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-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-185 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-333 ![]() |
EM-376 | EM-452 |
EM-467 ![]() |
EM-469 | EM-647 |
EM-774 ![]() |
EM-784 ![]() |
EM-812 ![]() |
EM-846 |
EM-851 ![]() |
EM-896 |
EM Short Name
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Plant species diversity, 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 | InVEST - Water provision, Francoli River, Spain | Blue crabs and SAV, Chesapeake Bay, 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 | Evoland v3.5 (unbounded growth), Eugene, OR, USA | MIMES: For Massachusetts Ocean (v1.0) | Relative reef sand generation, St. Croix, USVI | Yasso07 v1.0.1, Switzerland | Yasso07 - SOC, Loess Plateau, China | EcoAIM v.1.0 APG, MD | Plant-pollinator networks at reclaimed mine, USA | Wildflower mix supporting bees, Florida, USA | Wildflower mix supporting bees, CA, USA | Indigo bunting abund, Piedmont region, USA | InVEST Coastal Vulnerability, New York, USA | Random wave transformation on vegetation fields |
EM Full Name
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Plant species diversity, 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 | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) v2.4.2 - Water provision, Francoli River, Spain | Blue crabs and submerged aquatic vegetation interaction, Chesapeake Bay, 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 | Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA | Multi-scale Integrated Model of Ecosystem Services (MIMES) for the Massachusetts Ocean (v1.0) | Relative sand generation (of reef), St. Croix, USVI | Yasso07 v1.0.1 forest litter decomposition, Switzerland | Yasso07 - Land Use Effects on Soil Organic Carbon Stocks in the Loess Plateau, China | EcoAIM v.1.0, Aberdeen Proving Ground, MD | Restoration of plant-pollinator networks at reclaimed strip mine, Ohio, USA | Wildflower planting mix supporting bees in agricultural landscapes, Florida, USA | Wildflower planting mix supporting bees in agricultural landscapes, CA, USA | Indigo bunting abundance, Piedmont ecoregion, USA | InVEST Coastal Vulnerability, Jamaica Bay, New York, 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 | InVEST | None |
None ?Comment:EU Mapping Studies |
US EPA | US EPA | US EPA | Envision | US EPA | US EPA | None | None | None | None | None | None | None | InVEST | None |
EM Source Document ID
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260 | 260 | 123 | 137 | 228 | 280 |
292 ?Comment:Conference paper |
191 | 96 | 186 | 63 |
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
316 | 335 | 343 | 344 | 374 | 397 | 400 | 400 | 405 |
410 ?Comment:Sharp R, Tallis H, Ricketts T, Guerry A, Wood S, Chaplin-Kramer R, et al. InVEST User?s Guide. User Guide. Stanford (CA): The Natural Capital Project, Stanford University, University of Minnesota, The Nature Conservancy, World Wildlife Fund; 2015. |
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. | Marques, M., Bangash, R.F., Kumar, V., Sharp, R., and Schuhmacher, M. | Mykoniatis, N. and Ready, R. | 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. | Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Altman, I., R.Boumans, J. Roman, L. Kaufman | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Didion, M., B. Frey, N. Rogiers, and E. Thurig | Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu | Booth, P., Law, S. , Ma, J. Turnley, J., and J.W. Boyd | Cusser, S. and K. Goodell | 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 | 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 | Riffel, S., Scognamillo, D., and L. W. Burger | Hopper T. and M. S. Meixler | Mendez, F. J. and I. J. Losada |
Document Year
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2011 | 2011 | 2013 | 2011 | 2012 | 2013 | 2013 | 2013 | 2011 | 2013 | 2011 | 2008 | 2012 | 2014 | 2014 | 2015 | 2014 | 2013 | 2015 | 2015 | 2008 | 2016 | 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 | The impact of climate change on water provision under a low flow regime: A case study of the ecosystems services in the Francoli river basin | Evaluating habitat-fishery interactions: The case of submerged aquatic vegetation and blue crab fishery in the Chesapeake Bay | 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 | Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | 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 | Validating tree litter decomposition in the Yasso07 carbon model | Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement | Implementation of EcoAIM - A Multi-Objective Decision Support Tool for Ecosystem Services at Department of Defense Installations | Diversity and distribution of floral resources influence the restoration of plant-pollinator networks on a reclaimed strip mine | Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States | Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Modeling coastal vulnerability through space and time | 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 | Not formally documented | 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 |
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 | Conference proceedings | 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 |
EM ID
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EM-70 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-185 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-333 ![]() |
EM-376 | EM-452 |
EM-467 ![]() |
EM-469 | EM-647 |
EM-774 ![]() |
EM-784 ![]() |
EM-812 ![]() |
EM-846 |
EM-851 ![]() |
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 | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://evoland.bioe.orst.edu/ | http://www.afordablefutures.com/orientation-to-what-we-do | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://naturalcapitalproject.stanford.edu/software/invest-models/coastal-vulnerability | Not applicable | |
Contact Name
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Sandra Lavorel | Sandra Lavorel | Liem Tran | Yongping Yuan | Marion Potschin | Montse Marquès | Nikolaos Mykoniatis | Izaskun Casado-Arzuaga | Leah Oliver | M. Russell | Steve Jordan | Michael R. Guzy | Irit Altman | Susan H. Yee |
Markus Didion ?Comment:Tel.: +41 44 7392 427 |
Xing Wu | Pieter Booth |
Sarah Cusser ?Comment:Department of Evolution, Ecology, and Organismal Biology, Ohio State University, 318 West 12th Avenue, Columbus, OH 43202, U.S.A. |
Neal Williams | Neal Williams | Sam Riffell | Thomas Hopper |
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 | Environmental Analysis and Management Group, Department d'Enginyeria Qimica, Universitat Rovira I Virgili, Tarragona, Catalonia, Spain | Department of Agricultural Economics, Sociology and Education The Pennsylvania State University | 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 | Oregon State University, Dept. of Biological and Ecological Engineering | Boston University, Portland, Maine | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Chinese Academy of Sciences, Beijing 100085, China | Exponent Inc., Bellevue WA | Department of Evolution, Ecology, and Behavior, School of Biological Sciences, The University of Texas at Austin, 100 East 24th Street Stop A6500, Austin, TX 78712-1598, U.S.A. | Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616 | Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616 | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Not reported | 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 | montserrat.marques@fundacio.urv.cat | Not reported | izaskun.casado@ehu.es | leah.oliver@epa.gov | Russell.Marc@epamail.epa.gov | steve.jordan@epa.gov | Not reported | iritaltman@bu.edu | yee.susan@epa.gov | markus.didion@wsl.ch | xingwu@rceesac.cn | pbooth@ramboll.com | sarah.cusser@gmail.com | nmwilliams@ucdavis.edu | nmwilliams@ucdavis.edu | sriffell@cfr.msstate.edu | Tjhop1123@gmail.com | mendezf@unican.es |
EM ID
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EM-70 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-185 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-333 ![]() |
EM-376 | EM-452 |
EM-467 ![]() |
EM-469 | EM-647 |
EM-774 ![]() |
EM-784 ![]() |
EM-812 ![]() |
EM-846 |
EM-851 ![]() |
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 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." | 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. AUTHOR'S DESCRIPTION: "InVEST 2.4.2 model runs as script tool in the ArcGIS 10 ArcTool-Box on a gridded map at an annual average time step, and its results can be reported in either biophysical or monetary terms, depending on the needs and the availability of information. It is most effectively used within a decision making process that starts with a series of stakeholder consultations to identify questions and services of interest to policy makers, communities, and various interest groups. These questions may concern current service delivery and how services may be affected by new programmes, policies, and conditions in the future. For questions regarding the future, stakeholders develop scenarios of management interventions or natural changes to explore the consequences of potential changes on natural resources [21]. This tool informs managers and policy makers about the impacts of alternative resource management choices on the economy, human well-being, and the environment, in an integrated way [22]. The spatial resolution of analyses is flexible, allowing users to address questions at the local, regional or global scales. | ABSTRACT: "This paper investigates habitat-fisheries interaction between two important resources in the Chesapeake Bay: blue crabs and Submerged Aquatic Vegetation (SAV). A habitat can be essential to a species (the species is driven to extinction without it), facultative (more habitat means more of the species, but species can exist at some level without any of the habitat) or irrelevant (more habitat is not associated with more of the species). An empirical bioeconomic model that nests the essential-habitat model into its facultative-habitat counterpart is estimated. Two alternative approaches are used to test whether SAV matters for the crab stock. Our results indicate that, if we do not have perfect information on habitat-fisheries linkages, the right approach would be to run the more general facultative-habitat model instead of the essential- habitat one." | 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." | **Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | 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...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…Synthesis of scientific literature and expert opinion can be used to estimate the relative potential for recreational opportunities across different benthic habitat types (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to recreational opportunities as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative recreational opportunity j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A.palmata, Montastraea reef, patch reef, and dense or sparse gorgonians), and Mij is the magnitude associated with each habitat for a given metric j: sand generation" | ABSTRACT: "...We examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to (i) examine the effect of the application of three different published Yasso07 parameter sets on simulated decay rate; (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests;…" AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root litter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The result of the simulation was an annual estimate of the remaining fraction of the initial mass, which could then be compared with observed data." | 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: "This report describes the demonstration of the EcoAIM decision support framework and GIS-based tool. EcoAIM identifies and quantifies the ecosystem services provided by the natural resources at the Aberdeen Proving Ground (APG). A structured stakeholder process determined the mission and non-mission priorities at the site, elicited the natural resource management decision process, identified the stakeholders and their roles, and determine the ecosystem services of priority that impact missions and vice versa. The EcoAIM tool was customized to quantify in a geospatial context, five ecosystem services – vista aesthetics, landscape aesthetics, recreational opportunities, habitat provisioning for biodiversity and nutrient sequestration. The demonstration included a Baseline conditions quantification of ecosystem services and the effects of a land use change in the Enhanced Use Lease parcel in cantonment area (Scenario 1). Biodiversity results ranged widely and average scores decreased by 10% after Scenario 1. Landscape aesthetics scores increased by 10% after Scenario 1. Final scores did not change for recreation or nutrient sequestration because scores were outside the boundaries of the baseline condition. User feedback after the demonstration indicated positive reviews of EcoAIM as being useful and usable for land use decisions and particularly for use as a communication tool. " | ABSTRACT: "Plant–pollinator mutualisms are one of the several functional relationships that must be reinstated to ensure the long-term success of habitat restoration projects. These mutualisms are unlikely to reinstate themselves until all of the resource requirements of pollinators have been met. By meeting these requirements, projects can improve their long-term success. We hypothesized that pollinator assemblage and structure and stability of plant–pollinator networks depend both on aspects of the surrounding landscape and of the restoration effort itself. We predicted that pollinator species diversity and network stability would be negatively associated with distance from remnant habitat, but that local floral diversity might rescue pollinator diversity and network stability in locations distant from the remnant. We created plots of native prairie on a reclaimed strip mine in central Ohio, U.S.A. that ranged in floral diversity and isolation from the remnant habitat. We found that the pollinator diversity declined with distance from the remnant habitat. Furthermore, reduced pollinator diversity in low floral diversity plots far from the remnant habitat was associated with loss of network stability. High floral diversity, however, compensated for losses in pollinator diversity in plots far from the remnant habitat through the attraction of generalist pollinators. Generalist pollinators increased network connectance and plant-niche overlap. Asa result, network robustness of high floral diversity plots was independent of isolation. We conclude that the aspects of the restoration effort itself, such as floral community composition, can be successfully tailored to incorporate the restoration of pollinators and improve success given a particular landscape context." | 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: " 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:"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: "Coastal ecosystems experience a wide range of stressors including wave forces, storm surge, sea-level rise, and anthropogenic modification and are thus vulnerable to erosion. Urban coastal ecosystems are especially important due to the large populations these limited ecosystems serve. However, few studies have addressed the issue of urban coastal vulnerability at the landscape scale with spatial data that are finely resolved. The purpose of this study was to model and map coastal vulnerability and the role of natural habitats in reducing vulnerability in Jamaica Bay, New York, in terms of nine coastal vulnerability metrics (relief, wave exposure, geomorphology, natural habitats, exposure, exposure with no habitat, habitat role, erodible shoreline, and surge) under past (1609), current (2015), and future (2080) scenarios using InVEST 3.2.0. We analyzed vulnerability results both spatially and across all time periods, by stakeholder (ownership) and by distance to damage from Hurricane Sandy. We found significant differences in vulnerability metrics between past, current and future scenarios for all nine metrics except relief and wave exposure…" | 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 | None identified | Not applicable | Land management, ecosystem management, response to EU 2020 Biodiversity Strategy | Not applicable | Restoration of seagrass | None identified | Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None identified | None identified | None identified | None | None reported | None identified | None identrified | None identified | None reported | None identified | None identified |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, predominantly on 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 | Mediteranean coastal mountains | Submerged Aquatic Vegetation (SAV), eelgrass | 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 | No additional description provided | No additional description provided | No additional description provided | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | Agricultural plain, hills, gulleys, forest, grassland, Central China | Chesapeake bay coastal plain, elev. 60ft. | The site was surface mined for coal until the mid-1980s and soon after recontoured and seeded with a low diversity of non-native grasses and forbes. The property is grassland in a state of arrested succession, unable to support tree growth because of shallow, infertile soils. | field plots near agricultural fields (mixed row crop, almond, walnuts), central valley, Ca | field plots near agricultural fields (mixed row crop, almond, walnuts), central valley, Ca | Conservation Reserve Program lands left to go fallow | Jamaica Bay, New York, situated on the southern shore of Long Island, and characterized by extensive coastal ecosystems in the central bay juxtaposed with a largely urbanized shoreline containing fragmented and fringing coastal habitat. | 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) | IPPC scenarios A2- severe changes in temperature and precipitation, B1 - more moderate variations in temperature and precipitation schemes from the present | Essential or Facultative habitat | No scenarios presented | Not applicable | Habitat loss or restoration in Tampa Bay Estuary | No scenarios presented | Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves. | No scenarios presented | No scenarios presented |
No scenarios presented ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
Land use change | N/A | No scenarios presented | Varied wildflower planting mixes of annuals and perennials | Varied wildflower planting mixes of annuals and perennials | N/A | Past (1609), current (2015), and future (2080) scenarios | No scenarios presented |
EM ID
em.detail.idHelp
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EM-70 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-185 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-333 ![]() |
EM-376 | EM-452 |
EM-467 ![]() |
EM-469 | EM-647 |
EM-774 ![]() |
EM-784 ![]() |
EM-812 ![]() |
EM-846 |
EM-851 ![]() |
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 (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only |
New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
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EM-70 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-185 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-333 ![]() |
EM-376 | EM-452 |
EM-467 ![]() |
EM-469 | EM-647 |
EM-774 ![]() |
EM-784 ![]() |
EM-812 ![]() |
EM-846 |
EM-851 ![]() |
EM-896 |
EM Temporal Extent
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2007-2009 | Not reported | 1987-1997 | 1980-2006 | 1990-2030 | 1971-2100 | 1993-2011 | 2000 - 2007 | 2006-2007 | 1982-2010 | 2004 | 1990-2050 | Not applicable | 2006-2007, 2010 | 1993-2013 | 1969-2011 | 2014 | 2009-2010 | 2011-2012 | 2011-2012 | 2008 | 1609-2080 | 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-dependent | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | 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 | past time | Not applicable | Not applicable | Not applicable | Not applicable | future time | future time | Not applicable | future time | past time | Not applicable | Not applicable | past time | past time | 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 | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | discrete | discrete | Not applicable | Not applicable | discrete | discrete | 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 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 2 | 1 | Not applicable | 1 | 1 | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Year | Year | Not applicable | Year | Year | Not applicable | Not applicable | Year | Year | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-70 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-185 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-333 ![]() |
EM-376 | EM-452 |
EM-467 ![]() |
EM-469 | EM-647 |
EM-774 ![]() |
EM-784 ![]() |
EM-812 ![]() |
EM-846 |
EM-851 ![]() |
EM-896 |
Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Multiple unrelated locations (e.g., meta-analysis) | Geopolitical | Physiographic or ecological | Physiographic or ecological | Geopolitical | Watershed/Catchment/HUC | Geopolitical | 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 |
Point or points ?Comment:This is a guess based on information in the document. 3 field sites were separated by up to 9km |
Physiographic or ecological | 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 | Francoli River | Chesapeake Bay | Bilbao Metropolitan Greenbelt | St. Croix, U.S. Virgin Islands | Tampa Bay Estuary | Contiguous U.S. | Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Massachusetts Ocean | Coastal zone surrounding St. Croix | Switzerland | Yangjuangou catchment | Aberdeen Proving Ground | The Wilds | Agricultural plots | Agricultural plots | Piedmont Ecoregion | Jamaica Bay, Long Island, New York | 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 | 10,000-100,000 km^2 | 100-1000 km^2 | 10-100 km^2 | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | 10-100 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 10,000-100,000 km^2 | 1-10 km^2 | 100-1000 km^2 | 1-10 km^2 | 10-100 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | 10-100 km^2 | Not applicable |
EM ID
em.detail.idHelp
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EM-70 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-185 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-333 ![]() |
EM-376 | EM-452 |
EM-467 ![]() |
EM-469 | EM-647 |
EM-774 ![]() |
EM-784 ![]() |
EM-812 ![]() |
EM-846 |
EM-851 ![]() |
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 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) ?Comment:500m x 500m is also used for some computations. The evaluation does include some riparian buffers which are linear features along streams. |
spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) |
spatially distributed (in at least some cases) ?Comment:by coastal segment |
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 | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable | 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 | NHDplus v1 | 1 km^2 | 1 km x 1 km | 30m x 30m | Not applicable | 2 m x 2 m | Not applicable | 1 ha | Not applicable | varies | 1 km x1 km | 10 m x 10 m | 5 sites | 30m x 30m | 100m x 100m | 10 m radius | Not applicable | Not applicable | Not applicable | 80 m | 1m |
EM ID
em.detail.idHelp
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EM-70 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-185 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-333 ![]() |
EM-376 | EM-452 |
EM-467 ![]() |
EM-469 | EM-647 |
EM-774 ![]() |
EM-784 ![]() |
EM-812 ![]() |
EM-846 |
EM-851 ![]() |
EM-896 |
EM Computational Approach
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Analytic | Analytic | Numeric | Numeric | Logic- or rule-based | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Numeric | Numeric | Numeric | Analytic | Numeric | Numeric | Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | 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-70 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-185 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-333 ![]() |
EM-376 | EM-452 |
EM-467 ![]() |
EM-469 | EM-647 |
EM-774 ![]() |
EM-784 ![]() |
EM-812 ![]() |
EM-846 |
EM-851 ![]() |
EM-896 |
Model Calibration Reported?
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No | No | Yes | No | No | No | Yes | No | Yes | Yes | Yes | Unclear | No | Yes | No | Yes |
No ?Comment:Nutrient sequestion submodel ( EPA's P8 model has been long used) |
Not applicable | No | No | Yes | No | No |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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Yes | No | Yes | No | No | No | Yes | No | Yes | No | Yes | No | No | No | No |
Yes ?Comment:For the year 2006 and 2011 |
Not applicable | Not applicable | No | No | No | No | Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None |
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None | None | None | None | None |
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None |
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None | None | None | None |
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None | None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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No | No | No | Yes | No |
Yes ?Comment:Used Nash-Sutcliffe model efficiency index |
Yes | Yes | No | No | No | No | No | Yes | Yes | No | No | Yes | No | No | No | No | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | Yes | No | No | Yes | No | Yes | No | Yes | No | No | No | No | No | No | Yes | No | No | No | No | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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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 | Yes | No | No | No | Yes | No | No | No | No | No |
Unclear ?Comment:Just cannot tell, but no mention of sensitivity was made. |
No | No | No | 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 | Yes | 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 |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-70 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-185 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-333 ![]() |
EM-376 | EM-452 |
EM-467 ![]() |
EM-469 | EM-647 |
EM-774 ![]() |
EM-784 ![]() |
EM-812 ![]() |
EM-846 |
EM-851 ![]() |
EM-896 |
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None | None | None |
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None | None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-70 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-185 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-333 ![]() |
EM-376 | EM-452 |
EM-467 ![]() |
EM-469 | EM-647 |
EM-774 ![]() |
EM-784 ![]() |
EM-812 ![]() |
EM-846 |
EM-851 ![]() |
EM-896 |
None | None | None | None | None | None |
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None |
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None | None |
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None | None | None | None | None | None | None |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-70 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-185 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-333 ![]() |
EM-376 | EM-452 |
EM-467 ![]() |
EM-469 | EM-647 |
EM-774 ![]() |
EM-784 ![]() |
EM-812 ![]() |
EM-846 |
EM-851 ![]() |
EM-896 |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | 45.05 | 42.5 | 38.69 | 50.53 | 41.26 | 36.99 | 43.25 | 17.75 | 27.95 | -9999 | 44.11 | 41.72 | 17.73 | 46.82 | 36.7 | 39.46 | 39.82 | 29.4 | 29.4 | 36.23 | 40.61 | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
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6.4 | 6.4 | -90.63 | -89.1 | 7.6 | 1.18 | -75.95 | -2.92 | -64.75 | -82.47 | -9999 | -123.09 | -69.87 | -64.77 | 8.23 | 109.52 | 76.12 | -81.75 | -82.18 | -82.18 | -81.9 | -73.84 | 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 | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Provided | Estimated | Provided | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Not applicable | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Provided | Provided | Provided | Estimated | Provided | Not applicable |
EM ID
em.detail.idHelp
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EM-70 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-185 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-333 ![]() |
EM-376 | EM-452 |
EM-467 ![]() |
EM-469 | EM-647 |
EM-774 ![]() |
EM-784 ![]() |
EM-812 ![]() |
EM-846 |
EM-851 ![]() |
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) | Rivers and Streams | None | 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 | Agroecosystems | Created Greenspace | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Forests | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Forests | Created Greenspace | Grasslands | Scrubland/Shrubland | Grasslands | Agroecosystems | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | 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 | Coastal mountains | Yes | none | stony coral reef | Subtropical Estuary | Wetlands (multiple types) | Agricultural-urban interface at river junction | None identified | Coral reefs | forests | Loess plain | Coastal Plain | Grassland | Agricultural landscape | Agricultural landscape | grasslands | Coastal | 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 | Ecosystem | 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 | Yes | 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 | 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-70 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-185 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-333 ![]() |
EM-376 | EM-452 |
EM-467 ![]() |
EM-469 | EM-647 |
EM-774 ![]() |
EM-784 ![]() |
EM-812 ![]() |
EM-846 |
EM-851 ![]() |
EM-896 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Community | Not applicable | Not applicable | Not applicable | Not applicable | Yes | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Species | Guild or Assemblage | Community | Not applicable | Not applicable | Species | Species | Species | Species | Not applicable | Species |
Taxonomic level and name of organisms or groups identified
EM-70 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-185 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-333 ![]() |
EM-376 | EM-452 |
EM-467 ![]() |
EM-469 | EM-647 |
EM-774 ![]() |
EM-784 ![]() |
EM-812 ![]() |
EM-846 |
EM-851 ![]() |
EM-896 |
None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available |
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None Available | None Available |
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None Available | None Available | None Available | 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-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-185 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-333 ![]() |
EM-376 | EM-452 |
EM-467 ![]() |
EM-469 | EM-647 |
EM-774 ![]() |
EM-784 ![]() |
EM-812 ![]() |
EM-846 |
EM-851 ![]() |
EM-896 |
None |
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None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-70 | EM-82 | EM-91 | EM-97 |
EM-125 ![]() |
EM-148 ![]() |
EM-185 | EM-193 | EM-194 | EM-195 | EM-196 |
EM-333 ![]() |
EM-376 | EM-452 |
EM-467 ![]() |
EM-469 | EM-647 |
EM-774 ![]() |
EM-784 ![]() |
EM-812 ![]() |
EM-846 |
EM-851 ![]() |
EM-896 |
None | None | None |
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None |
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None | None | None |
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None |
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None | None |