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-12 ![]() |
EM-71 | EM-80 | EM-84 |
EM-98 ![]() |
EM-104 |
EM-129 ![]() |
EM-131 | EM-142 | EM-337 | EM-451 | EM-457 |
EM-485 ![]() |
EM-627 |
EM-660 ![]() |
EM-719 ![]() |
EM-891 | EM-961 |
EM Short Name
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Evoland v3.5 (bounded growth), Eugene, OR, USA | Community flowering date, Central French Alps | Agronomic ES and plant traits, Central French Alps | ACRU, South Africa | PATCH, western USA | SPARROW, Northeastern USA | 3-PG, South Australia | InVEST marine water quality, Hood Canal, WA, USA | EnviroAtlas - Water recharge | Rate of Fire Spread | Ease of reef access, St. Croix, USVI | Visitation to reef dive sites, St. Croix, USVI | Yasso07 v1.0.1, Switzerland, site level | N removal by wetland restoration, Midwest, USA | RUM: Valuing fishing quality, Michigan, USA | Seed mix for native plant establishment, IA, USA | Home ownership, Great Lakes, USA | Drainage water recycling, Midwest, USA |
EM Full Name
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Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA | Community weighted mean flowering date, Central French Alps | Agronomic ecosystem service estimated from plant functional traits, Central French Alps | ACRU (Agricultural Catchments Research Unit), South Africa | PATCH (Program to Assist in Tracking Critical Habitat), western USA | SPARROW (SPAtially Referenced Regressions On Watershed Attributes), Northeastern USA | 3-PG (Physiological Principles Predicting Growth), South Australia | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) marine water quality, Hood Canal, WA, USA | US EPA EnviroAtlas - Annual water recharge by tree cover; Example is shown for Durham NC and vicinity, USA | Rate of Fire Spread | Ease of access (to reef), St. Croix, USVI | Visitation to dive sites (reef), St. Croix, USVI | Yasso07 v1.0.1 forest litter decomposition, Switzerland, site level | Nitrate removal by potential wetland restoration, Mississippi River subbasins, USA | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | Cost-effective seed mix design for native plant establishment, Iowa, USA | Human well being indicator - home ownership, Great Lakes waterfront, USA | Drainage water recycling, Midwest, US |
EM Source or Collection
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Envision | EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | US EPA | US EPA | None | InVEST |
US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Hydro model. |
None | US EPA | US EPA | None | None | None | None | US EPA | None |
EM Source Document ID
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47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
260 | 260 | 271 | 2 | 86 | 243 | 205 |
223 ?Comment:Parameter default values used in the i-Tree Hydro model were obtained from the i-Tree website (Document ID 198, EM 137). |
306 | 335 | 335 | 343 |
370 ?Comment:Final project report to U.S. Department of Agriculture; Project number: IOW06682. December 2006. |
382 ?Comment:Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010. |
394 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
446 |
Document Author
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Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Carroll, C, Phillips, M. K. , Lopez-Gonzales, C. A and Schumaker, N. H. | Moore, R. B., Johnston, C.M., Smith, R. A. and Milstead, B. | Crossman, N. D., Bryan, B. A., and Summers, D. M. | Toft, J. E., Burke, J. L., Carey, M. P., Kim, C. K., Marsik, M., Sutherland, D. A., Arkema, K. K., Guerry, A. D., Levin, P. S., Minello, T. J., Plummer, M., Ruckelshaus, M. H., and Townsend, H. M. | US EPA Office of Research and Development - National Exposure Research Laboratory | Rothermel, Richard C. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Didion, M., B. Frey, N. Rogiers, and E. Thurig | Crumpton, W. G., G. A. Stenback, B. A. Miller, and M. J. Helmers | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | Meissen, J. | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | Reinhart, B.D., Frankenberger, J.R., Hay, C.H., and Helmers, J.M. |
Document Year
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2008 | 2011 | 2011 | 2008 | 2006 | 2011 | 2011 | 2013 | 2013 | 1972 | 2014 | 2014 | 2014 | 2006 | 2014 | 2018 | None | 2019 |
Document Title
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Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Mapping ecosystem services for planning and management | Defining recovery goals and strategies for endangered species: The wolf as a case study | Source and delivery of nutrients to receiving waters in the northeastern and mid-Atlantic regions of the United States | Carbon payments and low-cost conservation | From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA | EnviroAtlas - Featured Community | A Mathematical model for predicting fire spread in wildland fuels | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Validating tree litter decomposition in the Yasso07 carbon model | Potential benefits of wetland filters for tile drainage systems: Impact on nitrate loads to Mississippi River subbasins | Valuing recreational fishing quality at rivers and streams | Cost-effective seed mix design and first-year management | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | Simulated water quality and irrigation benefits from drainage wter recycling at two tile-drained sites in the U.S. Midwest |
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 | Documented, not peer reviewed | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Neither peer reviewed nor published (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed but unpublished (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 | Published journal manuscript | Published on US EPA EnviroAtlas website | Published USDA Forest Service report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published report | Journal manuscript submitted or in review | Published journal manuscript |
EM ID
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EM-12 ![]() |
EM-71 | EM-80 | EM-84 |
EM-98 ![]() |
EM-104 |
EM-129 ![]() |
EM-131 | EM-142 | EM-337 | EM-451 | EM-457 |
EM-485 ![]() |
EM-627 |
EM-660 ![]() |
EM-719 ![]() |
EM-891 | EM-961 |
http://evoland.bioe.orst.edu/ ?Comment:Software is likely available. |
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://www.csiro.au/products/3PGProductivity#a1 | https://www.naturalcapitalproject.org/invest/ | https://www.epa.gov/enviroatlas | http://firelab.org/project/farsite | Not applicable | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | |
Contact Name
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Michael R. Guzy | Sandra Lavorel | Sandra Lavorel | Roland E Schulze | Carlos Carroll | Richard Moore | Anders Siggins | J.E. Toft | EnviroAtlas Team | Charles McHugh | Susan H. Yee | Susan H. Yee | Markus Didion | William G. Crumpton | Richard Melstrom | Justin Meissen | Ted Angradi | Benjamin Reinhart |
Contact Address
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Oregon State University, Dept. of Biological and Ecological Engineering | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | School of Bioresources Engineering and Environmental Hydrology, University of Natal, South Africa | Klamath Center for Conservation Research, Orleans, CA 95556 | U.S. Environmental Protection Agency, 27 Tarzwell Drive, Narragansett, Rhode Island 02882 | Not reported | Not reported | Not reported | RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808 | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Dept. of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA 50011 | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | Tallgrass Prairie Center, University of Northern Iowa | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | Agricultural & Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA |
Contact Email
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Not reported | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | schulzeR@nu.ac.za | carlos@cklamathconservation.org | rmoore@usgs.gov | Anders.Siggins@csiro.au | jetoft@stanford.edu | enviroatlas@epa.gov | cmchugh@fs.fed.us | yee.susan@epa.gov | yee.susan@epa.gov | markus.didion@wsl.ch | crumpton@iastate.edu | melstrom@okstate.edu | Not reported | tedangradi@gmail.com | breinhar@purdue.edu |
EM ID
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EM-12 ![]() |
EM-71 | EM-80 | EM-84 |
EM-98 ![]() |
EM-104 |
EM-129 ![]() |
EM-131 | EM-142 | EM-337 | EM-451 | EM-457 |
EM-485 ![]() |
EM-627 |
EM-660 ![]() |
EM-719 ![]() |
EM-891 | EM-961 |
Summary Description
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**Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The Agronomic ecosystem service map is a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to agronomic ecosystem services are based on stakeholders’ perceptions, given positive or negative contributions." | AUTHOR'S DESCRIPTION (Doc ID 272): "ACRU is a daily timestep, physical conceptual and multipurpose model structured to simulate impacts of land cover/ use change. The model can output, inter alia, components of runoff, irrigation supply and demand, reservoir water budgets as well as sediment and crop yields." AUTHOR'S DESCRIPTION (Doc ID 271): "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…The total benefit to people of water supply is a function of both the quantity and quality with the ecosystem playing a key role in the latter. However, due to the lack of suitable national scale data on water quality for quantifying the service, runoff was used as an estimate of the benefit where runoff is the total water yield from a watershed including surface and subsurface flow. This assumes that runoff is positively correlated with quality, which is the case in South Africa (Allanson et al., 1990)…In South Africa, water resources are mapped in water management areas called catchments (vs. watersheds) where a catchment is defined as the area of land that is drained by a single river system, including its tributaries (DWAF, 2004). There are 1946 quaternary (4th order) catchments in South Africa, the smallest is 4800 ha and the average size is 65,000 ha. Schulze (1997) modelled annual runoff for each quaternary catchment. During modelling of runoff, he used rainfall data collected over a period of more than 30 years, as well as data on other climatic factors, soil characteristics and grassland as the land cover. In this study, median annual simulated runoff was used as a measure of surface water supply. The volume of runoff per quaternary catchment was calculated for surface water supply. The range (areas with runoff of 30 million m^3 or more) and hotspots (areas with runoff of 70 million m^3 or more) were defined using a combination of statistics and expert inputs due to a lack of published thresholds in the literature." | **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)." | AUTHOR'S DESCRIPTION: "SPAtially Referenced Regressions On Watershed attributes (SPARROW) nutrient models were developed for the Northeastern and Mid-Atlantic (NE US) regions of the United States to represent source conditions for the year 2002. The model developed to examine the source and delivery of nitrogen to the estuaries of nine large rivers along the NE US Seaboard indicated that agricultural sources contribute the largest percentage (37%) of the total nitrogen load delivered to the estuaries" | 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)." | Marine Water Quality Model. Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "We used outputs from the freshwater models as inputs to the marine water quality model.We adapted a box model that has been successfully applied in Puget Sound (Babson et al., 2006; Sutherland et al., 2011) to simulate seasonal and interannual variations in salinity, water temperature, and nitrates in the Canal." (p. 4) | The Water Recharge model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. METADATA ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina... runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA DESCRIPTION: The i-Tree Hydro model estimates the effects of tree and impervious cover on hourly stream flow values for a watershed (Wang et al 2008). The model was calibrated using hourly stream flow data to yield the best fit between model and measured stream flow results. Calibration coefficients (0-1 with 1.0 = perfect fit) were calculated for peak flow, base flow, and balance flow (peak and base). To estimate the effect of trees at the block group level for Durham, the Hydro model was run for: Gauging Station Name: SANDY CREEK AT CORNWALLIS RD NEAR DURHAM, NC, Gauging Station Location: 35°58'59.6",-78°57'24.5", Gauging Station Number: 0209722970. After calibration, the model was run a number of times under various conditions to see how the stream flow would respond given varying tree and impervious cover in the watershed. To estimate block group effects, the block group was assumed to act similarly to the watershed in terms of hydrologic effects. To estimate the block group effect, the outputs of the watershed were determined for each possible combination of tree cover (0-100%) and impervious cover (0-100%). Thus, there were a total of 10,201 possible responses (101 x 101). For each block group, the percent tree cover and percent impervious cover combination (e.g., 30% tree / 20% impervious) was matched to the appropriate watershed hydrologic response output for that combination. The hydrologic response outputs were calculated as either percent change or absolute change in units of cubic meters of water per square meter of land area for water flow or kg of pollutant per square meter of land area for pollutants. These per square meter values were multiplied by the square meters of land area in the block group to estimate the effects at the block group level. | ABSTRACT: "The development of a mathematical model for predicting rate of fire spread and intensity applicable to a wide range of wildland fuels is presented from the conceptual stage through evaluation and demonstration of results to hypothetical fuel models. The model was developed for and is now being used as a basis for appraising fire spread and intensity in the National Fire Danger Rating System. The initial work was done using fuel arrays composed of uniform size particles. Three fuel sizes were tested over a wide range of bulk densities. These were 0.026-inch-square cut excelsior, 114-inch sticks, and 112-inch sticks. The problem of mixed fuel sizes was then resolved by weighting the various particle sizes that compose actual fuel arrays by either surface area or loading, depending upon the feature of the fire being predicted. The model is complete in the sense that no prior knowledge of a fuel's burning characteristics is required. All that is necessary are inputs describing the physical and chemical makeup of the fuel and the environmental conditions in which it is expected to burn. Inputs include fuel loading, fuel depth, fuel particle surface-area-to-volume ratio, fuel particle heat content, fuel particle moisture and mineral content, and the moisture content at which extinction can be expected. Environmental inputs are mean wind velocity and slope of terrain. For heterogeneous mixtures, the fuel properties are entered for each particle size. The model as originally conceived was for dead fuels in a uniform stratum contiguous to the ground, such as litter or grass. It has been found to be useful, however, for fuels ranging from pine needle litter to heavy logging slash and for California brush fields." **FARSITE4 will no longer be supported or available for download or further supported. FlamMap6 now includes FARSITE.** | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…Synthesis of scientific literature and expert opinion can be used to estimate the relative potential for recreational opportunities across different benthic habitat types (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to recreational opportunities as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative recreational opportunity j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A.palmata, Montastraea reef, patch reef, and dense or sparse gorgonians), and Mij is the magnitude associated with each habitat for a given metric j: (1) ease of access for education" | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…Pendleton (1994) used field observations of dive sites to model potential impacts on local economies due to loss of dive tourism with reef degradation. A key part of the diver choice model is a fitted model of visitation to dive sites described by Visitation to dive sites = 2.897+0.0701creef -0.133D+0.0417τ where creef is percent coral cover, D is the time in hours to the dive site, which we estimate using distance from reef to shore and assuming a boat speed of 5 knots or 2.57ms-1, and τ is a dummy variable for the presence of interesting topographic features. We interpret τ as dramatic changes in bathymetry, quantified as having a standard deviation in depth among grid cells within 30 m that is greater than the75th percentile across all grid cells. Because our interpretation of topography differed from the original usage of “interesting features”, we also calculated dive site visitation assuming no contribution of topography (τ=0). Unsightly coastal development, an additional but non-significant variable in the original model, was assumed to be zero for St. Croix." | ABSTRACT: "...We examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to... (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests; and (iii) evaluate the suitability of Yasso07 for regional and national scale applications in Swiss forests." AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "The decomposition of below- and aboveground litter was studied over 10 years on five forest sites in Switzerland…" "At the time of this study, three parameter sets have been developed and published:... (3): Rantakari et al., 2012 (henceforth P12)… For the development of P12, Rantakari et al. (2012) obtained a subset of the previously used data which was restricted to European sites." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root lit-ter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The r | ABSTRACT: "The primary objective of this project was to estimate the nitrate reduction that could be achieved using restored wetlands as nitrogen sinks in tile-drained regions of the upper Mississippi River (UMR) and Ohio River basins. This report provides an assessment of nitrate concentrations and loads across the UMR and Ohio River basins and the mass reduction of nitrate loading that could be achieved using wetlands to intercept nonpoint source nitrate loads. Nitrate concentration and stream discharge data were used to calculate stream nitrate loading and annual flow-weighted average (FWA) nitrate concentrations and to develop a model of FWA nitrate concentration based on land use. Land use accounts for 90% of the variation among stations in long term FWA nitrate concentrations and was used to estimate FWA nitrate concentrations for a 100 ha grid across the UMR and Ohio River basins. Annual water yield for grid cells was estimated by interpolating over selected USGS monitoring station water yields across the UMR and Ohio River basins. For 1990 to 1999, mass nitrate export from each grid area was estimated as the product of the FWA nitrate concentration, water yield and grid area. To estimate potential nitrate removal by wetlands across the same grid area, mass balance simulations were used to estimate percent nitrate reduction for hypothetical wetland sites distributed across the UMR and Ohio River basins. Nitrate reduction was estimated using a temperature dependent, area-based, first order model. Model inputs included local temperature from the National Climatic Data Center and water yield estimated from USGS stream flow data. Results were used to develop a nonlinear model for percent nitrate removal as a function of hydraulic loading rate (HLR) and temperature. Mass nitrate removal for potential wetland restorations distributed across the UMR and Ohio River basin was estimated based on the expected mass load and the predicted percent removal. Similar functions explained most of the variability in per cent and mass removal reported for field scale experimental wetlands in the UMR and Ohio River basins. Results suggest that a 30% reduction in nitrate load from the UMR and Ohio River basins could be achieved using 210,000-450,000 ha of wetlands targeted on the highest nitrate contributing areas." AUTHOR'S DESCRIPTION: "Percent nitrate removal was estimated based on HLR functions (Figure 19) spanning a 3 fold range in loss rate coefficient (Crumpton 2001) and encompassing the observed performance reported for wetlands in the UMR and Ohio River basins (Table 2, Figure 7). The nitrate load was multiplied by the expected percent nitrate removal to estimate the mass removal. This procedure was repeated for each restoration scenario each year in the simulation period (1990 to 1999)… for a scenario with a wetland/watershed area ratio of 2%. These results are based on the assumption that the FWA nitrate concentration versus percent row crop r | ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. " | AUTHOR'S DESCRIPTION: "Restoring ecosystem services at scale requires executing conservation programs in a way that is resource and cost efficient as well as ecologically effective…Seed mix design is one of the largest determinants of project cost and ecological outcomes for prairie reconstructions. In particular, grass-to-forb seeding ratio affects cost since forb seed can be much more expensive relative to grass species (Prairie Moon Nursery 2012). Even for seed mixes with the same overall seeding rates, a mix with a low grass-to-forb seeding ratio is considerably more expensive than one with a high grass-to-forb ratio. Seeding rates for different plant functional groups that are too high or low may also adversely affect ecological outcomes…First-year management may also play a role in cost-effective prairie reconstruction. Post-agricultural sites where restoration typically occurs are often quickly dominated by fast-growing annual weeds by the time sown prairie seeds begin germinating (Smith et al. 2010)… Williams and others (2007) showed that prairie seedlings sown into established warm-season grasses were reliant on high light conditions created by frequently mowing tall vegetation in order to survive in subsequent years…Our objective was to compare native plant establishment and cost effectiveness with and without first-year mowing for three different seed mixes that differed in grass to forb ratio and soil type customization. With knowledge of plant establishment, cost effectiveness, and mowing management outcomes, conservation practitioners will be better equipped to restore prairie efficiently and successfully." | ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context. " | [Enter up to 65000 characters] |
Specific Policy or Decision Context Cited
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Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None identified | None identified | None identified | 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 | water-quality assessment, total maximum daily load(TMDL) determination | None identified | Land use change | None identified | None identified | None identified | None identified | None identified | None identified | None identified | Seed mix design and management practices for native plant restoration | None identified | None |
Biophysical Context
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No additional description provided | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | Great Plains to Pacific Coast, northern Rocky Mountains, Pacific Northwest | Norteneastern region (U.S.); Mid-Atlantic region (U.S.) | 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. | No additional description provided | Range of tree and impervious covers in urban setting | Not applicable | No additional description provided | No additional description provided | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | No additional description provided | stream and river reaches of Michigan | The soils underlying the study site are primarily poorly drained Clyde clay loams, with a minor component of somewhat poorly drained Floyd loams in the northwest (NRCS 2016). Topographically, the study site is level, and slopes do not exceed 5% grade. Land use prior to this experiment was agricultural, with corn and soybeans consistently grown in rotation at the site. | Waterfront districts on south Lake Michigan and south lake Erie | None |
EM Scenario Drivers
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Five scenarios that include urban growth boundaries and various combinations of unconstrainted development, fish conservation, agriculture and forest reserves. ?Comment:Additional alternatives included adding agricultural and forest reserves, and adding or removing urban growth boundaries to the three main scenarios. |
No scenarios presented | No scenarios presented | No scenarios presented | Population growth, road development (density) on public vs private land | No scenarios presented | Four carbon-planting systems including hardwood and mallee monoculture plantings, and mixed species ecological carbon plantings | future land use and land cover; Climate change | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | More conservative, average and less conservative nitrate loss rate | targeted sport fish biomass | No scenarios presented | N/A | None |
EM ID
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EM-12 ![]() |
EM-71 | EM-80 | EM-84 |
EM-98 ![]() |
EM-104 |
EM-129 ![]() |
EM-131 | EM-142 | EM-337 | EM-451 | EM-457 |
EM-485 ![]() |
EM-627 |
EM-660 ![]() |
EM-719 ![]() |
EM-891 | EM-961 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | 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 (multiple runs exist) | Method + Application | Method Only | Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Model runs are for different sites (Beatenberg, Vordemwald, Bettlachstock, Schanis, and Novaggio) differentiated by climate and forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). |
Method + Application (multiple runs exist) | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | None |
New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model |
Application of existing model ?Comment:EnviroAtlas uses an application of the i-Tree Hydro model. |
New or revised model | Application of existing model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | None |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-12 ![]() |
EM-71 | EM-80 | EM-84 |
EM-98 ![]() |
EM-104 |
EM-129 ![]() |
EM-131 | EM-142 | EM-337 | EM-451 | EM-457 |
EM-485 ![]() |
EM-627 |
EM-660 ![]() |
EM-719 ![]() |
EM-891 | EM-961 |
Document ID for related EM
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Doc-47 | Doc-313 | Doc-314 ?Comment:Doc 183 is a secondary source for the Evoland model. |
Doc-260 | Doc-269 | Doc-260 | Doc-270 |
Doc-272 ?Comment:Doc ID 272 was also used as a source document for this EM |
Doc-328 | Doc-337 | None | Doc-243 | Doc-246 | Doc-245 | None |
Doc-198 ?Comment:Parameter default values used in the i-Tree Hydro model were obtained from the i-Tree website (Document ID 198, EM 137). |
None | None | None | Doc-342 | Doc-343 | None | None | Doc-395 | Doc-422 | None |
EM ID for related EM
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EM-333 | EM-369 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-81 | EM-82 | EM-83 | None | EM-403 | EM-422 | None | None | None | EM-137 | EM-51 | None | None | None | EM-466 | EM-467 | EM-469 | EM-480 | None | None | EM-728 | EM-886 | EM-888 | EM-889 | EM-890 | EM-893 | EM-894 | EM-895 | None |
EM Modeling Approach
EM ID
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EM-71 | EM-80 | EM-84 |
EM-98 ![]() |
EM-104 |
EM-129 ![]() |
EM-131 | EM-142 | EM-337 | EM-451 | EM-457 |
EM-485 ![]() |
EM-627 |
EM-660 ![]() |
EM-719 ![]() |
EM-891 | EM-961 |
EM Temporal Extent
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1990-2050 | 2007-2008 | Not reported | 1950-1993 | 2000-2025 |
2002 ?Comment:Several nationwide database development and modeling efforts were necessary to create models consistent with 2002 conditions. |
2009-2050 | varies by run, see runs for values | 2008-2010 | Not applicable | 2006-2007, 2010 | 2006-2007, 2010 | 2000-2010 | 1973-1999 | 2008-2010 | 2015-2017 | 2022 | None |
EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | Not applicable | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-stationary | None |
EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | future time | future time | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | future time | Not applicable | Not applicable | Not applicable | None |
EM Time Continuity
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discrete | Not applicable | Not applicable | discrete | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | discrete | Not applicable | None |
EM Temporal Grain Size Value
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2 | Not applicable | Not applicable | 1 | 1 | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | 1 | Not applicable | None |
EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable | Day | Year | Not applicable | Month | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Day | Not applicable | Year | Not applicable | None |
EM ID
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EM-12 ![]() |
EM-71 | EM-80 | EM-84 |
EM-98 ![]() |
EM-104 |
EM-129 ![]() |
EM-131 | EM-142 | EM-337 | EM-451 | EM-457 |
EM-485 ![]() |
EM-627 |
EM-660 ![]() |
EM-719 ![]() |
EM-891 | EM-961 |
Bounding Type
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Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Physiographic or ecological | Geopolitical | Physiographic or Ecological | Physiographic or ecological | Geopolitical | Not applicable | Physiographic or ecological | Physiographic or ecological | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Other | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) |
Spatial Extent Name
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Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Central French Alps | Central French Alps | South Africa | Western United States | NE U.S. Regions | Agricultural districts of the state of South Australia | Hood Canal | Durham, NC and vicinity | Not applicable | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Switzerland | Upper Mississippi River and Ohio River basins | HUCS in Michigan | Iowa State University Northeast Research and Demonstration Farm | Great Lakes waterfront | Western & Eastern Corn Belt Plains |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | 100-1000 km^2 | Not applicable | 100-1000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | <1 ha | 1000-10,000 km^2. | 100,000-1,000,000 km^2 |
EM ID
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EM-12 ![]() |
EM-71 | EM-80 | EM-84 |
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EM-104 |
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EM-131 | EM-142 | EM-337 | EM-451 | EM-457 |
EM-485 ![]() |
EM-627 |
EM-660 ![]() |
EM-719 ![]() |
EM-891 | EM-961 |
EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. |
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) | Not applicable | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | None |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | Not applicable | None |
Spatial Grain Size
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varies | 20 m x 20 m | 20 m x 20 m | Distributed by catchments with average size of 65,000 ha | 504 km^2 | 30 x 30 m | 1 ha x 1 ha | Not reported | irregular | Not applicable | 10 m x 10 m | 10 m x 10 m | Not applicable | 1 km2 | reach in HUC | 20 ft x 28 ft | Not applicable | None |
EM ID
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EM-71 | EM-80 | EM-84 |
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EM-627 |
EM-660 ![]() |
EM-719 ![]() |
EM-891 | EM-961 |
EM Computational Approach
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Numeric | Analytic | Analytic | Numeric | Numeric | Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Numeric | Analytic | Numeric | * |
EM Determinism
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stochastic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | stochastic | deterministic | None |
Statistical Estimation of EM
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Comment:Agent based modeling results in response indices. |
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EM ID
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EM-12 ![]() |
EM-71 | EM-80 | EM-84 |
EM-98 ![]() |
EM-104 |
EM-129 ![]() |
EM-131 | EM-142 | EM-337 | EM-451 | EM-457 |
EM-485 ![]() |
EM-627 |
EM-660 ![]() |
EM-719 ![]() |
EM-891 | EM-961 |
Model Calibration Reported?
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Unclear | No | No | No | Unclear | Yes | Yes | No | Yes | Not applicable | Yes | Yes | No | No | No | Not applicable | No | None |
Model Goodness of Fit Reported?
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No | Yes | No | No | No |
Yes ?Comment:R-squared of .97 refers to the modelled loading whereas .83 refers to yield (see table 1, pg 972 for more information) |
No | No | Yes | Not applicable | No | No | No | No | Yes | Not applicable | No | None |
Goodness of Fit (metric| value | unit)
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None |
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None | None | None |
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None | None |
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None | None | None | None | None |
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None | None | None |
Model Operational Validation Reported?
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No | No | No | No | No | Yes | No | No | No | No | Yes | Yes | Yes |
No ?Comment:However, agreement of submodel and intermediate components; annual discharge (R2=0.79), and nitrate-N load (R2=0.74), based on GIS land use were determined in comparison with USGS NASQAN data. |
No | No | No | None |
Model Uncertainty Analysis Reported?
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No | No | No | No | No | Unclear | No | No | No | Not applicable | No | No | Yes | No | No | Not applicable | No | None |
Model Sensitivity Analysis Reported?
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No ?Comment:Sensitivity analysis performed for agent values only. |
No | No | No |
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. |
Yes | No | No | Unclear | Not applicable | No | No | No | No | No | Not applicable | Yes | None |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Unclear | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | None |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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EM-131 | EM-142 | EM-337 | EM-451 | EM-457 |
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EM-627 |
EM-660 ![]() |
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EM-891 | EM-961 |
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None | None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-71 | EM-80 | EM-84 |
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EM-131 | EM-142 | EM-337 | EM-451 | EM-457 |
EM-485 ![]() |
EM-627 |
EM-660 ![]() |
EM-719 ![]() |
EM-891 | EM-961 |
None | None | None | None | None | None | None |
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None | None |
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None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-12 ![]() |
EM-71 | EM-80 | EM-84 |
EM-98 ![]() |
EM-104 |
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EM-131 | EM-142 | EM-337 | EM-451 | EM-457 |
EM-485 ![]() |
EM-627 |
EM-660 ![]() |
EM-719 ![]() |
EM-891 | EM-961 |
Centroid Latitude
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44.11 | 45.05 | 45.05 | -30 | 39.88 | 42 | -34.9 | 47.8 | 35.99 | -9999 | 17.73 | 17.73 | 46.82 | 40.6 | 45.12 | 42.93 | 42.26 | None |
Centroid Longitude
em.detail.ddLongHelp
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-123.09 | 6.4 | 6.4 | 25 | -113.81 | -73 | 138.7 | -122.7 | -78.96 | -9999 | -64.77 | -64.77 | 8.23 | -88.4 | 85.18 | -92.57 | -87.84 | None |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | NAD83 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None |
Centroid Coordinates Status
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Estimated | Provided | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | None |
EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-71 | EM-80 | EM-84 |
EM-98 ![]() |
EM-104 |
EM-129 ![]() |
EM-131 | EM-142 | EM-337 | EM-451 | EM-457 |
EM-485 ![]() |
EM-627 |
EM-660 ![]() |
EM-719 ![]() |
EM-891 | EM-961 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Forests | Agroecosystems | Near Coastal Marine and Estuarine | Rivers and Streams | Ground Water | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Forests | Rivers and Streams | Inland Wetlands | Agroecosystems | Rivers and Streams | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Agricultural-urban interface at river junction | Subalpine terraces, grasslands, and meadows. | Subalpine terraces, grasslands, and meadows. | Not reported | Not reported | none | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | glacier-carver saltwater fjord | Urban areas including streams | Not applicable | Coral reefs | Coral reefs | forests | Agroecosystems and associated drainage and wetlands | stream reaches | Research farm in historic grassland | Lake Michigan & Lake Erie waterfront | Plains |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 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 |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-71 | EM-80 | EM-84 |
EM-98 ![]() |
EM-104 |
EM-129 ![]() |
EM-131 | EM-142 | EM-337 | EM-451 | EM-457 |
EM-485 ![]() |
EM-627 |
EM-660 ![]() |
EM-719 ![]() |
EM-891 | EM-961 |
EM Organismal Scale
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Not applicable | Community | Community | Not applicable | Species | Not applicable | Species | Not applicable | Community | Not applicable | Guild or Assemblage | Not applicable | Community | Not applicable | Not applicable | Community | Not applicable | None |
Taxonomic level and name of organisms or groups identified
EM-12 ![]() |
EM-71 | EM-80 | EM-84 |
EM-98 ![]() |
EM-104 |
EM-129 ![]() |
EM-131 | EM-142 | EM-337 | EM-451 | EM-457 |
EM-485 ![]() |
EM-627 |
EM-660 ![]() |
EM-719 ![]() |
EM-891 | EM-961 |
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None Available | None Available | None Available |
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None Available |
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None Available | None Available | None Available | None Available | None Available | 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-12 ![]() |
EM-71 | EM-80 | EM-84 |
EM-98 ![]() |
EM-104 |
EM-129 ![]() |
EM-131 | EM-142 | EM-337 | EM-451 | EM-457 |
EM-485 ![]() |
EM-627 |
EM-660 ![]() |
EM-719 ![]() |
EM-891 | EM-961 |
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None |
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None |
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None | None |
<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-12 ![]() |
EM-71 | EM-80 | EM-84 |
EM-98 ![]() |
EM-104 |
EM-129 ![]() |
EM-131 | EM-142 | EM-337 | EM-451 | EM-457 |
EM-485 ![]() |
EM-627 |
EM-660 ![]() |
EM-719 ![]() |
EM-891 | EM-961 |
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None |
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None | None |
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None |
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None | None |