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-99 | EM-106 | EM-123 |
EM-125 ![]() |
EM-130 | EM-143 | EM-317 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-629 |
EM-729 ![]() |
EM-846 | EM-892 | EM-937 | EM-941 | EM-961 | EM-990 | EM-997 |
EM Short Name
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Reduction in pesticide runoff risk, Europe | AnnAGNPS, Kaskaskia River watershed, IL, USA | Landscape importance for crops, Europe | Value of Habitat for Shrimp, Campeche, Mexico | Land-use change and wildlife products, Europe | Land-use change and recreation, Europe | KINEROS2, River Ravna watershed, Bulgaria | InVEST habitat quality | ARIES carbon, Puget Sound Region, USA | 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 | SolVES, Pike & San Isabel NF, WY | WESP: Urban Stormwater Treatment, ID, USA | Indigo bunting abund, Piedmont region, USA | VELMA v. 2.1 contaminant modeling | EPA national stormwater calculator tool | ESTIMAP - Pollination potential, Iran | Drainage water recycling, Midwest, USA | Atlantis ecosystem economics submodel | CEASAR and TRACER models, EU |
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 | Landscape importance for crop-based production, Europe | Value of Habitat for Shrimp, Campeche, Mexico | Land-use change effects on wildlife products, Europe | Land-use change effects on recreation, Europe | KINEROS (Kinematic runoff and erosion model) v2, River Ravna watershed,Bulgaria | InVEST (Integrated Valuation of Environmental Services and Tradeoffs) Habitat Quality | ARIES (Artificial Intelligence for Ecosystem Services) Carbon Storage and Sequestration, Puget Sound Region, Washington, USA | 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 | SolVES, Social Values for Ecosystem Services, Pike and San Isabel National Forest, CO | WESP: Urban Stormwater Treament, ID, USA | Indigo bunting abundance, Piedmont ecoregion, USA | VELMA (Visualizing Ecosystem Land Management Assessments) v. 2.1 contaminant modeling | Environmental Protection Agency National stormwater calculator tool | ESTIMAP - Pollination potential, Iran | Drainage water recycling, Midwest, US | Lessons in modelling and management of marine ecosystems: the Atlantis experience | Modelling remediation scenarios in historical mining catchments |
EM Source or Collection
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None | US EPA | EU Biodiversity Action 5 | None | EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 |
InVEST ?Comment:From the Natural Capital Project website |
ARIES | US EPA | InVEST | None | None | None | None | None | US EPA | US EPA | None | None | None | None |
EM Source Document ID
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255 | 137 | 228 | 227 | 228 | 228 |
248 ?Comment:Document 277 is also a source document for this EM |
278 | 302 | 335 |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
358 | 367 | 369 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
405 |
423 ?Comment:Document #430 is an additional source for this EM. Document #423 has been imcorporated into the more recently published document #430. |
428 ?Comment:This is a tool available on the web for downloading to personal computers. A manual is also available for further documentation of the tool. |
434 | 446 | 463 | 467 |
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. | Barbier, E. B., and Strand, I. | Haines-Young, R., Potschin, M. and Kienast, F. | Haines-Young, R., Potschin, M. and Kienast, F. | Nedkov, S., Burkhard, B. | Natural Capital Project | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | 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 | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | Murphy, C. and T. Weekley | Riffel, S., Scognamillo, D., and L. W. Burger | McKane | Rossman, L.A., Bernagros, J.T., Barr, C.M., and M.A. Simon | Rahimi, E., Barghjelveh, S., and P. Dong | Reinhart, B.D., Frankenberger, J.R., Hay, C.H., and Helmers, J.M. | Fulton, E.A., Link, J.S., Kaplan, I.C., Savina‐Rolland, M., Johnson, P., Ainsworth, C., Horne, P., Gorton, R., Gamble, R.J., Smith, A.D. and Smith, D.C. | Gamarra, J. G., Brewer, P. A., Macklin, M. G., & Martin, K. |
Document Year
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2012 | 2011 | 2012 | 1998 | 2012 | 2012 | 2012 | 2014 | 2014 | 2014 | 2018 | 2010 | 2017 | 2014 | 2012 | 2008 | None | 2022 | 2020 | 2019 | 2011 | 2014 |
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 | Valuing mangrove-fishery linkages: A case study of Campeche, Mexico | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria | Habitat Quality model - InVEST ver. 3.0 | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | 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. | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Tutorial A.1 – Contaminant Fate and Transport Modeling Concepts; VELMA 2.1 “How To” Documentation | EPA National Stormwater Calculator Web App users guide-Version 3.4.0. | Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran) | Simulated water quality and irrigation benefits from drainage wter recycling at two tile-drained sites in the U.S. Midwest | Lessons in modelling and management of marine ecosystems: the Atlantis experience | Modelling remediation scenarios in historical mining catchments |
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 | 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 on Natural Capital Project website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report | Published journal manuscript | Published report | Published journal manuscript | Published EPA report | Published EPA report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-94 | EM-97 | EM-99 | EM-106 | EM-123 |
EM-125 ![]() |
EM-130 | EM-143 | EM-317 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-629 |
EM-729 ![]() |
EM-846 | EM-892 | EM-937 | EM-941 | EM-961 | EM-990 | EM-997 |
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 | http://www.tucson.ars.ag.gov/agwa/ | https://www.naturalcapitalproject.org/invest/ | http://aries.integratedmodelling.org/ | Not applicable | https://www.naturalcapitalproject.org/invest/ | http://www.dndc.sr.unh.edu | Not applicable | Not applicable | Not applicable | Not applicable | https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=354355 | https://www.epa.gov/water-research/national-stormwatercalculator | Not applicable | Not applicable | https://research.csiro.au/atlantis/home/links/ | Not applicable | |
Contact Name
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Sven Lautenbach | Yongping Yuan | Marion Potschin | E.B. Barbier | Marion Potschin | Marion Potschin | David C. Goodrich | The Natural Capital Project | Ken Bagstad | Susan H. Yee | Michelle Ward | M. Abdalla | Justin Bousquin | Benson Sherrouse | Chris Murphy | Sam Riffell | Robert B. McKane | Lewis Rossman | Ehsan Rahini | Benjamin Reinhart | Elizabeth Fulton | Javier G. P. Gamarra |
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 | Environment Department, University of York, York YO1 5DD, UK | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | USDA - ARS Southwest Watershed Research Center, 2000 E. Allen Rd., Tucson, AZ 85719 | 371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | Geosciences and Environmental Change Science Center, US Geological Survey | 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 | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | US EPA, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon 97333 | Center for environmental solutions and emergency response, Cincinnati, Ohio | Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran | Agricultural & Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA | Department of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas. 7001, Australia | Institute of Biological, Environmental and Rural Sciences, Aberystwyth, SY23 3DB, UK |
Contact Email
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sven.lautenbach@ufz.de | yuan.yongping@epa.gov | marion.potschin@nottingham.ac.uk | Not reported | marion.potschin@nottingham.ac.uk | marion.potschin@nottingham.ac.uk | agwa@tucson.ars.ag.gov | invest@naturalcapitalproject.org | kjbagstad@usgs.gov | yee.susan@epa.gov | m.ward@uq.edu.au | abdallm@tcd.ie | bousquin.justin@epa.gov | bcsherrouse@usgs.gov | chris.murphy@idfg.idaho.gov | sriffell@cfr.msstate.edu | mckane.bob@epa.gov | n.a. | ehsanrahimi666@gmail.com | breinhar@purdue.edu | beth.fulton@csiro.au | jgg@aber.ac.uk |
EM ID
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EM-94 | EM-97 | EM-99 | EM-106 | EM-123 |
EM-125 ![]() |
EM-130 | EM-143 | EM-317 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-629 |
EM-729 ![]() |
EM-846 | EM-892 | EM-937 | EM-941 | EM-961 | EM-990 | EM-997 |
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 methods are explored in relation to mapping and assessing … “Crop-based production” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain." AUTHOR'S DESCRIPTION: "The analysis for "Crop-based production" maps all the areas that are important for food crops produced through commercial agriculture." | AUTHOR'S DESCRIPTION: "We assume throughout that shrimp harvesting occurs through open access management that yields production which is exported internationally, and we modify a standard open access fishery model to account explicitly for the effect of the mangrove area on carrying capacity and thus production.We derive the conditions determining the long-run equilibrium of the model, including the comparative static effects of a change in mangrove area, on this equilibrium. Through regressing a relationship between shrimp harvest, effort and mangrove area over time, we estimate parameters based on the combinations of the bioeconomic parameters of the model determining the comparative statics. By incorporating additional economic data, we are able to simulate an estimate of the effect of changes in mangrove area in Laguna de Terminos on the production and value of shrimp harvests in Campeche state." (153) | 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: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The novel aspect of this work is an analysis of whether the historical and the projected land use changes for the periods 1990–2000, 2000–2006, and 2000–2030 are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Recreation); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000 and 2000–2006 (LEAC, EEA, 2006) and EURURALIS 2.0 land use scenarios for 2000–2030. The results are reported at three spatial reporting units, i.e. (1) the NUTS-X regions, (2) the bioclimatic regions, and (3) the dominant landscape types." AUTHOR'S DESCRIPTION: " 'Recreation' is broadly defined as all areas where landscape properties are favourable for active recreation purposes….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers…The futures component of the work was based on EURURALIS 2.0 land use scenarios for 2000–2030, which are based on the four IPCC SRES land use scenarios." | ABSTRACT: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. In this study, the capacities of different ecosystems to regulate floods were assessed through investigations of water retention functions of the vegetation and soil cover. The use of the catchment based hydrologic model KINEROS and the GIS AGWA tool provided data about peak rivers’ flows and the capability of different land cover types to “capture” and regulate some parts of the water." AUTHOR'S DESCRIPTION: "KINEROS is a distributed, physically based, event model describing the processes of interception, dynamic infiltration, surface runoff and erosion from watersheds characterized by predominantly overland flow. The watershed is conceptualized as a cascade and the channels, over which the flow is routed in a top–down approach, are using a finite difference solution of the one-dimensional kinematic wave equations (Semmens et al., 2005). Rainfall excess, which leads to runoff, is defined as the difference between precipitation amount and interception and infiltration depth. The rate at which infiltration occurs is not constant but depends on the rainfall rate and the accumulated infiltration amount, or the available moisture condition of the soil. The AGWA tool is a multipurpose hydrologic analysis system addressed to: (1) provide a simple, direct and repeatable method for hydrologic modeling; (2) use basic, attainable GIS data; (3) be compatible with other geospatial basin-based environmental analysis software; and (4) be useful for scenario development and alternative future simulation work at multiple scales (Miller et al., 2002). AGWA provides the functionality to conduct the processes of modeling and assessment for…KINEROS." | Please note: This ESML entry describes an InVEST model version that was current as of 2014. More recent versions may be available at the InVEST website. AUTHORS DESCRIPTION: "The InVEST habitat quality model combines information on LULC and threats to biodiversity to produce habitat quality maps. This approach generates two key sets of information that are useful in making an initial assessment of conservation needs: the relative extent and degradation of different types of habitat types in a region and changes across time. This approach further allows rapid assessment of the status of and change in a proxy for more detailed measures of biodiversity status. If habitat changes are taken as representative of genetic, species, or ecosystem changes, the user is assuming that areas with high quality habitat will better support all levels of biodiversity and that decreases in habitat extent and quality over time means a decline in biodiversity persistence, resilience, breadth and depth in the area of decline. The habitat rarity model indicates the extent and pattern of natural land cover types on the current or a potential future landscape vis-a-vis the extent of the same natural land cover types in some baseline period. Rarity maps allow users to create a map of the rarest habitats on the landscape relative to the baseline chosen by the user to represent the mix of habitats on the landscape that is most appropriate for the study area’s native biodiversity. The model requires basic data that are available virtually everywhere in the world, making it useful in areas for which species distribution data are poor or lacking altogether. Extensive occurrence (presence/absence) data may be available in many places for current conditions. However, modeling the change in occurrence, persistence, or vulnerability of multiple species under future conditions is often impossible or infeasible. While a habitat approach leaves out the detailed species occurrence data available for current conditions, several of its components represent advances in functionality over many existing biodiversity conservation planning tools. The most significant is the ability to characterize the sensitivity of habitats types to various threats. Not all habitats are affected by all threats in the same way, and the InVEST model accounts for this variability. Further, the model allows users to estimate the relative impact of one threat over another so that threats that are more damaging to biodiversity persistence on the landscape can be represented as such. For example, grassland could be particularly sensitive to threats generated by urban areas yet moderately sensitive to threats generated by roads. In addition, the distance over which a threat will degrade natural systems can be incorporated into the model. Model assessment of the current landscape can be used as an input to a coarse-filter assessment of current conservation needs and opportunities. Model assessment of pote | ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "We quantified carbon sequestration and storage in vegetation and soils using Bayesian models (Bagstad et al. 2011) calibrated with Moderate-resolution Imaging Spectroradiometer Net Primary Productivity (MODIS GPP/NPP Project, http://secure.ntsg.umt. edu/projects/index.php/ID/ca2901a0/fuseaction/prohttp://www.whrc.org/ational Bwww.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_053627)vey Geographic Dahttp://www.geomac.gov/index.shtml)wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_053627) soils data, respectively. By overlaying fire boundary polygons from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov/index.shtml) we estimated carbon storage losses caused by wildfire, using fuel consumption coefficients from Spracklen et al. (2009) and carbon pool data from Smith et al. (2006). By incorporating the impacts of land-cover change from urbanization (Bolte and Vache 2010) within carbon models, we quantified resultant changes in carbon storage." | 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: " "Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders.With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems. | 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:"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: "This document describes the conceptual framework underpinning the use of VELMA 2.1 to model fate and transport of organic contaminants within watersheds. We review how VELMA 2.1 simulates contaminant fate and transport within soils and hillslopes as a function of two processes: (1) the partitioning of the total amount of a contaminant between sorbed (immobile) and aqueous (mobile) phases; and (2) the vertical and lateral transport of the contaminant’s aqueous phase within surface and subsurface waters." | "Abstract: EPA’s National Stormwater Calculator (SWC) is a software application tool that estimates the annual amount of rainwater and frequency of runoff from a specific site using green infrastructure as low impact development controls. The SWC is designed for use by anyone interested in reducing runoff from a property, including site developers, landscape architects, urban planners, and homeowners. This User’s guide contains information on the SWC web application. SWC Version 3.4 contains has updated historical meteorological data (from 1970 - 2006 to 1990 - 2019), updated Bureau of Labor Statistics Cost Data (from 2018 to 2020), and the 5.1.015 Stormwater Management Model (SWMM) engine (from 5.1.007). Evaporation was calculated by the Hargreaves method (EPA, 2015), based on historical or future daily temperature data." | Abstract: ". ..we used the ESTIMAP model to improve the results of the Lonsdorf model. For this, we included the effects of roads, railways, rivers, wetlands, lakes, altitude, climate, and ecosystem boundaries in the ESTIMAP modeling and compared the results with the Lonsdorf model. The results of the Lonsdorf model showed that the majority of Iran had a very low potential for providing pollination service and only three percent of the northern and western parts of Iran had high potential. However, the results of the ESTIMAP model showed that 16% of Iran had a high potential to provide pollination that covers most of the northern and southern parts of the country. The results of the ESTIMAP model for pollination mapping in Iran showed the Lonsdorf model of estimating pollination service can be improved through considering other relevant factors." | [Enter up to 65000 characters] | Models are key tools for integrating a wide range of system information in a common framework. Attempts to model exploited marine ecosystems can increase understanding of system dynamics; identify major processes, drivers and responses; highlight major gaps in knowledge; and provide a mechanism to ‘road test’ management strategies before implementing them in reality. The Atlantis modelling framework has been used in these roles for a decade and is regularly being modified and applied to new questions (e.g. it is being coupled to climate, biophysical and economic models to help consider climate change impacts, monitoring schemes and multiple use management). This study describes some common lessons learned from its implementation, particularly in regard to when these tools are most effective and the likely form of best practices for ecosystem-based management (EBM). Most importantly, it highlighted that no single management lever is sufficient to address the many trade-offs associated with EBM and that the mix of measures needed to successfully implement EBM will differ between systems and will change through time. Although it is doubtful that any single management action will be based solely on Atlantis, this modelling approach continues to provide important insights for managers when making natural resource management decisions. | Local remediation measures, particularly those undertaken in historical mining areas, can often be ineffective or even deleterious because erosion and sedimentation processes operate at spatial scales beyond those typically used in point-source remediation. Based on realistic simulations of a hybrid landscape evolution model combined with stochastic rainfall generation, we demonstrate that similar remediation strategies may result in differing effects across three contrasting European catchments depending on their topographic and hydrologic regimes. Based on these results, we propose a conceptual model of catchment-scale remediation effectiveness based on three basic catchment characteristics: the degree of contaminant source coupling, the ratio of contaminated to non-contaminated sediment delivery, and the frequency of sediment transport events. |
Specific Policy or Decision Context Cited
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European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | Not reported | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | Future rock lobster fisheries management | climate change | None identified | None | None identified | None reported | None identified | None given | None reported | None | None identified | None identified |
Biophysical Context
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Not applicable | Upper Mississipi River basin, elevation 142-194m, | No additional description provided | Gulf of Mexico; mangrove-lagoon system | No additional description provided | No additional description provided | Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak. | Not applicable | No additional description provided | No additional description provided | No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | wetlands | Rocky mountain conifer forests | restored, enhanced and created wetlands | Conservation Reserve Program lands left to go fallow | No additional description provided | Sites up to 12 acres | None additional | None | NA | Rver system catchments associated with mining sites distributed across Europe |
EM Scenario Drivers
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No scenarios presented | Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | No scenarios presented | No scenarios presented | Recent historical land-use change from 1990-2000 | Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use change (2000-2030) | No scenarios presented |
Potential land Use Land Class (LULC) future and baseline ?Comment:model requires current landuse but can compare to baseline (prior to intensive management of the land) and potential future landuse. These are the two scenarios suggested in the documentation. |
No scenarios presented | No scenarios presented | Fisheries exploitation; fishing vulnerability (of age classes) | fertilization | N/A | N/A | Sites, function or habitat focus | N/A | No scenarios presented | Climate change scenarios | N/A | None | No scenarios presented | No scenarios presented |
EM ID
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EM-94 | EM-97 | EM-99 | EM-106 | EM-123 |
EM-125 ![]() |
EM-130 | EM-143 | EM-317 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-629 |
EM-729 ![]() |
EM-846 | EM-892 | EM-937 | EM-941 | EM-961 | EM-990 | EM-997 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method Only | Method + Application | None | Method Only | Method + Application |
New or Pre-existing EM?
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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 | New or revised 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 | Application of existing model | None | Application of existing model | Application of existing 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-94 | EM-97 | EM-99 | EM-106 | EM-123 |
EM-125 ![]() |
EM-130 | EM-143 | EM-317 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-629 |
EM-729 ![]() |
EM-846 | EM-892 | EM-937 | EM-941 | EM-961 | EM-990 | EM-997 |
EM Temporal Extent
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2000 | 1980-2006 | 2000 | 1980-1990 | 1990-2000 | 1990-2030 | Not reported | Not applicable | 1950-2007 | 2006-2007, 2010 | 1986-2115 | 1961-1990 | Not applicable | 2004-2008 | 2010-2011 | 2008 | Not applicable | Not applicable | 2020 | None | Not applicable | 1800-2100 |
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-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | None | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | future time | both | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable | Not applicable | None | Not applicable | both |
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 | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | None | continuous | continuous |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not reported | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | None | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not reported | Not applicable | Not applicable | Not applicable | Year | Day | Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | None | Not applicable | Not applicable |
EM ID
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EM-94 | EM-97 | EM-99 | EM-106 | EM-123 |
EM-125 ![]() |
EM-130 | EM-143 | EM-317 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-629 |
EM-729 ![]() |
EM-846 | EM-892 | EM-937 | EM-941 | EM-961 | EM-990 | EM-997 |
Bounding Type
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Geopolitical | Watershed/Catchment/HUC | Geopolitical | Physiographic or Ecological | Geopolitical | Geopolitical | Watershed/Catchment/HUC | No location (no locational reference given) | Physiographic or ecological | Physiographic or ecological | Geopolitical | Point or points | Not applicable | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Not applicable | Not applicable | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Not applicable | Watershed/Catchment/HUC |
Spatial Extent Name
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EU-27 | East Fork Kaskaskia River watershed basin | The EU-25 plus Switzerland and Norway | Laguna de Terminos Mangrove system | The EU-25 plus Switzerland and Norway | The EU-25 plus Switzerland and Norway | River Ravna watershed | Not applicable | Puget Sound Region | Coastal zone surrounding St. Croix | Table Mountain National Park Marine Protected Area | Oak Park Research centre | Not applicable | National Park | Wetlands in idaho | Piedmont Ecoregion | Not applicable | Not applicable | Iran | Western & Eastern Corn Belt Plains | Not applicable | Ystwyth, Ampoi, and Naracauli |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 10-100 km^2 | Not applicable | 10,000-100,000 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 | 100,000-1,000,000 km^2 | Not applicable | Not applicable | >1,000,000 km^2 | 100,000-1,000,000 km^2 | Not applicable | 100-1000 km^2 |
EM ID
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EM-94 | EM-97 | EM-99 | EM-106 | EM-123 |
EM-125 ![]() |
EM-130 | EM-143 | EM-317 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-629 |
EM-729 ![]() |
EM-846 | EM-892 | EM-937 | EM-941 | EM-961 | EM-990 | EM-997 |
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 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) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
spatially distributed (in at least some cases) ?Comment:Varies by inputs, but results are for areas of country |
None | Not applicable | spatially distributed (in at least some cases) |
Spatial Grain Type
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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 | 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 | area, for pixel or radial feature | Not applicable | Not applicable | volume, for 3-D feature | Not applicable | area, for pixel or radial feature | None | Not applicable | map scale, for cartographic feature |
Spatial Grain Size
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10 km x 10 km | 1 km^2 | 1 km x 1 km | 1 km x 1 km | 1 km x 1 km | 1 km x 1 km | 25 m x 25 m | LULC pixel size | 200m x 200m | 10 m x 10 m | Not applicable | Not applicable | Not reported | 30m2 | Not applicable | Not applicable | user defined | Not applicable | ha^2 | None | Not applicable | Not reported |
EM ID
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EM-94 | EM-97 | EM-99 | EM-106 | EM-123 |
EM-125 ![]() |
EM-130 | EM-143 | EM-317 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-629 |
EM-729 ![]() |
EM-846 | EM-892 | EM-937 | EM-941 | EM-961 | EM-990 | EM-997 |
EM Computational Approach
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Analytic | Numeric | Logic- or rule-based | Analytic | Logic- or rule-based | Logic- or rule-based | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Numeric | Numeric | Analytic | Analytic | Analytic | Numeric | * | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | None | deterministic | stochastic |
Statistical Estimation of EM
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None |
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EM ID
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EM-94 | EM-97 | EM-99 | EM-106 | EM-123 |
EM-125 ![]() |
EM-130 | EM-143 | EM-317 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-629 |
EM-729 ![]() |
EM-846 | EM-892 | EM-937 | EM-941 | EM-961 | EM-990 | EM-997 |
Model Calibration Reported?
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No | No | No | Yes | No | No | Yes | Not applicable | Yes | Yes | No | Yes | Not applicable | No | No | Yes | Not applicable | Not applicable | No | None | Not applicable | Yes |
Model Goodness of Fit Reported?
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No | No | No | Yes | No | No | No | Not applicable | No | No | No |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
Not applicable | Yes | No | No | Not applicable | Not applicable | No | None | Not applicable | No |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None |
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None | None | None | None | None | None | None |
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None |
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None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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Yes | Yes | Yes | No | No | No | No | Not applicable | No | 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 | No | No | No | Not applicable | Not applicable | No | None | Not applicable | Yes |
Model Uncertainty Analysis Reported?
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No | Yes | No | Yes | No | No | No | Not applicable | No | No | No | No | Not applicable | No | No | No | Not applicable | Not applicable | No | None | Not applicable | Unclear |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | Unclear | No | Yes | No | No | No | Not applicable | No | No | No | No | Not applicable | No | No | Yes | Not applicable | Not applicable | No | None | Not applicable | Unclear |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | None | 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-99 | EM-106 | EM-123 |
EM-125 ![]() |
EM-130 | EM-143 | EM-317 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-629 |
EM-729 ![]() |
EM-846 | EM-892 | EM-937 | EM-941 | EM-961 | EM-990 | EM-997 |
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None |
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None | None |
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None |
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None |
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Comment:Model for Iran - no form preset id for country |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-94 | EM-97 | EM-99 | EM-106 | EM-123 |
EM-125 ![]() |
EM-130 | EM-143 | EM-317 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-629 |
EM-729 ![]() |
EM-846 | EM-892 | EM-937 | EM-941 | EM-961 | EM-990 | EM-997 |
None | None | None |
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None | None | None | None | None |
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None | None | None | 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-99 | EM-106 | EM-123 |
EM-125 ![]() |
EM-130 | EM-143 | EM-317 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-629 |
EM-729 ![]() |
EM-846 | EM-892 | EM-937 | EM-941 | EM-961 | EM-990 | EM-997 |
Centroid Latitude
em.detail.ddLatHelp
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50.53 | 38.69 | 50.53 | 18.61 | 50.53 | 50.53 | 42.8 | -9999 | 48 | 17.73 | -34.18 | 52.86 | Not applicable | 38.7 | 44.06 | 36.23 | Not applicable | Not applicable | 32.29 | None | Not applicable |
52.5 ?Comment:There are 3 locations provided in this study with latitudes of 52.5, 46, and 40 as well as longitudes of -4, 10, and 25, respectively. |
Centroid Longitude
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7.6 | -89.1 | 7.6 | -91.55 | 7.6 | 7.6 | 24 | -9999 | -123 | -64.77 | 18.35 | 6.54 | Not applicable | 105.89 | -114.69 | -81.9 | Not applicable | Not applicable | 53.68 | None | Not applicable | -4 |
Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | None provided | Not applicable | WGS84 | WGS84 | WGS84 | Not applicable | Not applicable | WGS84 | None | Not applicable | None provided |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated | Provided | Provided | Not applicable | Estimated | Estimated | Estimated | Not applicable | Not applicable | Estimated | None | Not applicable | Estimated |
EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-99 | EM-106 | EM-123 |
EM-125 ![]() |
EM-130 | EM-143 | EM-317 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-629 |
EM-729 ![]() |
EM-846 | EM-892 | EM-937 | EM-941 | EM-961 | EM-990 | EM-997 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Forests | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Forests | Atmosphere | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Agroecosystems | Inland Wetlands | Forests | Inland Wetlands | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Streams and near upstream environments | Row crop agriculture in Kaskaskia river basin | Not applicable | Mangrove | Not applicable | Not applicable | Primarily forested watershed | Not applicable | Terrestrial environment surrounding a large estuary | Coral reefs | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | farm pasture | Restored wetlands | Montain forest | created, restored and enhanced wetlands | grasslands | Terrestrial environment | Terrrestrial landcover | terrestrial land types | Plains | Multiple | Watershed catchment |
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 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 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 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 |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-94 | EM-97 | EM-99 | EM-106 | EM-123 |
EM-125 ![]() |
EM-130 | EM-143 | EM-317 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-629 |
EM-729 ![]() |
EM-846 | EM-892 | EM-937 | EM-941 | EM-961 | EM-990 | EM-997 |
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Individual or population, within a species | Not applicable | Not applicable | Not applicable | Not applicable | Species | Not applicable | Not applicable | Not applicable | None | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-94 | EM-97 | EM-99 | EM-106 | EM-123 |
EM-125 ![]() |
EM-130 | EM-143 | EM-317 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-629 |
EM-729 ![]() |
EM-846 | EM-892 | EM-937 | EM-941 | EM-961 | EM-990 | EM-997 |
None Available | None Available | None Available |
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None Available | None Available | None Available | None Available | None Available |
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None Available | None Available | None Available | None Available |
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None Available | None Available |
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None Available | 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-99 | EM-106 | EM-123 |
EM-125 ![]() |
EM-130 | EM-143 | EM-317 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-629 |
EM-729 ![]() |
EM-846 | EM-892 | EM-937 | EM-941 | EM-961 | EM-990 | EM-997 |
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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-99 | EM-106 | EM-123 |
EM-125 ![]() |
EM-130 | EM-143 | EM-317 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-629 |
EM-729 ![]() |
EM-846 | EM-892 | EM-937 | EM-941 | EM-961 | EM-990 | EM-997 |
None |
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None | None | None |
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None |
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None |
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None | None |
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