660 ![]() |
EM-706 | EM-846 | EM-896 |
Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Geopolitical | Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Multiple unrelated locations (e.g., meta-analysis) | Geopolitical | Other | Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Not applicable | Watershed/Catchment/HUC | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Not applicable | Physiographic or ecological | Not applicable |
Spatial Extent Name
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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 | Bilbao Metropolitan Greenbelt | St. Croix, U.S. Virgin Islands | Tampa Bay Estuary | Contiguous U.S. | Portel municipality | Agricultural landscape, Yolo County, Central Valley | Massachusetts Ocean | Guanica Bay watershed | Coastal zone surrounding St. Croix | Not applicable | Yangjuangou catchment | conterminous United States | Upper Mississippi River and Ohio River basins | Lake Rotorua catchment | HUCS in Michigan | Not applicable | Piedmont Ecoregion | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 10-100 km^2 | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. | 100-1000 km^2 | Not applicable | 1-10 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 100,000-1,000,000 km^2 | Not applicable | 100,000-1,000,000 km^2 | Not applicable |
EM ID
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-321 ![]() |
EM-338 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 | EM-491 | EM-627 | EM-659 |
EM-660 ![]() |
EM-706 | EM-846 | 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) | 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 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 lumped (in all 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 | NHDplus v1 | 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 | 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 | Not applicable | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | Not applicable | 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 | NHDplus v1 | 1 km^2 | 1 km x 1 km | 2 m x 2 m | Not applicable | 1 ha | Not applicable | 250 m x 250 m | 30 m x 30 m | 1 km x1 km | 30 m x 30 m | 10 m x 10 m | Not applicable | 30m x 30m | irregular | 1 km2 | 5m x 5m | reach in HUC | not reported | Not applicable | 1m |
EM ID
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-321 ![]() |
EM-338 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 | EM-491 | EM-627 | EM-659 |
EM-660 ![]() |
EM-706 | EM-846 | EM-896 |
EM Computational Approach
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Analytic | Analytic | Numeric | Numeric | Logic- or rule-based | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Numeric | Numeric | Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-321 ![]() |
EM-338 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 | EM-491 | EM-627 | EM-659 |
EM-660 ![]() |
EM-706 | EM-846 | EM-896 |
Model Calibration Reported?
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No | No | Yes | No | No | No | Yes | Yes | Yes | No | Unclear | No | No | Yes | Not applicable | Yes | No | No | No | No | Not applicable | Yes | No |
Model Goodness of Fit Reported?
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No | No | Yes | No | No | No | Yes | No | Yes | No | No | No | No | No | Not applicable |
Yes ?Comment:For the year 2006 and 2011 |
No | No | No | Yes | Not applicable | No | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None |
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None | None | None |
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None |
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None | None | None | None | None | None |
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None | None | None |
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None | None | None |
Model Operational Validation Reported?
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No | No | No | Yes | No | Yes | No | No | No | No |
Yes ?Comment:Performed just for "Total pollinator abundance service score". |
No | No | Yes | Not applicable | No | No |
No ?Comment:However, agreement of submodel and intermediate components; annual discharge (R2=0.79), and nitrate-N load (R2=0.74), based on GIS land use were determined in comparison with USGS NASQAN data. |
No | No | No | No | Not applicable |
Model Uncertainty Analysis Reported?
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No | No | No | Yes | No | No | Yes | No | Yes | No | No | No | No | No | Not applicable | No | No | No | No | No | Not applicable | No | Not applicable |
Model Sensitivity Analysis Reported?
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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 | Yes | No | No | No | No | No | Not applicable | No | No | No | No | No | Not applicable | Yes | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Yes | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-321 ![]() |
EM-338 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 | EM-491 | EM-627 | EM-659 |
EM-660 ![]() |
EM-706 | EM-846 | EM-896 |
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None | None | None |
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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-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-321 ![]() |
EM-338 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 | EM-491 | EM-627 | EM-659 |
EM-660 ![]() |
EM-706 | EM-846 | EM-896 |
None | None | None | None | None | None |
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None | None | None |
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None | None | None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-321 ![]() |
EM-338 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 | EM-491 | EM-627 | EM-659 |
EM-660 ![]() |
EM-706 | EM-846 | EM-896 |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | 45.05 | 42.5 | 38.69 | 50.53 | 43.25 | 17.75 | 27.95 | -9999 | 38.3 | 38.7 | 41.72 | 17.96 | 17.73 | Not applicable | 36.7 | 39.5 | 40.6 | -38.14 | 45.12 | Not applicable | 36.23 | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
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6.4 | 6.4 | -90.63 | -89.1 | 7.6 | -2.92 | -64.75 | -82.47 | -9999 | -7.7 | -121.8 | -69.87 | -67.02 | -64.77 | Not applicable | 109.52 | -98.35 | -88.4 | 176.25 | 85.18 | Not applicable | -81.9 | Not applicable |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | NAD83 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Provided | Estimated | Provided | Estimated | Provided | Estimated | Estimated | Not applicable | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Provided | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Not applicable |
EM ID
em.detail.idHelp
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-321 ![]() |
EM-338 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 | EM-491 | EM-627 | EM-659 |
EM-660 ![]() |
EM-706 | EM-846 | EM-896 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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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) | 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 | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Scrubland/Shrubland | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Near Coastal Marine and Estuarine | Inland Wetlands | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Forests | Grasslands | Scrubland/Shrubland | Tundra | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Agroecosystems | Aquatic Environment (sub-classes not fully specified) | Ground Water | Forests | Agroecosystems | Scrubland/Shrubland | Rivers and Streams | Inland Wetlands | Grasslands | 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. | None | Row crop agriculture in Kaskaskia river basin | Not applicable | none | stony coral reef | Subtropical Estuary | Wetlands (multiple types) | Silvo-pastoral system | Cropland and surrounding landscape | None identified | Thirteen land use land cover classes were used | Coral reefs | Not applicable | Loess plain | Terrestrial | Agroecosystems and associated drainage and wetlands | Largely agricultural, commercial forestry, non-commercial forest and shrubland and urban | stream reaches | Wetlands | grasslands | Near coastal marine and estuarine |
EM Ecological Scale
em.detail.ecoScaleHelp
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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 coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-321 ![]() |
EM-338 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 | EM-491 | EM-627 | EM-659 |
EM-660 ![]() |
EM-706 | EM-846 | EM-896 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Community | Not applicable | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Species | Species | Not applicable | Guild or Assemblage | Species | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Not applicable | Species | Species |
Taxonomic level and name of organisms or groups identified
EM-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-321 ![]() |
EM-338 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 | EM-491 | EM-627 | EM-659 |
EM-660 ![]() |
EM-706 | EM-846 | EM-896 |
None Available | None Available | None Available | None Available | None Available | None Available |
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None Available | None Available | None Available |
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None Available | None Available | None Available | None Available |
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None Available | None Available |
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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-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-321 ![]() |
EM-338 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 | EM-491 | EM-627 | EM-659 |
EM-660 ![]() |
EM-706 | EM-846 | 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-81 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-193 | EM-194 | EM-195 | EM-196 |
EM-321 ![]() |
EM-338 ![]() |
EM-376 | EM-424 | EM-444 | EM-466 | EM-469 | EM-491 | EM-627 | EM-659 |
EM-660 ![]() |
EM-706 | EM-846 | EM-896 |
None | None | None |
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None |
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None | None |
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None |