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-98 ![]() |
EM-109 ![]() |
EM-119 |
EM-129 ![]() |
EM-178 | EM-185 | EM-317 | EM-368 | EM-448 |
EM-480 ![]() |
EM-541 ![]() |
EM-598 | EM-617 |
EM-709 ![]() |
EM-848 | EM-883 | EM-943 | EM-961 | EM-967 | EM-980 |
EM Short Name
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Reduction in pesticide runoff risk, Europe | AnnAGNPS, Kaskaskia River watershed, IL, USA | PATCH, western USA | UFORE-Hydro, Baltimore, MD, USA | Landscape importance for wildlife products, Europe | 3-PG, South Australia | Natural attenuation by soil, The Netherlands | Blue crabs and SAV, Chesapeake Bay, USA | ARIES carbon, Puget Sound Region, USA | InVEST - Water Yield (v3.0) | Wave energy attenuation, St. Croix, USVI | Yasso07 - Land use SOC dynamics, China | InVEST fisheries, lobster, South Africa | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | RBI Spatial Analysis Method | Pollinators on landfill sites, United Kingdom | National invertebrate community rank index | VELMA v. 2.0 LSR | Visitation to natural areas, New England, USA | Drainage water recycling, Midwest, USA | IPaC, USFWS, USA | Childrens health, Melbourne, Australia |
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 | PATCH (Program to Assist in Tracking Critical Habitat), western USA |
UFORE-Hydro (Urban Forest Effects - Hydrology) v1, Dead Run Catchment, Baltimore, MD ?Comment:UFORE-Hydro is now incorporated in the i-Tree suite of models as iTree-Hydro. |
Landscape importance for wildlife products, Europe | 3-PG (Physiological Principles Predicting Growth), South Australia | Natural attenuation capacity of the soil, The Netherlands | Blue crabs and submerged aquatic vegetation interaction, Chesapeake Bay, USA | ARIES (Artificial Intelligence for Ecosystem Services) Carbon Storage and Sequestration, Puget Sound Region, Washington, USA | InVEST v3.0 Reservoir Hydropower Projection, aka Water Yield | Wave energy attenuation (by reef), St. Croix, USVI | Yasso07 - Land use dynamics of Soil Organic Carbon in the Loess Plateau, China | 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 | Pollinating insects on landfill sites, East Midlands, United Kingdon | National invertebrate community ranking index (NICRI) | VELMA (Visualizing Ecosystems for Land Management Assessments) Version 2.0 Leaf Stem Root (LSR) | Estimating natural area use with cell phone data, Narragansett Beach, New England, USA | Drainage water recycling, Midwest, US | Information for Planning and Conservation tool, USFWS, U.S. | Childrens mental, emotional and social health- a model, Melbourne, Australia |
EM Source or Collection
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None | US EPA | US EPA | i-Tree | USDA Forest Service | EU Biodiversity Action 5 | None | None | None | ARIES | InVEST | US EPA | None | InVEST | None | None | None | None | US EPA | US EPA | None | None | None |
EM Source Document ID
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255 | 137 | 2 | 190 | 228 | 243 | 287 |
292 ?Comment:Conference paper |
302 | 311 | 335 | 344 |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
358 | 367 | 389 | 407 | 366 | 436 | 446 |
451 ?Comment:Assume peer reviewed at least internally by USFWS |
458 |
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. | Carroll, C, Phillips, M. K. , Lopez-Gonzales, C. A and Schumaker, N. H. | Wang, J., Endreny, T. A. and Nowak, D. J. | Haines-Young, R., Potschin, M. and Kienast, F. | Crossman, N. D., Bryan, B. A., and Summers, D. M. | van Wijnen, H.J., Rutgers, M., Schouten, A.J., Mulder, C., de Zwart, D., and Breure, A.M. | Mykoniatis, N. and Ready, R. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Natural Capital Project | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu | 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 | Cuffney, Tom | McKane, R. B., A. Brookes, K. Djang, M. Stieglitz, A. G. Abdelnour, F. Pan, J. J. Halama, P. B. Pettus and D. L. Phillips | Merrill, N.H., Atkinson, S.F., Mulvaney, K.K., Mazzotta, K.K., and J. Bousquin | Reinhart, B.D., Frankenberger, J.R., Hay, C.H., and Helmers, J.M. | U.S. Fish and Wildlife Service | Maller, C.J. |
Document Year
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2012 | 2011 | 2006 | 2008 | 2012 | 2011 | 2012 | 2013 | 2014 | 2015 | 2014 | 2015 | 2018 | 2010 | 2017 | 2013 | 2003 | 2014 | 2020 | 2019 | None | 2009 |
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 | Defining recovery goals and strategies for endangered species: The wolf as a case study | Mechanistic simulation of tree effects in an urban water balance model | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Carbon payments and low-cost conservation | How to calculate the spatial distribution of ecosystem services - Natural attenuation as example from the Netherlands | Evaluating habitat-fishery interactions: The case of submerged aquatic vegetation and blue crab fishery in the Chesapeake Bay | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Water Yield: Reservoir Hydropower Production- InVEST (v3.0) | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement | 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 | Invertebrate Status Index | VELMA Version 2.0 User Manual and Technical Documentation | Using data derived from cellular phone locations to estimate visitation to natural areas: An application to water recreation in New England, USA | Simulated water quality and irrigation benefits from drainage wter recycling at two tile-drained sites in the U.S. Midwest | Information for Planning and Consultation (IPaC | Promoting children’s mental, emotional and social health through contact with nature: a model |
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 | Not formally documented | 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 | Other or unclear (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Other or unclear (explain in Comment) | 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 | Conference proceedings | Published journal manuscript | Web published | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report | Published journal manuscript | Published report | Published EPA report | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript |
EM ID
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EM-94 | EM-97 |
EM-98 ![]() |
EM-109 ![]() |
EM-119 |
EM-129 ![]() |
EM-178 | EM-185 | EM-317 | EM-368 | EM-448 |
EM-480 ![]() |
EM-541 ![]() |
EM-598 | EM-617 |
EM-709 ![]() |
EM-848 | EM-883 | EM-943 | EM-961 | EM-967 | EM-980 |
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 | http://www.itreetools.org/ | Not applicable | http://www.csiro.au/products/3PGProductivity#a1 | Not applicable | Not applicable | http://aries.integratedmodelling.org/ | https://www.naturalcapitalproject.org/invest/ | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | https://www.naturalcapitalproject.org/invest/ | http://www.dndc.sr.unh.edu | Not applicable | Not applicable | Not applicable | https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | https://github.com/USEPA/Recreation_Benefits.git | Not applicable | https://ipac.ecosphere.fws.gov/ | Not applicable | |
Contact Name
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Sven Lautenbach | Yongping Yuan | Carlos Carroll | Jun Wang | Marion Potschin | Anders Siggins | H.J. van Wijnen | Nikolaos Mykoniatis | Ken Bagstad | Natural Capital Project | Susan H. Yee | Xing Wu | Michelle Ward | M. Abdalla | Justin Bousquin | Sam Tarrant | Tom Cuffney | Robert B. McKane | Nathaniel Merrill | Benjamin Reinhart | USFWS | Cecily Maller |
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 | Klamath Center for Conservation Research, Orleans, CA 95556 | Environmental Resources and Forest Engineering, Colecge of Environmental Science and Forestry, State University of New York, Syracuse, New York 13210 | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Not reported | National Institute for Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, The Netherlands | Department of Agricultural Economics, Sociology and Education The Pennsylvania State University | Geosciences and Environmental Change Science Center, US Geological Survey | 371 Serra Mall, Stanford University, Stanford, Ca 94305 | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Chinese Academy of Sciences, Beijing 100085, China | 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. | 3916 Sunset Ridge Rd, Raleigh, NC 27607 | USEPA Office of Research and Development National Health and Environmental Effects Research Laboratory Western Ecology Division Corvallis, Oregon 97333 | Atlantic Coastal Environmental Sciences Division, U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Narragansett, Rhode Island, United States of America, | Agricultural & Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA | 911 NE 11th Avenue Portland, OR 97232 | The Centre for Design & The Global Cities Institute, College of Design and Social Context, RMIT University Melbourne, Australia |
Contact Email
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sven.lautenbach@ufz.de | yuan.yongping@epa.gov | carlos@cklamathconservation.org | Not reported | marion.potschin@nottingham.ac.uk | Anders.Siggins@csiro.au | harm.van.wijnen@rivm.nl | Not reported | kjbagstad@usgs.gov | invest@naturalcapitalproject.org | yee.susan@epa.gov | xingwu@rceesac.cn | m.ward@uq.edu.au | abdallm@tcd.ie | bousquin.justin@epa.gov | sam.tarrant@rspb.org.uk | tcuffney@usgs.gov | mckane.bob@epa.gov | merrill.nathaniel@epa.gov | breinhar@purdue.edu | fwhq_ipac@fws.gov | cecily.maller@rmit.edu.au |
EM ID
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EM-94 | EM-97 |
EM-98 ![]() |
EM-109 ![]() |
EM-119 |
EM-129 ![]() |
EM-178 | EM-185 | EM-317 | EM-368 | EM-448 |
EM-480 ![]() |
EM-541 ![]() |
EM-598 | EM-617 |
EM-709 ![]() |
EM-848 | EM-883 | EM-943 | EM-961 | EM-967 | EM-980 |
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" | **Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** AUTHORS' DESCRIPTION: "PATCH (program to assist in tracking critical habitat), the SEPM used here, is designed for studying territorial vertebrates. It links the survival and fecundity of individual animals to geographic information system (GIS) data on mortality risk and habitat productivity at the scale of an individual or pack territory. Territories are allocated by intersecting the GIS data with an array of hexagonal cells. The different habitat types in the GIS maps are assigned weights based on the relative levels of fecundity and survival expected in those habitat classes. Base survival and reproductive rates, derived from published field studies, are then supplied to the model as a population projection matrix. The model scales these base matrix values using the mean of the habitat weights within each hexagon, with lower means translating into lower survival rates or reproductive output. Each individual in the population is tracked through a yearly cycle of survival, fecundity, and dispersal events. Environmental stochasticity is incorporated by drawing each year’s base population matrix from a randomized set of matrices whose elements were drawn from a beta (survival) or normal (fecundity) distribution. Adult organisms are classified as either territorial or floaters. The movement of territorial individuals is governed by a parameter for site fidelity, but floaters must always search for available breeding sites. As pack size increases, pack members in the model have a greater tendency to disperse and search for new available breeding sites. Movement decisions use a directed random walk that combines varying proportions of randomness, correlation, and attraction to higher-quality habitat (Schumaker 1998)." | ABSTRACT: "A semidistributed, physical-based Urban Forest Effects – Hydrology (UFORE-Hydro) model was created to simulate and study tree effects on urban hydrology and guide management of urban runoff at the catchment scale. The model simulates hydrological processes of precipitation, interception, evaporation, infiltration, and runoff using data inputs of weather, elevation, and land cover along with nine channel, soil, and vegetation parameters. Weather data are pre-processed by UFORE using Penman-Monteith equations to provide potential evaporation terms for open water and vegetation. Canopy interception algorithms modified established routines to better account for variable density urban trees, short vegetation, and seasonal growth phenology. Actual evaporation algorithms allocate potential energy between leaf surface storage and transpiration from soil storage. Infiltration algorithms use a variable rain rate Green-Ampt formulation and handle both infiltration excess and saturation excess ponding and runoff. Stream discharge is the sum of surface runoff and TOPMODEL- based subsurface flow equations. Automated calibration routines that use observed discharge has been coupled to the model." FURTHER DESCRIPTION: UFORE-Hydro was tested in the urban Dead Run catchment of Baltimore, Maryland, USA. | 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 … “Wildlife Products” . . . 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, adjacency to coastal and wetland ecosystems, as well as adjacency to landscape protection zones." AUTHOR'S DESCRIPTION: "Wildlife Products…includes the provisioning of all non-edible raw material products that are gained through non-agriculutural 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." | AUTHOR'S DESCRIPTION: "Carbon trading and its resultant market for carbon offsets are expected to drive investment in the sequestration of carbon through tree plantings (i.e., carbon plantings). Most carbon-planting investment has been in monocultures of trees that offer a rapid return on investment but have relatively little compositional and structural diversity (Bekessy & Wintle 2008; Munro et al. 2009). There are additional benefits available should carbon plantings comprise native species of diverse composition and age that are planted strategically to meet conservation and restoration objectives (hereafter ecological carbon plantings) (Bekessy &Wintle 2008; Dwyer et al. 2009; Bekessy et al. 2010). Ecological carbon plantings may increase availability of resources and refugia for native animals, but they often yield less carbon and are more expensive to establish than monocultures and therefore are less profitable…We used the tree-stand growth model 3-PG (physiological principles predicting growth) (Landsberg & Waring 1997) to simulate annual carbon sequestration under permanent carbon plantings in the part of the study area cleared for agriculture. The 3-PG model calculates total above- and below-ground biomass of a stand after accounting for soil water deficit, atmospheric vapor pressure deficits, and stand age…The 3-PG model was originally parameterized for a generic species, but species-specific parameters have since been calibrated for many commercially valuable trees (Paul et al. 2007) and most recently for mixed species used in permanent ecological restoration plantings (Polglase et al. 2008). We simulated four carbon-planting systems described in Polglase et al. (2008) for which the plants in the systems would grow in our study area. All species were native to areas of Australia with climate similar to that in the study area. We simulated the annual growth of three trees typically grown in monoculture (Eucalyptus globulus, native to Tasmania, constrained to precipitation ≥ 550 mm/year; Eucalyptus camaldulensis, native to the study area, constrained to 350–549 mm/year; Eucalyptus kochii, native to Western Australia, constrained to <350 mm/year). For the simulations of ecological carbon plantings we used a set of trees and shrubs representative of those planted for ecological restoration in temperate southern Australia (species list in England et al. 2006).We assumed the ecological carbon plantings were planted and managed in such a way as to comply with the definition of ecological restoration (Society for Ecological Restoration International Science and PolicyWorking Group 2004)." | ABSTRACT: "Maps play an important role during the entire process of spatial planning and bring ecosystem services to the attention of stakeholders' negotiation more easily. As example we show the quantification of the ecosystem service ‘natural attenuation of pollutants’, which is a service necessary to keep the soil clean for production of safe food and provision of drinking water, and to provide a healthy habitat for soil organisms to support other ecosystem services. A method was developed to plot the relative measure of the natural attenuation capacity of the soil in a map. Several properties of Dutch soils were related to property-specific reference values and subsequently combined into one proxy for the natural attenuation of pollutants." AUTHOR'S DESCRIPTION: "The natural attenuation capacity that is modeled in this study must be seen as a measure that describes the ‘biodegradation capacity’ of the soil, including biodegradation of all types of contaminants" | ABSTRACT: "This paper investigates habitat-fisheries interaction between two important resources in the Chesapeake Bay: blue crabs and Submerged Aquatic Vegetation (SAV). A habitat can be essential to a species (the species is driven to extinction without it), facultative (more habitat means more of the species, but species can exist at some level without any of the habitat) or irrelevant (more habitat is not associated with more of the species). An empirical bioeconomic model that nests the essential-habitat model into its facultative-habitat counterpart is estimated. Two alternative approaches are used to test whether SAV matters for the crab stock. Our results indicate that, if we do not have perfect information on habitat-fisheries linkages, the right approach would be to run the more general facultative-habitat model instead of the essential- habitat one." | ABSTRACT: "...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." | Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. AUTHOR'S DESCRIPTION: "The InVEST Reservoir Hydropower model estimates the relative contributions of water from different parts of a landscape, offering insight into how changes in land use patterns affect annual surface water yield and hydropower production. Modeling the connections between landscape changes and hydrologic processes is not simple. Sophisticated models of these connections and associated processes (such as the WEAP model) are resource and data intensive and require substantial expertise. To accommodate more contexts, for which data are readily available, InVEST maps and models the annual average water yield from a landscape used for hydropower production, rather than directly addressing the affect of LULC changes on hydropower failure as this process is closely linked to variation in water inflow on a daily to monthly timescale. Instead, InVEST calculates the relative contribution of each land parcel to annual average hydropower production and the value of this contribution in terms of energy production. The net present value of hydropower production over the life of the reservoir also can be calculated by summing discounted annual revenues. The model runs on a gridded map. It estimates the quantity and value of water used for hydropower production from each subwatershed in the area of interest. It has three components, which run sequentially. First, it determines the amount of water running off each pixel as the precipitation less the fraction of the water that undergoes evapotranspiration. The model does not differentiate between surface, subsurface and baseflow, but assumes that all water yield from a pixel reaches the point of interest via one of these pathways. This model then sums and averages water yield to the subwatershed level. The pixel-scale calculations allow us to represent the heterogeneity of key driving factors in water yield such as soil type, precipitation, vegetation type, etc. However, the theory we are using as the foundation of this set of models was developed at the subwatershed to watershed scale. We are only confident in the interpretation of these models at the subwatershed scale, so all outputs are summed and/or averaged to the subwatershed scale. We do continue to provide pixel-scale representations of some outputs for calibration and model-checking purposes only. These pixel-scale maps are not to be interpreted for understanding of hydrological processes or to inform decision making of any kind. | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion, storm damage, or coastal inundation during extreme events (UNEP-WCMC (United Nations Environment Programme, World Conservation Monitoring Centre), 2006; WRI (World Resources Institute), 2009), but is often quantified as wave energy attenuation, an intermediate service that contributes to shoreline protection by reducing rates of erosion or coastal inundation (Principeet al., 2012)...The energy (attenuation) in a moving wave (E) can then be calculated by E = 1/8ρgH^2 where ρ is the density of seawater (1025 kg m^-3) and H is wave height (attenuation)." | ABSTRACT: "Land use changes are known to significantly affect the soil C balance by altering both C inputs and losses. Since the late 1990s, a large area of the Loess Plateau has undergone intensive land use changes during several ecological restoration projects to control soil erosion and combat land degradation, especially in the Grain for Green project. By using remote sensing techniques and the Yasso07 model, we simulated the dynamics of soil organic carbon (SOC) stocks in the Yangjuangou catchment of the Loess Plateau. The performance of the model was evaluated by comparing the simulated results with the intensive field measurements in 2006 and 2011 throughout the catchment. SOC stocks and NPP values of all land use types had generally increased during our study period. The average SOC sequestration rate in the upper 30 cm soil from 2006 to 2011 in the Yangjuangou catchment was approximately 44 g C m-2 yr-1, which was comparable to other studies in the Loess Plateau. Forest and grassland showed a more effective accumulation of SOC than the other land use types in our study area. The Yasso07 model performed reasonably well in predicting the overall dynamics of SOC stock for different land use change types at both the site and catchment scales. The assessment of the model performance indicated that the combination of Yasso07 model and remote sensing data could be used for simulating the effect of land use changes on SOC stock at catchment scale in the Loess Plateau." | 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…In the first year of study, plants in flower and flower visitors were surveyed using the same transects as for the floral resources surveys. The transect was left undisturbed for 20 minutes following the initial plant survey to allow the flower visitors to return. Each transect was surveyed at a rate of approximately 3m/minute for 30 minutes. All insects observed to touch the sexual parts of flowers were either captured using a butterfly net and transferred into individually labeled specimen jars, or directly captured into the jars. After the survey was completed, those insects that could be identified in the field were recorded and released. The flower-visitor surveys were conducted in the morning, within 1 hour of midday, and in the afternoon to sample those insects active at different times. Insects that could not be identified in the field were collected as voucher specimens for later identification. Identifications were verified using reference collections and by taxon specialists. Relatively low capture rates in the first year led to methods being altered in the second year when surveying followed a spiral pattern from a randomly determined point on the sites, at a standard pace of 10 m/minute for 30 minutes, following Nielsen and Bascompte (2007) and Kalikhman (2007). Given a 2-m wide transect, an area of approximately 600m2 was sampled in each | ABSTRACT: "The Invertebrate Status Index is a multimetric index that was derived for the NAWQA Program to provide a simple national characterization of benthic invertebrate communities. This index— referred to here as the National Invertebrate Community Ranking Index (NICRI)—provides a simple method of placing community conditions within the context of all sites sampled by the NAWQA Program. The multimetric index approach is the most commonly used method of characterizing biological conditions within the U.S. (Barbour and others, 1999). Using this approach, communities may be compared by considering how individual metrics vary among sites or by combining individual metrics into a single composite (i.e., multimetric) index and examining how this single index varies among sites. Combining metrics into a single multimetric index simplifies the presentation of results (Barbour and others, 1999) and minimizes weaknesses that may be associated with individual metrics (Ohio EPA, 1987a,b). The NICRI is a multimetric index that combines 11 metrics (RICH, EPTR, CG_R, PR_R, EPTRP, CHRP, V2DOMP, EPATOLR, EPATOLA, DIVSHAN, and EVEN; Table 1) into a single, nationally consistent, composite index. The NICRI was used to rank 140 sites of the FY94 group of study units, with median values used for sites where data were available for multiple reaches and(or) multiple years. Average metric scores were then rescaled using the PERCENTRANK function and multiplied by 100 to produce a final NICRI score that ranged from 0 (low ranking relative to other NAWQA Program sites and presumably diminished community conditions) to 100 (high ranking relative to other NAWQA Program sites and presumably excellent community conditions). " | ABSTRACT: "VELMA – Visualizing Ecosystems for Land Management Assessments – is a spatially distributed, eco-hydrological model that links a land surface hydrology model with a terrestrial biogeochemistry model for simulating the integrated responses of vegetation, soil, and water resources to interacting stressors. For example, VELMA can simulate how changes in climate and land use interact to affect soil water storage, surface and subsurface runoff, vertical drainage, evapotranspiration, vegetation and soil carbon and nitrogen dynamics, and transport of nitrate, ammonium, and dissolved organic carbon and nitrogen to water bodies. VELMA differs from other existing eco-hydrology models in its simplicity, flexibility, and theoretical foundation. The model has a user-friendly Graphics User Interface (GUI) for easy input of model parameter values. In addition, advanced visualization of simulation results can enhance understanding of results and underlying concepts. VELMA’s visualization and interactivity features are packaged in an open-source, open-platform programming environment (Java / Eclipse). The development team for VELMA version 2.0 includes Dr. Bob McKane and coworkers at the U.S. Environmental Protection Agency’s Western Ecology Division, Dr. Marc Stieglitz and coworkers at the Georgia Institute of Technology, and Dr. Feifei Pan at the University of North Texas." | ABSTRACT: "We introduce and validate the use of commercially available human mobility datasets based on cell phone locations to estimate visitation to natural areas. By combining this data with on-the-ground observations of visitation to water recreation areas in New England, we fit a model to estimate daily visitation for four months to more than 500 sites. The results show the potential for this new big data source of human mobility to overcome limitations in traditional methods of estimating visitation and to provide consistent information at policy-relevant scales. However, the data providers’ opaque and rapidly developing methods for processing locational information required a calibration and validation against data collected by traditional means to confidently reproduce the desired estimates of visitation. We found that with this calibration, the high-resolution information in both space and time provided by cell phone location-derived data creates opportunities for developing next-generation models of human interactions with the natural environment. " | [Enter up to 65000 characters] | IPaC is a project planning tool that streamlines the USFWS environmental review process. Explores species and habitat: See if any listed species, critical habitat, migratory birds or other natural resources may be impacted by your project. Using the map tool, explore other resources in your location, such as wetlands, wildlife refuges, GAP land cover, and other important biological resources. Conduct a regulatory review: Log in and define a project to get an official species list and evaluate potential impacts on resources managed by the U.S. Fish and Wildlife Service. Follow IPaC's Endangered Species Act (ESA) Review process—a streamlined, step-by-step consultation process available in select areas for certain project types, agencies, and species. Build a Consultation Package: Consultation Package Builder (CPB) replaces and improves on the original Impact Analysis by providing an interactive, step-by-step process to help you prepare a full consultation package leveraging U.S. Fish and Wildlife Service data and recommendations, including conservation measures designed to help you avoid or minimize effects to listed species. | ABSTRACT: "Design/methodology/approach – The approach was exploratory using qualitative methods. Face-to-face interviews were conducted with school principals and teachers as well as professionals from the environmental education industry. Interviews focused on the perceived benefits for children’s health from school activities involving hands-on contact with nature. Findings – Hands-on contact with nature is perceived by educators to improve self-esteem, engagement with school and a sense of empowerment, among other benefits. Different types of activities are perceived to have different outcomes. A model is proposed to illustrate the findings." |
Specific Policy or Decision Context Cited
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European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | Not reported | AUTHOR DESCRIPTION: "Comprehensive habitat and viability assessments. . . [more rigoursly defined] can clarify debate of goals for recovery of large carnivores"; Endangered Species Act and related litigation | None identified | None identified | None identified | None identified | Not applicable | None identified | None identified | None identified | None identified | Future rock lobster fisheries management | climate change | None identified | None identified | None Identified | None identified | None identified | None | Determination of Effects on ESA listed taxa. | None |
Biophysical Context
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Not applicable | Upper Mississipi River basin, elevation 142-194m, | Great Plains to Pacific Coast, northern Rocky Mountains, Pacific Northwest | No additional description provided | No additional description provided | Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm. | Five soil types including Löss, Fluvial clay, Peat, Sand, and Silty Loam. Five land-use types including Pasture, Arable farming, Semi-natural grassland, Heathland, and Forest. | Submerged Aquatic Vegetation (SAV), eelgrass | No additional description provided | None applicable | No additional description provided | Agricultural plain, hills, gulleys, forest, grassland, Central China | No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | wetlands | No additional description provided | Streams and Rivers | No additional description provided | Natural area water bodies | None | N/A | N/A |
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 | Population growth, road development (density) on public vs private land |
Base case; increase pervious area tree cover to 40%; increase impervious area tree cover to 40%; double impervious area to 60%; halve pervious area tree cover to 6%; double pervious area tree cover to 24% and increase pervious area tree cover to 20%. ?Comment:Base case is existing conditions. |
No scenarios presented | Four carbon-planting systems including hardwood and mallee monoculture plantings, and mixed species ecological carbon plantings | No scenarios presented | Essential or Facultative habitat | No scenarios presented | N/A | No scenarios presented | Land use change | Fisheries exploitation; fishing vulnerability (of age classes) | fertilization | N/A | No scenarios presented | N/A | No scenarios presented | N/A | None | N/A | N/A |
EM ID
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EM-94 | EM-97 |
EM-98 ![]() |
EM-109 ![]() |
EM-119 |
EM-129 ![]() |
EM-178 | EM-185 | EM-317 | EM-368 | EM-448 |
EM-480 ![]() |
EM-541 ![]() |
EM-598 | EM-617 |
EM-709 ![]() |
EM-848 | EM-883 | EM-943 | EM-961 | EM-967 | EM-980 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Runs are differentiated based on the the average annual biomass flux and carbon sequestration from two types of carbon plantings: 1) Tree-based monocultures of three different species (i.e., monoculture carbon planting) and 2) Diverse plantings of nine different native tree and shrub species (i.e., ecological carbon planting) |
Method + Application | Method + Application | Method + Application | Method Only | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application | 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 | Application of existing 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 | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | None | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
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EM-94 | EM-97 |
EM-98 ![]() |
EM-109 ![]() |
EM-119 |
EM-129 ![]() |
EM-178 | EM-185 | EM-317 | EM-368 | EM-448 |
EM-480 ![]() |
EM-541 ![]() |
EM-598 | EM-617 |
EM-709 ![]() |
EM-848 | EM-883 | EM-943 | EM-961 | EM-967 | EM-980 |
EM Temporal Extent
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2000 | 1980-2006 | 2000-2025 | 2000 | 2000 | 2009-2050 | 1999-2005 | 1993-2011 | 1950-2007 | Not applicable | 2006-2007, 2010 | 1969-2011 | 1986-2115 | 1961-1990 | Not applicable | 2007-2008 | 1991-1994 |
Not applicable ?Comment:User defined model duration. |
2017 | None | Not applicable | 2002-2005 |
EM Time Dependence
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time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | None | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | future time | both | Not applicable | future time | Not applicable | past time | Not applicable | future time | Not applicable | past time | future time | both | Not applicable | Not applicable | Not applicable | Not applicable | past time | None | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | discrete | discrete | Not applicable | discrete | Not applicable | discrete | Not applicable | discrete | Not applicable | discrete | discrete | discrete | Not applicable | Not applicable | Not applicable | discrete | discrete | None | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | 1 | 1 | Not applicable | 1 | Not applicable | 1 | Not applicable | 1 | Not applicable | 1 | 1 | 1 | Not applicable | Not applicable | Not applicable | 1 | 1 | None | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Year | Hour | Not applicable | Month | Not applicable | Year | Not applicable | Year | Not applicable | Year | Year | Day | Not applicable | Not applicable | Not applicable | Day | Day | None | Not applicable | Not applicable |
EM ID
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EM-94 | EM-97 |
EM-98 ![]() |
EM-109 ![]() |
EM-119 |
EM-129 ![]() |
EM-178 | EM-185 | EM-317 | EM-368 | EM-448 |
EM-480 ![]() |
EM-541 ![]() |
EM-598 | EM-617 |
EM-709 ![]() |
EM-848 | EM-883 | EM-943 | EM-961 | EM-967 | EM-980 |
Bounding Type
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Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Geopolitical | Physiographic or Ecological | Geopolitical | Physiographic or ecological | Physiographic or ecological | Not applicable | Physiographic or ecological | Watershed/Catchment/HUC | Geopolitical | Point or points | Not applicable | Multiple unrelated locations (e.g., meta-analysis) | Other | Not applicable | Point or points | Multiple unrelated locations (e.g., meta-analysis) | Not applicable | Geopolitical |
Spatial Extent Name
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EU-27 | East Fork Kaskaskia River watershed basin | Western United States | Dead Run Catchement, Baltimore, MD | The EU-25 plus Switzerland and Norway | Agricultural districts of the state of South Australia | The Netherlands | Chesapeake Bay | Puget Sound Region | Not applicable | Coastal zone surrounding St. Croix | Yangjuangou catchment | Table Mountain National Park Marine Protected Area | Oak Park Research centre | Not applicable | East Midlands | Not applicable | Not applicable | Cape Cod | Western & Eastern Corn Belt Plains | Not applicable | Melbourne |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 10-100 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 10,000-100,000 km^2 | 10,000-100,000 km^2 | 10,000-100,000 km^2 | Not applicable | 100-1000 km^2 | 1-10 km^2 | 100-1000 km^2 | 1-10 ha | Not applicable | 1000-10,000 km^2. | Not applicable | Not applicable | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | Not applicable | 10-100 km^2 |
EM ID
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EM-94 | EM-97 |
EM-98 ![]() |
EM-109 ![]() |
EM-119 |
EM-129 ![]() |
EM-178 | EM-185 | EM-317 | EM-368 | EM-448 |
EM-480 ![]() |
EM-541 ![]() |
EM-598 | EM-617 |
EM-709 ![]() |
EM-848 | EM-883 | EM-943 | EM-961 | EM-967 | EM-980 |
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 lumped (in all cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:pixel is likely 30m x 30m |
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 distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:User defined scale, from plot to basin size. |
spatially distributed (in at least some cases) | None | spatially lumped (in all cases) | Not applicable |
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 | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | 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) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | None | Not applicable | Not applicable |
Spatial Grain Size
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10 km x 10 km | 1 km^2 | 504 km^2 | irregular topographically delineated similar units | 1 km x 1 km | 1 ha x 1 ha | 100 m x 100 m | Not applicable | 200m x 200m | Not specified | 10 m x 10 m | 30m x 30m | Not applicable | Not applicable | Not reported | multiple unrelated locations | stream reach | user defined | water feature edge (beach) | None | Not applicable | Not applicable |
EM ID
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EM-94 | EM-97 |
EM-98 ![]() |
EM-109 ![]() |
EM-119 |
EM-129 ![]() |
EM-178 | EM-185 | EM-317 | EM-368 | EM-448 |
EM-480 ![]() |
EM-541 ![]() |
EM-598 | EM-617 |
EM-709 ![]() |
EM-848 | EM-883 | EM-943 | EM-961 | EM-967 | EM-980 |
EM Computational Approach
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Analytic | Numeric | Numeric | Numeric | Logic- or rule-based | Numeric | Analytic | Analytic | Analytic | Numeric | Analytic | Numeric | Numeric | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | * | Other or unclear (comment) | Numeric |
EM Determinism
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deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | None | Not applicable |
other or unclear (comment) ?Comment:A review of interview questions and answers after grouping by teachers/principals and industry folks in education |
Statistical Estimation of EM
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None |
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EM ID
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EM-94 | EM-97 |
EM-98 ![]() |
EM-109 ![]() |
EM-119 |
EM-129 ![]() |
EM-178 | EM-185 | EM-317 | EM-368 | EM-448 |
EM-480 ![]() |
EM-541 ![]() |
EM-598 | EM-617 |
EM-709 ![]() |
EM-848 | EM-883 | EM-943 | EM-961 | EM-967 | EM-980 |
Model Calibration Reported?
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No | No | Unclear | Yes | No | Yes | No | Yes | Yes |
Yes ?Comment:Annual Yield can be calibrated with actual yield based up 10 year average input data though this was an "optional" part of the model. Calibrate with total precipitation and potential evapotranspiration. Before the calibration process is commenced, the modelers suggest performing a sensitivity analysis with the observed runoff data to define the parameters that influence model outputs the most. The calibration can then focus on highly sensitive parameters followed by less sensitive ones. |
Yes | No | No | Yes | Not applicable | Not applicable | Not applicable | Not applicable | Yes | None | Not applicable | No |
Model Goodness of Fit Reported?
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No | No | No | Yes | No | No | No | Yes | No | Not applicable | No |
Yes ?Comment:p value: p<0.001 |
No |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
Not applicable | Not applicable | Not applicable | Not applicable |
Yes ?Comment:Random forest model performance statistics |
None | Not applicable | No |
Goodness of Fit (metric| value | unit)
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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 |
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None | None | None |
Model Operational Validation Reported?
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Yes | Yes | No | Yes | Yes | No | No | Yes | No | No | Yes | No |
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 | Not applicable | Yes | None | Not applicable | Unclear |
Model Uncertainty Analysis Reported?
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No | Yes | No | Unclear | No | No | No | Yes | No | No | No | No | No | No | Not applicable | Not applicable | Yes | Not applicable | Unclear | None | Not applicable | No |
Model Sensitivity Analysis Reported?
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No | Unclear |
Yes ?Comment:No results reported. Just a general statement was made about PATCH sensitivity and that demographic parameters are more sensitive that variation in other parameters such as dispersadistance . Reference made to another publication Carroll et al. 2003. Use of population viability analysis and reserve slelection algorithms in regional conservation plans. Ecol. App. 13:1773-1789. |
No | No | No | No | Yes | No | Not applicable | No | No | No | No | Not applicable | Not applicable | Yes | Not applicable | Yes | None | Not applicable |
Unclear ?Comment:Identified two groups to interview but do not know if that was BPJ or split the group after initial review of data. Appears to be apriori |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Unclear | Not applicable | Not applicable | Not applicable | Not applicable | Yes | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Yes | Not applicable | Unclear | 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-98 ![]() |
EM-109 ![]() |
EM-119 |
EM-129 ![]() |
EM-178 | EM-185 | EM-317 | EM-368 | EM-448 |
EM-480 ![]() |
EM-541 ![]() |
EM-598 | EM-617 |
EM-709 ![]() |
EM-848 | EM-883 | EM-943 | EM-961 | EM-967 | EM-980 |
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None | None |
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None |
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None |
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Comment:No specific location but developed in United States |
None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-94 | EM-97 |
EM-98 ![]() |
EM-109 ![]() |
EM-119 |
EM-129 ![]() |
EM-178 | EM-185 | EM-317 | EM-368 | EM-448 |
EM-480 ![]() |
EM-541 ![]() |
EM-598 | EM-617 |
EM-709 ![]() |
EM-848 | EM-883 | EM-943 | EM-961 | EM-967 | EM-980 |
None | None | None | None | None | None | None |
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None | None |
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None |
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None | None | None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-94 | EM-97 |
EM-98 ![]() |
EM-109 ![]() |
EM-119 |
EM-129 ![]() |
EM-178 | EM-185 | EM-317 | EM-368 | EM-448 |
EM-480 ![]() |
EM-541 ![]() |
EM-598 | EM-617 |
EM-709 ![]() |
EM-848 | EM-883 | EM-943 | EM-961 | EM-967 | EM-980 |
Centroid Latitude
em.detail.ddLatHelp
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50.53 | 38.69 | 39.88 | 39.31 | 50.53 | -34.9 | 52.37 | 36.99 | 48 | -9999 | 17.73 | 36.7 | -34.18 | 52.86 | Not applicable | 52.22 | Not applicable | Not applicable | 41.72 | None | Not applicable | -37.77 |
Centroid Longitude
em.detail.ddLongHelp
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7.6 | -89.1 | -113.81 | -76.74 | 7.6 | 138.7 | 4.88 | -75.95 | -123 | -9999 | -64.77 | 109.52 | 18.35 | 6.54 | Not applicable | -0.91 | Not applicable | Not applicable | -70.29 | None | Not applicable | 144.96 |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | None provided | Not applicable | WGS84 | Not applicable | Not applicable | WGS84 | None | Not applicable | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Provided | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Provided | Provided | Provided | Not applicable | Estimated | Not applicable | Not applicable | Estimated | None | Not applicable | Estimated |
EM ID
em.detail.idHelp
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EM-94 | EM-97 |
EM-98 ![]() |
EM-109 ![]() |
EM-119 |
EM-129 ![]() |
EM-178 | EM-185 | EM-317 | EM-368 | EM-448 |
EM-480 ![]() |
EM-541 ![]() |
EM-598 | EM-617 |
EM-709 ![]() |
EM-848 | EM-883 | EM-943 | EM-961 | EM-967 | EM-980 |
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) | Rivers and Streams | Ground Water | Created Greenspace | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | None | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Forests | Atmosphere | Rivers and Streams | Near Coastal Marine and Estuarine | Agroecosystems | Near Coastal Marine and Estuarine | Agroecosystems | Inland Wetlands | Created Greenspace | Grasslands | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Lakes and Ponds | Near Coastal Marine and Estuarine | 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 | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Tundra | Ice and Snow | Atmosphere | Not applicable |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Streams and near upstream environments | Row crop agriculture in Kaskaskia river basin | Not reported | Urban watershed | Not applicable | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Not applicable | Yes | Terrestrial environment surrounding a large estuary | Watershed | Coral reefs | Loess plain | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | farm pasture | Restored wetlands | restored landfills and grasslands | benthic habitat | Terrestrial | beaches | Plains | None | N/A |
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 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 corresponds to the Environmental Sub-class | Yes | Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale 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 | Not applicable | Not applicable |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-94 | EM-97 |
EM-98 ![]() |
EM-109 ![]() |
EM-119 |
EM-129 ![]() |
EM-178 | EM-185 | EM-317 | EM-368 | EM-448 |
EM-480 ![]() |
EM-541 ![]() |
EM-598 | EM-617 |
EM-709 ![]() |
EM-848 | EM-883 | EM-943 | EM-961 | EM-967 | EM-980 |
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Species | Community | Not applicable | Species | Not applicable | Yes | Not applicable | Not applicable | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Not applicable | Individual or population, within a species |
Other (Comment) ?Comment:Community metrics of tolerance, food groups, sensitivity, taxa richness, diversity |
Not applicable | Not applicable | None |
Other (Comment) ?Comment:ESA designations include species and Ecological Significan Units of species |
Not applicable |
Taxonomic level and name of organisms or groups identified
EM-94 | EM-97 |
EM-98 ![]() |
EM-109 ![]() |
EM-119 |
EM-129 ![]() |
EM-178 | EM-185 | EM-317 | EM-368 | EM-448 |
EM-480 ![]() |
EM-541 ![]() |
EM-598 | EM-617 |
EM-709 ![]() |
EM-848 | EM-883 | EM-943 | EM-961 | EM-967 | EM-980 |
None Available | None Available |
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None Available | None Available |
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None Available | None Available | None Available | None Available | None Available | None Available |
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None Available | None Available |
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None Available | None Available | None Available | None Available | 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-98 ![]() |
EM-109 ![]() |
EM-119 |
EM-129 ![]() |
EM-178 | EM-185 | EM-317 | EM-368 | EM-448 |
EM-480 ![]() |
EM-541 ![]() |
EM-598 | EM-617 |
EM-709 ![]() |
EM-848 | EM-883 | EM-943 | EM-961 | EM-967 | EM-980 |
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None |
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None |
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None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-94 | EM-97 |
EM-98 ![]() |
EM-109 ![]() |
EM-119 |
EM-129 ![]() |
EM-178 | EM-185 | EM-317 | EM-368 | EM-448 |
EM-480 ![]() |
EM-541 ![]() |
EM-598 | EM-617 |
EM-709 ![]() |
EM-848 | EM-883 | EM-943 | EM-961 | EM-967 | EM-980 |
None |
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None | None | None |
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None | None |
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None | None | None |
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None |
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