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
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EM ID
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EM-12 |
EM-51 |
EM-63 | EM-68 | EM-82 | EM-103 | EM-137 | EM-143 | EM-154 | EM-303 | EM-306 | EM-344 |
EM-359 |
EM-438 | EM-628 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
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EM Short Name
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Evoland v3.5 (bounded growth), Eugene, OR, USA | EnviroAtlas-Nat. filtration-water | EnviroAtlas - Natural biological nitrogen fixation | Fodder crude protein content, Central French Alps | Pollination ES, Central French Alps | Birds in estuary habitats, Yaquina Estuary, WA, USA | i-Tree Hydro v4.0 | InVEST habitat quality | Mangrove development, Tampa Bay, FL, USA | Biological pest control, Uppland Province, Sweden | Urban Temperature, Baltimore, MD, USA | InVEST water yield, Xitiaoxi River basin, China | InVEST (v1.004) sediment retention, Indonesia | InVESTv3.0 Nutrient retention, Guánica Bay | SolVES, Bridger-Teton NF, WY | Floral resources on landfill sites, United Kingdom | Northern Shoveler recruits, CREP wetlands, IA, USA | Pollinators on landfill sites, United Kingdom | C sequestration in grassland restoration, England |
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EM Full Name
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Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA | US EPA EnviroAtlas - Natural filtration (of water by tree cover); Example is shown for Durham NC and vicinity, USA | US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Fodder crude protein content, Central French Alps | Pollination ecosystem service estimated from plant functional traits, Central French Alps | Bird use of estuarine habitats, Yaquina Estuary, WA, USA | i-Tree Hydro v4.0 (default data option) | InVEST (Integrated Valuation of Environmental Services and Tradeoffs) Habitat Quality | Mangrove wetland development, Tampa Bay, FL, USA | Biological control of agricultural pests by natural predators, Uppland Province, Sweden | Urban Air Temperature Change, Baltimore, MD, USA | InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) water yield, Xitiaoxi River basin, China | InVEST (Integrated Valuation of Environmental Services and Tradeoffs v1.004) sediment retention, Sumatra, Indonesia | InVEST (Integrated Valuation of Environmental Services and Tradeoffs)v3.0 Nutrient retention, Guánica Bay, Puerto Rico, USA | SolVES, Social Values for Ecosystem Services, Bridger-Teton National Forest, WY | Floral resources on landfill sites, East Midlands, United Kingdom | Northern Shoveler duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Pollinating insects on landfill sites, East Midlands, United Kingdon | Carbon sequestration in grassland diversity restoration, England |
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EM Source or Collection
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Envision |
US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Hydro model. |
US EPA | EnviroAtlas | EU Biodiversity Action 5 | EU Biodiversity Action 5 | US EPA | i-Tree | USDA Forest Service |
InVEST ?Comment:From the Natural Capital Project website |
US EPA | None | i-Tree | USDA Forest Service | InVEST | InVEST | US EPA | InVEST | None | None | None | None | None |
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EM Source Document ID
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47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
223 |
262 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
260 | 260 | 275 | 198 | 278 | 97 | 299 | 217 | 307 | 309 | 338 | 369 | 389 |
372 ?Comment:Document 373 is a secondary source for this EM. |
389 | 396 |
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Document Author
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Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | US EPA Office of Research and Development - National Exposure Research Laboratory | US EPA Office of Research and Development - National Exposure Research Laboratory | 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. | Frazier, M. R., Lamberson, J. O. and Nelson, W. G. | USDA Forest Service | Natural Capital Project | Osland, M. J., Spivak, A. C., Nestlerode, J. A., Lessmann, J. M., Almario, A. E., Heitmuller, P. T., Russell, M. J., Krauss, K. W., Alvarez, F., Dantin, D. D., Harvey, J. E., From, A. S., Cormier, N. and Stagg, C.L. | Jonsson, M., Bommarco, R., Ekbom, B., Smith, H.G., Bengtsson, J., Caballero-Lopez, B., Winqvist, C., and Olsson, O. | Heisler, G. M., Ellis, A., Nowak, D. and Yesilonis, I. | Zhang C., Li, W., Zhang, B., and Liu, M. | Bhagabati, N. K., Ricketts, T., Sulistyawan, T. B. S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A., Tallis, H., and Wolney, S. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin | De Deyn, G. B., R. S. Shiel, N. J. Ostle, N. P. McNamara, S. Oakley, I. Young, C. Freeman, N. Fenner, H. Quirk, and R. D. Bardgett |
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Document Year
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2008 | 2013 | 2013 | 2011 | 2011 | 2014 | Not Reported | 2014 | 2012 | 2014 | 2016 | 2012 | 2014 | 2017 | 2014 | 2013 | 2010 | 2013 | 2011 |
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Document Title
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Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | EnviroAtlas - Featured Community | EnviroAtlas - National | 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 | Intertidal habitat utilization patterns of birds in a Northeast Pacific estuary | i-Tree Hydro User's Manual v. 4.0 | Habitat Quality model - InVEST ver. 3.0 | Ecosystem development after mangrove wetland creation: plant–soil change across a 20-year chronosequence | Ecological production functions for biological control services in agricultural landscapes | Modeling and imaging land-cover influences on air-temperature in and near Baltimore, MD | Water yield of Xitiaoxi River basin based on InVEST modeling | Ecosystem services reinforce Sumatran tiger conservation in land use plans | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects | Additional carbon sequestration benefits of grassland diversity restoration |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published on US EPA EnviroAtlas website | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Webpage | Published on Natural Capital Project website | 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 report | Published journal manuscript | Published journal manuscript |
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EM ID
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EM-12 |
EM-51 |
EM-63 | EM-68 | EM-82 | EM-103 | EM-137 | EM-143 | EM-154 | EM-303 | EM-306 | EM-344 |
EM-359 |
EM-438 | EM-628 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
http://evoland.bioe.orst.edu/ ?Comment:Software is likely available. |
https://www.epa.gov/enviroatlas | https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | http://www.itreetools.org | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | https://www.naturalcapitalproject.org/invest/ | http://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | |
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Contact Name
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Michael R. Guzy | EnviroAtlas Team |
EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Sandra Lavorel | Sandra Lavorel |
M. R. Frazier ?Comment:Present address: M. R. Frazier National Center for Ecological Analysis and Synthesis, 735 State St. Suite 300, Santa Barbara, CA 93101, USA |
Not applicable | The Natural Capital Project | Michael Osland | Mattias Jonsson | Gordon M. Heisler | Li Wenhua | Nirmal K. Bhagabati | Susan H. Yee | Benson Sherrouse | Sam Tarrant | David Otis | Sam Tarrant | Gerlinde B. De Deyn |
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Contact Address
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Oregon State University, Dept. of Biological and Ecological Engineering | Not reported | Not reported | 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 | Western Ecology Division, Office of Research and Development, U.S. Environmental Protection Agency, Pacific coastal Ecology Branch, 2111 SE marine Science Drive, Newport, OR 97365 | Not applicable | 371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | U.S. Environmental Protection Agency, Gulf Ecology Division, gulf Breeze, FL 32561 | Department of Ecology, Swedish University of Agricultural Sciences, PO Box 7044, SE-750 07 Uppsala, Sweden | 5 Moon Library, c/o SUNY-ESF, Syracuse, NY 13210 | Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China | The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K. | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K. | Dept. of Terrestrial Ecology, Netherlands Institute of Ecology, P O Box 40, 6666 ZG Heteren, The Netherlands |
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Contact Email
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Not reported | enviroatlas@epa.gov | enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | frazier@nceas.ucsb.edu | Not applicable | invest@naturalcapitalproject.org | mosland@usgs.gov | mattias.jonsson@slu.se | gheisler@fs.fed.us | liwh@igsnrr.ac.cn | nirmal.bhagabati@wwfus.org | yee.susan@epa.gov | bcsherrouse@usgs.gov | sam.tarrant@rspb.org.uk | dotis@iastate.edu | sam.tarrant@rspb.org.uk | g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com |
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EM ID
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EM-12 |
EM-51 |
EM-63 | EM-68 | EM-82 | EM-103 | EM-137 | EM-143 | EM-154 | EM-303 | EM-306 | EM-344 |
EM-359 |
EM-438 | EM-628 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
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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." | The Natural Filtration 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). i-Tree Hydro also estimates changes in water quality using hourly runoff estimates and mean and median national event mean concentration (EMC) values. The model was calibrated using hourly stream flow data to yield the best fit between model and measured stream flow results… 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… The term event mean concentration (EMC) is a statistical parameter used to represent the flow-proportional average concentration of a given parameter during a storm event. EMC data is used for estimating pollutant loading into watersheds. The response outputs were calculated as 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." METADATA DESCRIPTION PARAPHRASED: Changes in water quality were estimated for the following pollutants (entered as separate runs); total suspended solids (TSS), total phosphorus, soluble phosphorus, nitrites and nitrates, total Kjeldahl nitrogen (TKN), biochemical oxygen demand (BOD5), chemical oxygen demand (COD5), and copper. "Reduction in annual runoff (census block group)" variable data was derived from the EnviroAtlas water recharge coverage which used the i-Tree Hydro model. | DATA FACT SHEET: "This EnviroAtlas national map displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM." | 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. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., fodder crude protein content), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in fodder crude protein content was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy…Fodder crude protein for each pixel was calculated and mapped using model estimates...This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on fodder protein content. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The pollination ecosystem service map was a simple sums of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to pollination ecosystem services are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions." | AUTHOR'S DESCRIPTION: "To describe bird utilization patterns of intertidal habitats within Yaquina estuary, Oregon, we conducted censuses to obtain bird species and abundance data for the five dominant estuarine intertidal habitats: Zostera marina (eelgrass), Upogebia (mud shrimp)/ mudflat, Neotrypaea (ghost shrimp)/sandflat, Zostera japonica (Japanese eelgrass), and low marsh. EPFs were developed for the following metrics of bird use: standardized species richness; Shannon diversity; and density for the following four groups: all birds, all birds excluding gulls, waterfowl (ducks and geese), and shorebirds." | ABSTRACT: "i-Tree Hydro is the first urban hydrology model that is specifically designed to model vegetation effects and to be calibrated against measured stream flow data. It is designed to model the effects of changes in urban tree cover and impervious surfaces on hourly stream flows and water quality at the watershed level." AUTHOR'S DESCRIPTION: "The purpose of i-Tree Hydro is to simulate hourly changes in stream flow (and water quality) given changes in tree and impervious cover in the watershed. The following is an overview of the process: 1) Determine your watershed of analysis and stream gauge station. i-Tree Hydro works on a watershed basis with the watershed determined as the total drainage area upstream from a measured stream gauge. Stream gauge availability varies. 2) Download national digital elevation data. Once the area and location of the watershed are known, digital elevation data are downloaded from the USGS for an area that encompasses the entire watershed. ArcGIS software is then used to create a digital elevation map and to determine the exact boundary for the watershed upstream from the gauge station location. 3) Determine cover attributes of the watershed and gather other required data. i-Tree Canopy and other sources can be used to determine the tree cover, shrub cover, impervious surface and other cover types. Information about other aspects of the watershed such as proportion of evergreen trees and shrubs, leaf area index, and a variety of hydrologic parameters must be collected. 4) Get started with Hydro. Once these input data are ready, they are loaded into Hydro to begin analysis. 5) Calibrate the model. The Hydro model contains an auto-calibration routine that tries to find the best fit between the stream flow predicted by the model and the stream flow measured at the stream gauge station given the various inputs. The model can also be manually calibrated to improve the fit by changing the parameters as needed. 6) Model new scenarios: Once the model is properly calibrated, tree and impervious cover parameters can be changed to illustrate the impact on stream flow and water quality." | Please note: This ESML entry describes an InVEST model version that was current as of 2014. More recent versions may be available at the InVEST website. AUTHORS DESCRIPTION: "The InVEST habitat quality model combines information on LULC and threats to biodiversity to produce habitat quality maps. This approach generates two key sets of information that are useful in making an initial assessment of conservation needs: the relative extent and degradation of different types of habitat types in a region and changes across time. This approach further allows rapid assessment of the status of and change in a proxy for more detailed measures of biodiversity status. If habitat changes are taken as representative of genetic, species, or ecosystem changes, the user is assuming that areas with high quality habitat will better support all levels of biodiversity and that decreases in habitat extent and quality over time means a decline in biodiversity persistence, resilience, breadth and depth in the area of decline. The habitat rarity model indicates the extent and pattern of natural land cover types on the current or a potential future landscape vis-a-vis the extent of the same natural land cover types in some baseline period. Rarity maps allow users to create a map of the rarest habitats on the landscape relative to the baseline chosen by the user to represent the mix of habitats on the landscape that is most appropriate for the study area’s native biodiversity. The model requires basic data that are available virtually everywhere in the world, making it useful in areas for which species distribution data are poor or lacking altogether. Extensive occurrence (presence/absence) data may be available in many places for current conditions. However, modeling the change in occurrence, persistence, or vulnerability of multiple species under future conditions is often impossible or infeasible. While a habitat approach leaves out the detailed species occurrence data available for current conditions, several of its components represent advances in functionality over many existing biodiversity conservation planning tools. The most significant is the ability to characterize the sensitivity of habitats types to various threats. Not all habitats are affected by all threats in the same way, and the InVEST model accounts for this variability. Further, the model allows users to estimate the relative impact of one threat over another so that threats that are more damaging to biodiversity persistence on the landscape can be represented as such. For example, grassland could be particularly sensitive to threats generated by urban areas yet moderately sensitive to threats generated by roads. In addition, the distance over which a threat will degrade natural systems can be incorporated into the model. Model assessment of the current landscape can be used as an input to a coarse-filter assessment of current conservation needs and opportunities. Model assessment of pote | ABSTRACT: "Mangrove wetland restoration and creation effortsare increasingly proposed as mechanisms to compensate for mangrove wetland losses. However, ecosystem development and functional equivalence in restored and created mangrove wetlands are poorly understood. We compared a 20-year chronosequence of created tidal wetland sites in Tampa Bay, Florida (USA) to natural reference mangrove wetlands. Across the chronosequence, our sites represent the succession from salt marsh to mangrove forest communities. Our results identify important soil and plant structural differences between the created and natural reference wetland sites; however, they also depict a positive developmental trajectory for the created wetland sites that reflects tightly coupled plant-soil development. Because upland soils and/or dredge spoils were used to create the new mangrove habitats, the soils at younger created sites and at lower depths (10–30 cm) had higher bulk densities, higher sand content, lower soil organic matter (SOM), lower total carbon (TC), and lower total nitrogen (TN) than did natural reference wetland soils. However, in the upper soil layer (0–10 cm), SOM, TC, and TN increased with created wetland site age simultaneously with mangrove forest growth. The rate of created wetland soil C accumulation was comparable to literature values for natural mangrove wetlands. Notably, the time to equivalence for the upper soil layer of created mangrove wetlands appears to be faster than for many other wetland ecosystem types. Collectively, our findings characterize the rate and trajectory of above- and below-ground changes associated with ecosystem development in created mangrove wetlands; this is valuable information for environmental managers planning to sustain existing mangrove wetlands or mitigate for mangrove wetland losses." | ABSTRACT: "We develop a novel, mechanistic landscape model for biological control of cereal aphids, explicitly accounting for the influence of landscape composition on natural enemies varying in mobility, feeding rates and other life history traits. Finally, we use the model to map biological control services across cereal fields in a Swedish agricultural region with varying landscape complexity. The model predicted that biological control would reduce crop damage by 45–70% and that the biological control effect would be higher in complex landscapes. In a validation with independent data, the model performed well and predicted a significant proportion of biological control variation in cereal fields. However, much variability remains to be explained, and we propose that the model could be improved by refining the mechanistic understanding of predator dynamics and accounting for variation in aphid colonization." | An empirical model for predicting below-canopy air temperature differences is developed for evaluating urban structural and vegetation influences on air temperature in and near Baltimore, MD. AUTHOR'S DESCRIPTION: "The study . . . Developed an equation for predicting air temperature at the 1.5m height as temperature difference, T, between a reference weather station and other stations in a variety of land uses. Predictor variables were derived from differences in land cover and topography along with forcing atmospheric conditions. The model method was empirical multiple linear regression analysis.. . Independent variables included remotely sensed tree cover, impervious cover, water cover, descriptors of topography, an index of thermal stability, vapor pressure deficit, and antecedent precipitation." | 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. ABSTRACT: "A water yield model based on InVEST was employed to estimate water runoff in the Xitiaoxi River basin…In order to test model accuracy the natural runoff of Xitiaoxi River was estimated based on linear regression relation of rainfall-runoff in a 'reference period'." AUTHOR'S DESCRIPTION: "The water yield model is based on the Budyko curve (1974) and annual precipitation…Water yield models require land use and land cover, precipitation, average annual potential evapotranspiration, soil depth, plant available water content, watersheds and sub-watersheds as well as a biophysical table reflecting the attributes of each land use and land cover." | 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. ABSTRACT: "...Here we use simple spatial analyses on readily available datasets to compare the distribution of five ecosystem services with tiger habitat in central Sumatra. We assessed services and habitat in 2008 and the changes in these variables under two future scenarios: a conservation-friendly Green Vision, and a Spatial Plan developed by the Indonesian government..." AUTHOR'S DESCRIPTION: "We used a modeling tool, InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004; Tallis et al., 2010), to map and quantify tiger habitat quality and five ecosystem services. InVEST maps ecosystem services and the quality of species habitat as production functions of LULC using simple biophysical models. Models were parameterized using data from regional agencies, literature surveys, global databases, site visits and prior field experience (Table 1)... The sediment retention model is based on the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978). It estimates erosion as ton y^-1 of sediment load, based on the energetic ability of rainfall to move soil, the erodibility of a given soil type, slope, erosion protection provided by vegetated LULC, and land management practices. The model routes sediment originating on each land parcel along its flow path, with vegetated parcels retaining a fraction of sediment with varying efficiencies, and exporting the remainder downstream. ...Although InVEST reports ecosystem services in biophysical units, its simple models are best suited to understanding broad patterns of spatial variation (Tallis and Polasky, 2011), rather than for precise quantification. Additionally, we lacked field measurements against which to calibrate our outputs. Therefore, we focused on relative spatial distribution across the landscape, and relative change to scenarios." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "Nutrient retention was estimated by first calculating water yield and establishing the quantity of nitrogen or phosphorus retained by different land cover classes using a water purification model (InVEST 3.0.0; Tallis et al., 2013). Different land cover classes were assumed to have different capacities for retaining nutrients, depending on the efficiency of vegetation in removing either nitrogen or phosphorus and the rates of nitrogen or phosphorus loading." “Use of other models in conjunction with this model:Average runoff per pixel modeled here were derived from the InVEST Water Yield model" | [ABSTRACT: " "Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders.With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems. | ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level… A “floral cover” method to represent available floral resources was used which combines floral abundance with inflorescence size. Mean area of the floral unit from above was measured for each flowering plant species and then multiplied by their frequencies." "Insect pollinated flowering plant species composition and floral abundance between sites by type were represented by non-metric multidimensional scaling (NMDS)...This method is sensitive to showing outliers and the distance between points shows the relative similarity (McCune & Grace 2002; Ollerton et al. 2009)." (This data is not entered into ESML) | ABSTRACT: "Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2*R*n where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = H*Z*B/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632). | 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: "A major aim of European agri-environment policy is the management of grassland for botanical diversity conservation and restoration, together with the delivery of ecosystem services including soil carbon (C) sequestration. To test whether management for biodiversity restoration has additional benefits for soil C sequestration, we investigated C and nitrogen (N) accumulation rates in soil and C and N pools in vegetation in a long-term field experiment (16 years) in which fertilizer application and plant seeding were manipulated. In addition, the abundance of the legume Trifolium pratense was manipulated for the last 2 years. To unravel the mechanisms underlying changes in soil C and N pools, we also tested for effects of diversity restoration management on soil structure, ecosystem respiration and soil enzyme activities…" AUTHOR'S DESCRIPTION: "Measurements were made on 36 plots of 3 x 3 m comprising two management treatments (and their controls) in a long-term multifactorial grassland restoration experiment which have successfully increased plant species diversity, namely the cessation of NPK fertilizer application and the addition of seed mixtures…" |
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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 | None identified | None identified | None identified | None identified | Not applicable | None identified | None identified | None identified | This analysis provided input to government-led spatial planning and strategic environmental assessments in the study area. This region contains some of the last remaining forest habitat of the critically endangered Sumatran tiger, Panthera tigris sumatrae. | Improving water quality | None | None identified | None identified | None identified | None identified |
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Biophysical Context
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No additional description provided | No additional description provided | No additional description provided | Elevation ranges from 1552 to 2442 m, on predominantely south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Estuarine intertidal, eelgrass, mudflat, sandflat and low marsh | No additional description provided | Not applicable | mangrove forest,Salt marsh, estuary, sea level, | Spring-sown cereal croplands, where the bird chearry-oat aphid is a key aphid pest. The aphid colonizes the crop during late May and early June, depending on weather and location. The colonization phase is followed by a brief phase of rapid exponential population growth by wingless aphids, continuing until about the time of crop heading, in late June or early July. After heading, aphid populations usually decline rapidly in the crop due to decreased plant quality and migration to grasslands. The aphids are attacked by a complex of arthropod natural enemies, but parasitism is not important in the region and therefore not modelled here. | One airport site, one urban site, one site in deciduous leaf litter, and four sites in short grass ground cover. Measured sky view percentages ranged from 6% at the woods site, to 96% at the rural open site. | Mean elevation of 266 m, with southwestern mountainous area. Subtropical monsoon climate. Annual average temperature of 12.2-15.6 °C. Annual mean precipitation is 1500 mm, and over 70% of rainfall occurs in the flood season (Apr-Oct). | Six watersheds in central Sumatra covering portions of Riau, Jambi and West Sumatra provinces. The Barisan mountain range comprises the western edge of the watersheds, while peat swamps predominate in the east. | No additional description provided | Rocky mountain conifer forests | No additional description provided | Prairie Pothole Region of Iowa | No additional description provided | Lolium perenne-Cynosorus cristatus grassland; The soil is a shallow brown-earth (average depth 28 cm) over limestone of moderate-high residual fertility. |
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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 | No scenarios presented | No scenarios presented | No scenarios presented |
Potential land Use Land Class (LULC) future and baseline ?Comment:model requires current landuse but can compare to baseline (prior to intensive management of the land) and potential future landuse. These are the two scenarios suggested in the documentation. |
Not applicable | No scenarios presented | No scenarios presented | No scenarios presented | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | No scenarios presented | N/A | No scenarios presented | No scenarios presented | No scenarios presented | Additional benefits due to biodiversity restoration practices |
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EM ID
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EM-12 |
EM-51 |
EM-63 | EM-68 | EM-82 | EM-103 | EM-137 | EM-143 | EM-154 | EM-303 | EM-306 | EM-344 |
EM-359 |
EM-438 | EM-628 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
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Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method Only | Method Only | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs |
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New or Pre-existing EM?
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New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-12 |
EM-51 |
EM-63 | EM-68 | EM-82 | EM-103 | EM-137 | EM-143 | EM-154 | EM-303 | EM-306 | EM-344 |
EM-359 |
EM-438 | EM-628 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
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Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-47 | Doc-313 | Doc-314 ?Comment:Doc 183 is a secondary source for the Evoland model. |
Doc-198 |
Doc-346 | Doc-347 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
Doc-260 | Doc-269 | Doc-260 | None | Doc-190 | Doc-223 | Doc-309 | None | None | Doc-220 | Doc-219 | Doc-218 | Doc-280 | Doc-311 | Doc-338 | Doc-205 | Doc-338 | Doc-309 | Doc-205 | None | None | Doc-372 | Doc-373 | Doc-389 | None |
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EM ID for related EM
em.detail.relatedEmEmIdHelp
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EM-333 | EM-369 | EM-137 | EM-142 | None | EM-65 | EM-66 | EM-69 | EM-70 | EM-71 | 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-80 | EM-81 | EM-83 | None | EM-109 | EM-142 | EM-51 | EM-345 | None | None | None | EM-148 | EM-368 | EM-437 | EM-111 | EM-435 | EM-363 | EM-112 | EM-629 | EM-626 | EM-709 | EM-705 | EM-704 | EM-703 | EM-701 | EM-700 | EM-632 | EM-697 | None |
EM Modeling Approach
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EM ID
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EM-12 |
EM-51 |
EM-63 | EM-68 | EM-82 | EM-103 | EM-137 | EM-143 | EM-154 | EM-303 | EM-306 | EM-344 |
EM-359 |
EM-438 | EM-628 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
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EM Temporal Extent
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1990-2050 | 1999-2010 | 2006-2010 | 2007-2009 | Not reported | December 2007 - November 2008 | Not applicable | Not applicable | 1990-2010 | 2009 | May 5-Sept 30 2006 | 2003-2007 | 2008-2020 | 1980 - 2013 | 2004-2008 | 2007-2008 | 1987-2007 | 2007-2008 | 1990-2007 |
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EM Time Dependence
em.detail.timeDependencyHelp
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time-dependent |
time-stationary ?Comment:The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, operated on an hourly timestep. The final annual flow parameter however is time stationary. |
time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | future time | Not applicable | Not applicable | other or unclear (comment) | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | continuous | Not applicable | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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2 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Hour | Not applicable | Not applicable | Not applicable | Hour | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM ID
em.detail.idHelp
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EM-12 |
EM-51 |
EM-63 | EM-68 | EM-82 | EM-103 | EM-137 | EM-143 | EM-154 | EM-303 | EM-306 | EM-344 |
EM-359 |
EM-438 | EM-628 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
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Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Geopolitical | Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Physiographic or ecological | Not applicable | No location (no locational reference given) | Physiographic or Ecological | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Other |
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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 | Durham, NC and vicinity | counterminous United States | Central French Alps | Central French Alps | Yaquina Estuary (intertidal), Oregon, USA | Not applicable | Not applicable | Tampa Bay | Uppland province | Baltimore, MD | Xitiaoxi River basin | central Sumatra | Guanica Bay Study Area | National Park | East Midlands | CREP (Conservation Reserve Enhancement Program | East Midlands | Colt Park meadows, Ingleborough National Nature Reserve, northern England |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 10-100 km^2 | 10-100 km^2 | 1-10 km^2 | Not applicable | Not applicable | 100-1000 km^2 | 10,000-100,000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. | 10,000-100,000 km^2 | 1000-10,000 km^2. | <1 ha |
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EM ID
em.detail.idHelp
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EM-12 |
EM-51 |
EM-63 | EM-68 | EM-82 | EM-103 | EM-137 | EM-143 | EM-154 | EM-303 | EM-306 | EM-344 |
EM-359 |
EM-438 | EM-628 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
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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) ?Comment:Watersheds (12-digit HUCs). |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially 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) |
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Spatial Grain Type
em.detail.spGrainTypeHelp
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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 | area, for pixel or radial feature | other (habitat type) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | 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) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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varies | irregular | irregular | 20 m x 20 m | 20 m x 20 m | 0.87-104.29 ha | 30 x 30 m | LULC pixel size | m^2 | 25 m x 25 m | 10m x 10m | Not reported | 30 m x 30 m | 30 m x 30 m | 30m2 | multiple unrelated locations | multiple, individual, irregular sites | multiple unrelated locations | 3 m x 3 m |
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EM ID
em.detail.idHelp
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EM-12 |
EM-51 |
EM-63 | EM-68 | EM-82 | EM-103 | EM-137 | EM-143 | EM-154 | EM-303 | EM-306 | EM-344 |
EM-359 |
EM-438 | EM-628 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
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EM Computational Approach
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Numeric |
Analytic ?Comment:The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, was numeric. The final parameter however did not require iteration. |
Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Analytic | Analytic |
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EM Determinism
em.detail.deterStochHelp
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stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic |
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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-51 |
EM-63 | EM-68 | EM-82 | EM-103 | EM-137 | EM-143 | EM-154 | EM-303 | EM-306 | EM-344 |
EM-359 |
EM-438 | EM-628 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
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Model Calibration Reported?
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Unclear | Unclear | No | No | No | Unclear | Not applicable | Not applicable | No | No | Yes | Yes | No | No | No | Not applicable | Unclear | Not applicable | Not applicable |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | No | Yes | No | No | Not applicable | Not applicable | No | No | Yes | No | No | No | Yes | Not applicable | No | Not applicable | Not applicable |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None |
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None | None | None | None | None | None |
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None | None | None |
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None | None | None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | Unclear | No | Yes | No | No | Not applicable | Not applicable | No | Yes | No | No | No | No | No | Not applicable | No | Not applicable | No |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | Unclear | No | No | No | No | Not applicable | Not applicable | Yes | No | No | No | No | No | No | Not applicable | No | Not applicable | No |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No ?Comment:Sensitivity analysis performed for agent values only. |
Unclear | No | No | No | No | Not applicable | Not applicable | Yes |
Yes ?Comment:AUTHOR'S NOTE: "Varying aphid fecundity, overall predator abundances and attack rates affected the biological control effect, but had little influence on the relative differences between landscapes with high and low levels of biological control. The model predictions were more sensitive to changing the predators' landscape relations, but, with few exceptions, did not dramatically alter the overall patterns generated by the model." |
No | Yes | No | No | No | Not applicable | No | Not applicable | No |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | No | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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EM-12 |
EM-51 |
EM-63 | EM-68 | EM-82 | EM-103 | EM-137 | EM-143 | EM-154 | EM-303 | EM-306 | EM-344 |
EM-359 |
EM-438 | EM-628 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
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None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-12 |
EM-51 |
EM-63 | EM-68 | EM-82 | EM-103 | EM-137 | EM-143 | EM-154 | EM-303 | EM-306 | EM-344 |
EM-359 |
EM-438 | EM-628 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
| None | None | None | None | None |
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None | None |
Comment:Realm: Tropical Atlantic Region: West Tropical Atlantic Province: Tropical Northwestern Atlantic Ecoregion: Floridian |
None | None | None | None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-12 |
EM-51 |
EM-63 | EM-68 | EM-82 | EM-103 | EM-137 | EM-143 | EM-154 | EM-303 | EM-306 | EM-344 |
EM-359 |
EM-438 | EM-628 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
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Centroid Latitude
em.detail.ddLatHelp
?
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44.11 | 35.99 | 39.5 | 45.05 | 45.05 | 44.62 | -9999 | -9999 | 27.8 | 59.52 | 39.28 | 30.55 | 0 | 17.97 | 43.93 | 52.22 | 42.62 | 52.22 | 54.2 |
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Centroid Longitude
em.detail.ddLongHelp
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-123.09 | -78.96 | -98.35 | 6.4 | 6.4 | -124.06 | -9999 | -9999 | -82.4 | 17.9 | -76.62 | 119.5 | 102 | -66.93 | 110.24 | -0.91 | -93.84 | -0.91 | -2.35 |
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Centroid Datum
em.detail.datumHelp
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WGS84 | None provided | WGS84 | WGS84 | WGS84 | None provided | Not applicable | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Estimated | Estimated | Provided | Provided | Provided | Not applicable | Not applicable | Estimated | Estimated | Estimated | Provided | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Provided |
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EM ID
em.detail.idHelp
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EM-12 |
EM-51 |
EM-63 | EM-68 | EM-82 | EM-103 | EM-137 | EM-143 | EM-154 | EM-303 | EM-306 | EM-344 |
EM-359 |
EM-438 | EM-628 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Rivers and Streams | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Rivers and Streams | Ground Water | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Atmosphere | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Aquatic Environment (sub-classes not fully specified) | Inland Wetlands | Near Coastal Marine and Estuarine | Open Ocean and Seas | Forests | Agroecosystems | Created Greenspace | Scrubland/Shrubland | Barren | Forests | Created Greenspace | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Created Greenspace | Grasslands | Agroecosystems | Grasslands |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Agricultural-urban interface at river junction | Urban areas including streams | Terrestrial | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Estuarine intertidal | Urban watersheds | Not applicable | Created Mangrove wetlands | Spring-sown cereal croplands and surrounding grassland and non-arable land | Urban landscape and surrounding area | Watershed | 104 land use land cover classes | 13 LULC were used | Montain forest | restored landfills and grasslands | Wetlands buffered by grassland within agroecosystems | restored landfills and grasslands | fertilized grassland (historically hayed) |
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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 finer than that of the Environmental Sub-class | Not applicable | 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 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 | 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 corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
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EM ID
em.detail.idHelp
?
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EM-12 |
EM-51 |
EM-63 | EM-68 | EM-82 | EM-103 | EM-137 | EM-143 | EM-154 | EM-303 | EM-306 | EM-344 |
EM-359 |
EM-438 | EM-628 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
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EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Not applicable | Community | Community | Guild or Assemblage | Community | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Not applicable | Community | Not applicable | Not applicable | Individual or population, within a species | Individual or population, within a species | Individual or population, within a species | Community |
Taxonomic level and name of organisms or groups identified
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EM-12 |
EM-51 |
EM-63 | EM-68 | EM-82 | EM-103 | EM-137 | EM-143 | EM-154 | EM-303 | EM-306 | EM-344 |
EM-359 |
EM-438 | EM-628 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
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None Available | None Available | None Available | None Available |
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None Available | None Available |
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None Available | None Available | None Available | None Available | None Available |
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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)
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EM-12 |
EM-51 |
EM-63 | EM-68 | EM-82 | EM-103 | EM-137 | EM-143 | EM-154 | EM-303 | EM-306 | EM-344 |
EM-359 |
EM-438 | EM-628 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
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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)
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EM-12 |
EM-51 |
EM-63 | EM-68 | EM-82 | EM-103 | EM-137 | EM-143 | EM-154 | EM-303 | EM-306 | EM-344 |
EM-359 |
EM-438 | EM-628 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
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None |
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None |
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None | None |
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None |
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None | None |
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