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-94 | EM-97 | EM-123 | EM-418 | EM-457 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 |
EM-697 ![]() |
EM-729 ![]() |
EM-784 ![]() |
EM-820 | EM-944 | EM-945 |
EM Short Name
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Reduction in pesticide runoff risk, Europe | AnnAGNPS, Kaskaskia River watershed, IL, USA | Land-use change and wildlife products, Europe | SIRHI, St. Croix, USVI | Visitation to reef dive sites, St. Croix, USVI | Value of finfish, St. Croix, USVI | InVEST fisheries, lobster, South Africa | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | RBI Spatial Analysis Method | Floral resources on landfill sites, United Kingdom | WESP: Urban Stormwater Treatment, ID, USA | Wildflower mix supporting bees, Florida, USA | MMI method for aquatic surveys | COBRA v 4.1 | Air pollution removal by green roofs, Chicago, USA |
EM Full Name
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Reduction in pesticide runoff risk, Europe | AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | Land-use change effects on wildlife products, Europe | SIRHI (SImplified Reef Health Index), St. Croix, USVI | Visitation to dive sites (reef), St. Croix, USVI | Relative value of finfish (on reef), St. Croix, USVI | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa | DeNitrification-DeComposition simulation of N2O flux Ireland | Rapid Benefit Indicator (RBI) Spatial Analysis Toolset Method | Floral resources on landfill sites, East Midlands, United Kingdom | WESP: Urban Stormwater Treament, ID, USA | Wildflower planting mix supporting bees in agricultural landscapes, Florida, USA | Multimetric Indice (MMI) method for large scale aquatic surveys | COBRA (CO–Benefits Risk Assessment) v 4.1 | Air pollution removal by green roofs, Chigago, USA |
EM Source or Collection
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None | US EPA | EU Biodiversity Action 5 | US EPA | US EPA | US EPA | InVEST | None | None | None | None | None | US EPA | US EPA | None |
EM Source Document ID
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255 | 137 | 228 | 335 | 335 | 335 |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
358 | 367 | 389 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
400 | 403 |
437 ?Comment:User's manual is provided at the webpage. |
438 ?Comment:Document 439 is an additional source for this EM. |
Document Author
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Lautenbach, S., Maes, J., Kattwinkel, M., Seppelt, R., Strauch, M., Scholz, M., Schulz-Zunkel, C., Volk, M., Weinert, J. and Dormann, 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. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Bousquin, J., Mazzotta M., and W. Berry | Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin | Murphy, C. and T. Weekley | 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 | Stoddard, J.L., Herlihy, A.T., Peck, D.V., Hughes, R.M., Whittier, T.R., and E. Tarquinio | US EPA | Yang, J., Q. Yu and P. Gong |
Document Year
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2012 | 2011 | 2012 | 2014 | 2014 | 2014 | 2018 | 2010 | 2017 | 2013 | 2012 | 2015 | 2008 | 2021 | 2008 |
Document Title
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Mapping water quality-related ecosystem services: concepts and applications for nitrogen retention and pesticide risk reduction | 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 | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals | Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture | Rapid Benefit Indicators (RBI) Spatial Analysis Toolset - Manual. | Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States | A process for creating multimetric indices for large-scale A process for creating multimetic indices for large-scale aquatic surveys | CO-Benefits Risk Assessment Health Impacts Screening and Mapping Tool (COBRA) | Quantifying air pollution removal by green roofs in Chicago |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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 EPA report | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Webpage | Published journal manuscript |
EM ID
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EM-94 | EM-97 | EM-123 | EM-418 | EM-457 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 |
EM-697 ![]() |
EM-729 ![]() |
EM-784 ![]() |
EM-820 | EM-944 | EM-945 |
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 | https://www.naturalcapitalproject.org/invest/ | http://www.dndc.sr.unh.edu | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.epa.gov/cobra | Not applicable | |
Contact Name
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Sven Lautenbach | Yongping Yuan | Marion Potschin | Susan H. Yee | Susan H. Yee | Susan H. Yee | Michelle Ward | M. Abdalla | Justin Bousquin | Sam Tarrant | Chris Murphy | Neal Williams | John Stoddard | Emma Zinsmeister | Jun Yang |
Contact Address
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Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany | 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 | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | US EPA, Office of Research and Development, National health and environmental Effects Lab, Gulf Ecology Division, Gulf Breeze, FL 32561 | RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K. | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616 | 200 SW 35th St., Corvallis, OR 97333 | EPA’s Office of Atmospheric Programs’ Climate Protection Partnerships Division | Department of Landscape Architecture and Horticulture, Temple University, 580 Meetinghouse Road, Ambler, PA 19002, USA. |
Contact Email
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sven.lautenbach@ufz.de | yuan.yongping@epa.gov | marion.potschin@nottingham.ac.uk | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | m.ward@uq.edu.au | abdallm@tcd.ie | bousquin.justin@epa.gov | sam.tarrant@rspb.org.uk | chris.murphy@idfg.idaho.gov | nmwilliams@ucdavis.edu | stoddard.john@epa.gov | zinsmeister.emma@epa.gov | juny@temple.edu |
EM ID
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EM-94 | EM-97 | EM-123 | EM-418 | EM-457 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 |
EM-697 ![]() |
EM-729 ![]() |
EM-784 ![]() |
EM-820 | EM-944 | EM-945 |
Summary Description
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AUTHOR'S DESCRIPTION: "We used a spatially explicit model to predict the potential exposure of small streams to insecticides (run-off potential – RP) as well as the resulting ecological risk (ER) for freshwater fauna on the European scale (Schriever and Liess 2007; Kattwinkel et al. 2011)...The recovery of community structure after exposure to insecticides is facilitated by the presence of undisturbed upstream stretches that can act as sources for recolonization (Niemi et al. 1990; Hatakeyama and Yokoyama 1997). In the absence of such sources for recolonization, the structure of the aquatic community at sites that are exposed to insecticides differs significantly from that of reference sites (Liess and von der Ohe 2005)...Hence, we calculated the ER depending on RP for insecticides and the amount of recolonization zones. ER gives the percentage of stream sites in each grid cell (10 × 10 km) in which the composition of the aquatic community deviated from that of good ecological status according to the WFD. In a second step, we estimated the service provided by the environment comparing the ER of a landscape lacking completely recolonization sources with that of the actual landscape configuration. Hence, the ES provided by non-arable areas (forests, pastures, natural grasslands, moors and heathlands) was calculated as the reduction of ER for sensitive species. The service can be thought of as a habitat provisioning/nursery service that leads to an improvement of ecological water quality." | 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…are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Wildlife products); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000." AUTHOR'S DESCRIPTION: "Wildlife products belongs to the service group Biotic Materials in the CICES system; it includes the provisioning of all non-edible raw material products that are gained through non-agricultural practices or which are produced as a by-product of commercial and non-commercial forests, primarily in non-intensively used land or semi-natural and natural areas….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers." | ABSTRACT: "...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). The Simplified Integrated Reef Health Index (SIRHI) combines four attributes of reef condition into a single index: SIRHI = ΣiGi where Gi are the grades on a scale of 1 to 5 for four key reef attributes: percent coral cover, percent macroalgal cover, herbivorous fish biomass, and commercial fish biomass (Table2; Healthy Reefs Initiative, 2010). 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 to predict reef condition in non-sampled locations (Fig. 1). In general, we followed the methods of Pittman et al. (2007) which generated predictive models for fish richness using readily available benthic habitat maps and bathymetry data. Because these models rely on readily available data (benthic habitat maps and bathymetry data), the models have the potential for high transferability to other locati | 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)…Pendleton (1994) used field observations of dive sites to model potential impacts on local economies due to loss of dive tourism with reef degradation. A key part of the diver choice model is a fitted model of visitation to dive sites described by Visitation to dive sites = 2.897+0.0701creef -0.133D+0.0417τ where creef is percent coral cover, D is the time in hours to the dive site, which we estimate using distance from reef to shore and assuming a boat speed of 5 knots or 2.57ms-1, and τ is a dummy variable for the presence of interesting topographic features. We interpret τ as dramatic changes in bathymetry, quantified as having a standard deviation in depth among grid cells within 30 m that is greater than the75th percentile across all grid cells. Because our interpretation of topography differed from the original usage of “interesting features”, we also calculated dive site visitation assuming no contribution of topography (τ=0). Unsightly coastal development, an additional but non-significant variable in the original model, was assumed to be zero for St. Croix." | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(5) value of finfish," | AUTHOR'S DESCRIPTION: "Here we develop a method for assessing future scenarios of environmental management change that improve coastal ecosystem services and thereby, support the success of the SDGs. We illustrate application of the method using a case study of South Africa’s West Coast Rock Lobster fishery within the Table Mountain National Park (TMNP) Marine Protected Area...We calculated the retrospective and current value of the West Coast Rock Lobster fishery using published and unpublished data from various sources and combined the market worth of landed lobster from recreational fishers, small-scale fisheries (SSF), large-scale fisheries (LSF) and poachers. Then using the InVEST tool, we combined data to build scenarios that describe possible futures for the West Coast Rock Lobster fishery (see Table 1). The first scenario, entitled ‘Business as Usual’ (BAU), takes the current situation and most up-to-date data to model the future if harvest continues at the existing rate. The second scenario is entitled ‘Redirect the Poachers’ (RP), which attempts to model implementation of strict management, whereby poaching is minimised from the Marine Protected Area and other economic and nutritional sources are made available through government initiatives. The third scenario, entitled ‘Large Scale Cutbacks’ (LSC), excludes large-scale fisheries from harvesting West Coast Rock Lobster within the TMNP Marine Protected Area." | Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. | AUTHOR DESCRIPTION: "The Rapid Benefits Indicators (RBI) approach consists of five steps and is outlined in Assessing the Benefits of Wetland Restoration – A Rapid Benefits Indicators Approach for Decision Makers, hereafter referred to as the “guide.” The guide presents the assessment approach, detailing each step of the indicator development process and providing an example application in the “Step in Action” pages. The spatial analysis toolset is intended to be used to analyze existing spatial information to produce metrics for many of the indicators developed in that guide. This spatial analysis toolset manual gives directions on the mechanics of the tool and its data requirements, but does not detail the reasoning behind the indicators and how to use results of the assessment; this information is found in the guide. " | ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level… A “floral cover” method to represent available floral resources was used which combines floral abundance with inflorescence size. Mean area of the floral unit from above was measured for each flowering plant species and then multiplied by their frequencies." "Insect pollinated flowering plant species composition and floral abundance between sites by type were represented by non-metric multidimensional scaling (NMDS)...This method is sensitive to showing outliers and the distance between points shows the relative similarity (McCune & Grace 2002; Ollerton et al. 2009)." (This data is not entered into ESML) | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | Abstract: " 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: "Differences in sampling and laboratory protocols, differences in techniques used to evaluate metrics, and differing scales of calibration and application prohibit the use of many existing multimetric indices (MMIs) in large-scale bioassessments. We describe an approach to developing MMIs of ecological condition that is applicable to a variety of biological assemblage types and to spatially extensive (regional, national) aquatic resource surveys. The process involves testing the performance characteristics of candidate metrics in several categories that correspond to key dimensions of biotic condition. The performance characteristics include: information content (range), reproducibility, calibration for natural gradients, responsiveness to stressor gradients, and independence from other metrics. The best-performing metric from each category is included in the final MMI. The consistency of the process enables development of separate MMIs in different regions that can be combined in a national assessment and that are more comparable across regions and taxonomic groups than a set of independently developed MMIs would be. Range: Generally eliminate metrics if their range is <4 or if > 1/3 of samples have values = 0. Very few macroinvertebrate metrics are eliminated by this test. It does eliminate a large number of potentially poor metrics for assemblages with fewer taxa (e.g., fish). Reproducibility: We quantify metric reproducibility with a variant of the signal:noise ratio (S/N). S/N is the ratio of the variance among all sites (signal) to the variance of repeated visits to the same site (noise). S/N values 1 indicate that visiting a single site twice yields as much metric variability as visiting 2 different sites. Natural gradient calibration: Focusing solely on reference-site data and to quantify the remaining correspondence between the metric value and the natural gradient. Responsiveness: The ability of a metric to distinguish least-disturbed (reference) from most-disturbed sites. We identify the metrics that have the highest responsiveness (t-scores) within each class and aggregated ecoregion. Redundancy: We often consider metrics as too strongly correlated when their Pearson correlation coefficients at least-disturbed sites are > |0.71| (R2 = 0.5). | Introduction: "COBRA is a screening tool that provides preliminary estimates of the impact of air pollution emission changes on ambient particulate matter (PM) air pollution concentrations, translates this into health effect impacts, and then monetizes these impacts, as illustrated below. The model does not require expertise in air quality modeling, health effects assessment, or economic valuation. Built into COBRA are emissions inventories, a simplified air quality model, health impact equations, and economic valuations ready for use, based on assumptions that EPA currently uses as reasonable best estimates. COBRA also enables advanced users to import their own datasets of emissions inventories, population, incidence, health impact functions, and valuation functions. Analyses can be performed at the state or county level and across the 14 major emissions categories (these categories are called “tiers”) included in the National Emissions Inventory. COBRA presents results in tabular as well as geographic form, and enables policy analysts to obtain a first-order approximation of the benefits of different mitigation scenarios under consideration. However, COBRA is only a screening tool. More sophisticated, albeit time- and resource-intensive, modeling approaches are currently available to obtain a more refined picture of the health and economic impacts of changes in emissions. EPA initially developed COBRA as a desktop application. In 2021, EPA released a web-based version of the tool, known as the COBRA Web Edition. Although the desktop version and web versions of COBRA both use the same methodology to calculate outdoor air quality and health impacts from changes in air pollution emissions, the desktop version offers additional advanced features that are not included in the more streamlined Web Edition. In particular, the desktop version is preloaded with input data on emissions, population, and baseline health incidence for 2016, 2023, and 2028; the Web Edition includes data only for 2023. Similarly, the desktop version allows users to import custom input datasets, while the Web Edition does not. The Web Edition, however, does not require the user to download or install additional software, and it runs more quickly than the desktop version. Users might choose to use the desktop version if they would like to use advanced features, such as custom input data and/or use the preloaded data for 2016 or 2028. Otherwise, users may choose to use the Web Edition for data analysis relevant to 2023. The process for entering emissions input data into COBRA is very similar for the desktop and web versions of the tool. The remainder of this User’s Manual focuses on the steps required to run the desktop version of the tool. The same general process can be used with the Web Edition." | ABSTRACT: "The level of air pollution removal by green roofs in Chicago was quantified using a dry deposition model. The result showed that a total of 1675 kg of air pollutants was removed by 19.8 ha of green roofs in one year with O3 accounting for 52% of the total, NO2 (27%), PM10 (14%), and SO2 (7%). The highest level of air pollution removal occurred in May and the lowest in February. The annual removal per hectare of green roof was 85 kg/ha/yr. The amount of pollutants removed would increase to 2046.89 metric tons if all rooftops in Chicago were covered with intensive green roofs. Although costly, the installation of green roofs could be justified in the long run if the environmental benefits were considered. The green roof can be used to supplement the use of urban trees in air pollution control, especially in situations where land and public funds are not readily available." |
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
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European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | Not reported | None identified | None identified | None identified | None identified | Future rock lobster fisheries management | climate change | None identified | None identified | None identified | None identrified | None identified | None identified | None identified |
Biophysical Context
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Not applicable | Upper Mississipi River basin, elevation 142-194m, | No additional description provided | No additional description provided | No additional description provided | No additional description provided | No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | wetlands | No additional description provided | restored, enhanced and created wetlands | field plots near agricultural fields (mixed row crop, almond, walnuts), central valley, Ca | Aquatic systems | No additional description provided | No additional description provided |
EM Scenario Drivers
em.detail.scenarioDriverHelp
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No scenarios presented | Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | Recent historical land-use change from 1990-2000 | No scenarios presented | No scenarios presented | No scenarios presented | Fisheries exploitation; fishing vulnerability (of age classes) | fertilization | N/A | No scenarios presented | Sites, function or habitat focus | Varied wildflower planting mixes of annuals and perennials | Not applicable | No scenarios presented | No scenarios presented |
EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-123 | EM-418 | EM-457 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 |
EM-697 ![]() |
EM-729 ![]() |
EM-784 ![]() |
EM-820 | EM-944 | EM-945 |
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method Only | Method Only | Method + Application |
New or Pre-existing EM?
em.detail.newOrExistHelp
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Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | New or revised model | New or revised model | WESP - Urban Stormwater Treatment | 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 ID
em.detail.idHelp
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EM-94 | EM-97 | EM-123 | EM-418 | EM-457 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 |
EM-697 ![]() |
EM-729 ![]() |
EM-784 ![]() |
EM-820 | EM-944 | EM-945 |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-254 | Doc-256 ?Comment:Document 254 was also used as a source document for this EM |
Doc-142 | Doc-228 | Doc-238 | Doc-239 | Doc-240 | Doc-241 | Doc-242 | None | None | None | None | None | None | None | Doc-390 | None | None | None | Doc-439 |
EM ID for related EM
em.detail.relatedEmEmIdHelp
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None | None | EM-122 | EM-124 | EM-125 | EM-162 | EM-164 | EM-165 | EM-166 | EM-170 | EM-171 | EM-99 | EM-119 | EM-120 | EM-121 | None | None | None | None | EM-593 | None | EM-709 | EM-718 | EM-734 | EM-796 | EM-797 | EM-804 | EM-805 | EM-806 | EM-812 | EM-814 | EM-821 | None | None |
EM Modeling Approach
EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-123 | EM-418 | EM-457 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 |
EM-697 ![]() |
EM-729 ![]() |
EM-784 ![]() |
EM-820 | EM-944 | EM-945 |
EM Temporal Extent
em.detail.tempExtentHelp
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2000 | 1980-2006 | 1990-2000 | 2006-2007, 2010 | 2006-2007, 2010 | 2006-2007, 2010 | 1986-2115 | 1961-1990 | Not applicable | 2007-2008 | 2010-2011 | 2011-2012 | Not applicable | Not applicable | July 2006 to July 2007 |
EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | Not applicable | time-dependent |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | both | Not applicable | Not applicable | past time | past time | Not applicable | Not applicable | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 |
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 | Day | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Month |
EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-123 | EM-418 | EM-457 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 |
EM-697 ![]() |
EM-729 ![]() |
EM-784 ![]() |
EM-820 | EM-944 | EM-945 |
Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Watershed/Catchment/HUC | Geopolitical | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Geopolitical | Point or points | Not applicable | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) |
Point or points ?Comment:This is a guess based on information in the document. 3 field sites were separated by up to 9km |
Not applicable | Geopolitical | Geopolitical |
Spatial Extent Name
em.detail.extentNameHelp
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EU-27 | East Fork Kaskaskia River watershed basin | The EU-25 plus Switzerland and Norway | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Table Mountain National Park Marine Protected Area | Oak Park Research centre | Not applicable | East Midlands | Wetlands in idaho | Agricultural plots | Not applicable | Not applicable | Chicago |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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>1,000,000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 1-10 ha | Not applicable | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | 10-100 km^2 | Not applicable | Not applicable | 100-1000 km^2 |
EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-123 | EM-418 | EM-457 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 |
EM-697 ![]() |
EM-729 ![]() |
EM-784 ![]() |
EM-820 | EM-944 | EM-945 |
EM Spatial Distribution
em.detail.distributeLumpHelp
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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 lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | Not applicable | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | Not applicable | map scale, for cartographic feature | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
em.detail.spGrainSizeHelp
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10 km x 10 km | 1 km^2 | 1 km x 1 km | 10 m x 10 m | 10 m x 10 m | 10 m x 10 m | Not applicable | Not applicable | Not reported | multiple unrelated locations | Not applicable | Not applicable | Not applicable | user defined | plot (green roof) size |
EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-123 | EM-418 | EM-457 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 |
EM-697 ![]() |
EM-729 ![]() |
EM-784 ![]() |
EM-820 | EM-944 | EM-945 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Numeric | Logic- or rule-based | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Analytic |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic |
Statistical Estimation of EM
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EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-123 | EM-418 | EM-457 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 |
EM-697 ![]() |
EM-729 ![]() |
EM-784 ![]() |
EM-820 | EM-944 | EM-945 |
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | No | Yes | Yes | Yes | No | Yes | Not applicable | Not applicable | No | No | Not applicable | Not applicable | Unclear |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | No | No | No | No | No |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
Not applicable | Not applicable | No | No | Not applicable | Not applicable | No |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | 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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Yes | Yes | No | Yes | Yes | Yes |
Yes ?Comment:A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data. |
Yes | Not applicable | Not applicable | No | No | Not applicable | Not applicable | No |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | Yes | No | No | No | No | No | No | Not applicable | Not applicable | No | No | Not applicable | Not applicable | No |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | Unclear | No | No | No | No | No | No | Not applicable | Not applicable | No | No | Yes | Not applicable | No |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Yes | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-94 | EM-97 | EM-123 | EM-418 | EM-457 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 |
EM-697 ![]() |
EM-729 ![]() |
EM-784 ![]() |
EM-820 | EM-944 | EM-945 |
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None | None | None | None |
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None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-94 | EM-97 | EM-123 | EM-418 | EM-457 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 |
EM-697 ![]() |
EM-729 ![]() |
EM-784 ![]() |
EM-820 | EM-944 | EM-945 |
None | None | None |
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None | None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-123 | EM-418 | EM-457 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 |
EM-697 ![]() |
EM-729 ![]() |
EM-784 ![]() |
EM-820 | EM-944 | EM-945 |
Centroid Latitude
em.detail.ddLatHelp
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50.53 | 38.69 | 50.53 | 17.73 | 17.73 | 17.73 | -34.18 | 52.86 | Not applicable | 52.22 | 44.06 | 29.4 | Not applicable | Not applicable | 41.88 |
Centroid Longitude
em.detail.ddLongHelp
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7.6 | -89.1 | 7.6 | -64.77 | -64.77 | -64.77 | 18.35 | 6.54 | Not applicable | -0.91 | -114.69 | -82.18 | Not applicable | Not applicable | 87.65 |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | Not applicable | WGS84 | WGS84 | WGS84 | Not applicable | Not applicable | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Provided | Provided | Not applicable | Estimated | Estimated | Provided | Not applicable | Not applicable | Provided |
EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-123 | EM-418 | EM-457 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 |
EM-697 ![]() |
EM-729 ![]() |
EM-784 ![]() |
EM-820 | EM-944 | EM-945 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Agroecosystems | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Agroecosystems | Inland Wetlands | Created Greenspace | Grasslands | Inland Wetlands | Agroecosystems | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Streams and near upstream environments | Row crop agriculture in Kaskaskia river basin | Not applicable | Coral reefs | Coral reefs | Coral reefs | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | farm pasture | Restored wetlands | restored landfills and grasslands | created, restored and enhanced wetlands | Agricultural landscape | Multiple | Not applicable | urban green roofs |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Other or unclear (comment) ?Comment:Used in both large and small scale context depending upon survey |
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-94 | EM-97 | EM-123 | EM-418 | EM-457 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 |
EM-697 ![]() |
EM-729 ![]() |
EM-784 ![]() |
EM-820 | EM-944 | EM-945 |
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Guild or Assemblage | Individual or population, within a species | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Species | Guild or Assemblage | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-94 | EM-97 | EM-123 | EM-418 | EM-457 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 |
EM-697 ![]() |
EM-729 ![]() |
EM-784 ![]() |
EM-820 | EM-944 | EM-945 |
None Available | None Available | None Available |
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None Available |
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None Available | None Available |
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None Available |
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None Available | 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-94 | EM-97 | EM-123 | EM-418 | EM-457 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 |
EM-697 ![]() |
EM-729 ![]() |
EM-784 ![]() |
EM-820 | EM-944 | EM-945 |
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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-94 | EM-97 | EM-123 | EM-418 | EM-457 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 |
EM-697 ![]() |
EM-729 ![]() |
EM-784 ![]() |
EM-820 | EM-944 | EM-945 |
None |
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None |
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None | None |
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None | None |