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-71 | EM-82 | EM-119 |
EM-129 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-367 |
EM-380 ![]() |
EM-447 | EM-451 |
EM-467 ![]() |
EM-491 | EM-592 | EM-652 | EM-685 | EM-703 |
EM-713 ![]() |
EM-718 ![]() |
EM-838 | EM-861 |
EM-863 ![]() |
EM-886 | EM-889 |
EM Short Name
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Community flowering date, Central French Alps | Pollination ES, Central French Alps | Landscape importance for wildlife products, Europe | 3-PG, South Australia | Salmon habitat values, west coast of Canada | FORCLIM v2.9, Western OR, USA | Coral and land development, St.Croix, VI, USA | InVEST Coastal Blue Carbon | VELMA plant-soil, Oregon, USA | Wave height attenuation, St. Croix, USVI | Ease of reef access, St. Croix, USVI | Yasso07 v1.0.1, Switzerland | EnviroAtlas - Crops with no pollinator habitat | APEX v1501 | Savannah Sparrow density, CREP, Iowa, USA | Visitor value lost to a beach closure, MA, USA | Gadwall duck recruits, CREP wetlands, Iowa, USA | ESII Tool, Michigan, USA | WESP: Riparian & stream habitat, ID, USA | Eastern meadowlark abundance, Piedmont region, USA | ARIES Carbon sstorage, Santa Fe, NM | SLAMM, Tampa Bay, FL, USA | HWB indicator-poor mental health, Great Lakes, USA | HWB poor health, Great Lakes waterfront, USA |
EM Full Name
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Community weighted mean flowering date, Central French Alps | Pollination ecosystem service estimated from plant functional traits, Central French Alps | Landscape importance for wildlife products, Europe | 3-PG (Physiological Principles Predicting Growth), South Australia | Value of habitat quality changes for salmon populations, South Thompson watershed, west coast of Canada | FORCLIM (FORests in a changing CLIMate) v2.9, Western OR, USA | Coral colony density and land development, St.Croix, Virgin Islands, USA | InVEST v3.0 Coastal Blue Carbon | VELMA (Visualizing Ecosystems for Land Management Assessments) plant-soil, Oregon, USA | Wave height attenuation (by reef), St. Croix, USVI | Ease of access (to reef), St. Croix, USVI | Yasso07 v1.0.1 forest litter decomposition, Switzerland | US EPA EnviroAtlas - Acres of pollinated crops with no nearby pollinator habitat, USA | APEX (Agricultural Policy/Environmental eXtender Model) v1501 | Savannah Sparrow population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Visitor value lost to a beach closure, Barnstable, Massachusetts, USA | Gadwall duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | ESII (Ecosystem Services Identification and Inventory) Tool, Michigan, USA | WESP: Riparian and stream habitat focus projects, ID, USA | Eastern meadowlark abundance, Piedmont ecoregion, USA | ARIES Carbon storage, Santa Fe, New Mexico | SLAMM (sea level affecting marshes model), Tampa Bay, Florida, USA | Human well being indicator-poor mental health,Great Lakes waterfront, USA | Human well being indicator-poor health, Great Lakes waterfront, USA |
EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | None | US EPA | US EPA | InVEST | US EPA | US EPA | US EPA | None | US EPA | EnviroAtlas | None | None | US EPA | None | None | None | None | None | None | None | None |
EM Source Document ID
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260 | 260 | 228 | 243 | 286 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
96 | 310 | 317 | 335 | 335 | 343 | 262 | 357 | 372 | 386 |
372 ?Comment:Document 373 is a secondary source for this EM. |
392 ?Comment:Document 391 is an additional source for this EM. |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
405 | 411 |
415 ?Comment:Secondary sources: Documents 412 and 413. |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
Document Author
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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. | Haines-Young, R., Potschin, M. and Kienast, F. | Crossman, N. D., Bryan, B. A., and Summers, D. M. | Knowler, D.J., MacGregor, B.W., Bradford, M.J., Peterman, R.M | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Natural Capital Project | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | 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 | US EPA Office of Research and Development - National Exposure Research Laboratory | Steglich, E. M., J. Jeong and J. R. Williams | 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 | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | 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 | Guertin, F., K. Halsey, T. Polzin, M. Rogers, and B. Witt | Murphy, C. and T. Weekley | Riffel, S., Scognamillo, D., and L. W. Burger | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. | Sherwood, E. T. and H. S. Greening | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick |
Document Year
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2011 | 2011 | 2012 | 2011 | 2003 | 2007 | 2011 | 2014 | 2013 | 2014 | 2014 | 2014 | 2013 | 2016 | 2010 | 2018 | 2010 | 2019 | 2012 | 2008 | 2018 | 2014 | None | None |
Document Title
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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 | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Carbon payments and low-cost conservation | Valuing freshwater salmon habitat on the west coast of Canada | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Blue Carbon model - InVEST (v3.0) | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | 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 | EnviroAtlas - National | Agricultural Policy/Environmental eXtender Model User's Manual Version 1501 | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | From ash pond to riverside wetlands: Making the business case for engineered natural technologies | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Towards globally customizable ecosystem service models | Potential impacts and management implications of climate change on Tampa Bay estuary critical coastal habitats | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | Human well-being and natural capital indictors for Great Lakes waterfront revitalization |
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 | Documented, not peer reviewed | 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 but unpublished (explain in Comment) | Peer reviewed but unpublished (explain in Comment) |
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 | other | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | Published report | Published report | Published journal manuscript | Published report | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Journal manuscript submitted or in review | Journal manuscript submitted or in review |
EM ID
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EM-71 | EM-82 | EM-119 |
EM-129 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-367 |
EM-380 ![]() |
EM-447 | EM-451 |
EM-467 ![]() |
EM-491 | EM-592 | EM-652 | EM-685 | EM-703 |
EM-713 ![]() |
EM-718 ![]() |
EM-838 | EM-861 |
EM-863 ![]() |
EM-886 | EM-889 |
Not applicable | Not applicable | Not applicable | http://www.csiro.au/products/3PGProductivity#a1 | Not applicable | Not applicable | Not applicable | http://ncp-dev.stanford.edu/~dataportal/invest-releases/documentation/current_release/blue_carbon.html#running-the-model | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | https://www.epa.gov/enviroatlas | https://epicapex.tamu.edu/manuals-and-publications/ | Not applicable | Not applicable | Not applicable | https://www.esiitool.com/ | Not applicable | Not applicable |
https://integratedmodelling.org/hub/#/register ?Comment:Need to set up an account first and then can access the main integrated modelling hub page: |
http://warrenpinnacle.com/prof/SLAMM/index.html com/prof/SLAMM/index.html | Not applicable | Not applicable | |
Contact Name
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Sandra Lavorel | Sandra Lavorel | Marion Potschin | Anders Siggins | Duncan Knowler | Richard T. Busing | Leah Oliver | Gregg Verutes | Alex Abdelnour | Susan H. Yee | Susan H. Yee |
Markus Didion ?Comment:Tel.: +41 44 7392 427 |
EnviroAtlas Team | E. M. Steglich | David Otis | Kate K, Mulvaney | David Otis | Not reported | Chris Murphy | Sam Riffell | Javier Martinez-Lopez | Edward T. Sherwood | Ted Angradi | Ted Angradi |
Contact Address
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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 | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Not reported | School of Resource and Environmental Management, Simon Fraser University, Burnaby, Canada BC V5H 1S6 | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | National Health and Environmental Research Effects Laboratory | Stanford University | Dept. of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Not reported | Blackland Research and Extension Center, 720 East Blackland Road, Temple, TX 76502 | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Not reported | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Not reported | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | BC3-Basque Centre for Climate Change, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain | Tampa Bay Estuary Program, 263 13th Avenue South, St. Petersburg, FL 33701, USA | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | marion.potschin@nottingham.ac.uk | Anders.Siggins@csiro.au | djk@sfu.ca | rtbusing@aol.com | leah.oliver@epa.gov | gverutes@stanford.edu | abdelnouralex@gmail.com | yee.susan@epa.gov | yee.susan@epa.gov | markus.didion@wsl.ch | enviroatlas@epa.gov | epicapex@brc.tamus.edu | dotis@iastate.edu | Mulvaney.Kate@EPA.gov | dotis@iastate.edu | Not reported | chris.murphy@idfg.idaho.gov | sriffell@cfr.msstate.edu | javier.martinez@bc3research.org | esherwood@tbep.org | tedangradi@gmail.com | tedangradi@gmail.com |
EM ID
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EM-71 | EM-82 | EM-119 |
EM-129 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-367 |
EM-380 ![]() |
EM-447 | EM-451 |
EM-467 ![]() |
EM-491 | EM-592 | EM-652 | EM-685 | EM-703 |
EM-713 ![]() |
EM-718 ![]() |
EM-838 | EM-861 |
EM-863 ![]() |
EM-886 | EM-889 |
Summary Description
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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 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." | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Wildlife Products” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain, adjacency to coastal and wetland ecosystems, as well as adjacency to landscape protection zones." AUTHOR'S DESCRIPTION: "Wildlife Products…includes the provisioning of all non-edible raw material products that are gained through non-agriculutural practices or which are produced as a by-product of commercial and non-commercial forests, primarily in non-intensively used land or semi-natural and natural areas." | AUTHOR'S DESCRIPTION: "Carbon trading and its resultant market for carbon offsets are expected to drive investment in the sequestration of carbon through tree plantings (i.e., carbon plantings). Most carbon-planting investment has been in monocultures of trees that offer a rapid return on investment but have relatively little compositional and structural diversity (Bekessy & Wintle 2008; Munro et al. 2009). There are additional benefits available should carbon plantings comprise native species of diverse composition and age that are planted strategically to meet conservation and restoration objectives (hereafter ecological carbon plantings) (Bekessy &Wintle 2008; Dwyer et al. 2009; Bekessy et al. 2010). Ecological carbon plantings may increase availability of resources and refugia for native animals, but they often yield less carbon and are more expensive to establish than monocultures and therefore are less profitable…We used the tree-stand growth model 3-PG (physiological principles predicting growth) (Landsberg & Waring 1997) to simulate annual carbon sequestration under permanent carbon plantings in the part of the study area cleared for agriculture. The 3-PG model calculates total above- and below-ground biomass of a stand after accounting for soil water deficit, atmospheric vapor pressure deficits, and stand age…The 3-PG model was originally parameterized for a generic species, but species-specific parameters have since been calibrated for many commercially valuable trees (Paul et al. 2007) and most recently for mixed species used in permanent ecological restoration plantings (Polglase et al. 2008). We simulated four carbon-planting systems described in Polglase et al. (2008) for which the plants in the systems would grow in our study area. All species were native to areas of Australia with climate similar to that in the study area. We simulated the annual growth of three trees typically grown in monoculture (Eucalyptus globulus, native to Tasmania, constrained to precipitation ≥ 550 mm/year; Eucalyptus camaldulensis, native to the study area, constrained to 350–549 mm/year; Eucalyptus kochii, native to Western Australia, constrained to <350 mm/year). For the simulations of ecological carbon plantings we used a set of trees and shrubs representative of those planted for ecological restoration in temperate southern Australia (species list in England et al. 2006).We assumed the ecological carbon plantings were planted and managed in such a way as to comply with the definition of ecological restoration (Society for Ecological Restoration International Science and PolicyWorking Group 2004)." | ABSTRACT: "In this paper, we present a framework for valuing benefits for fisheries from protecting areas from degradation, using the example of the Strait of Georgia coho salmon fishery in southern British Columbia, Canada. Our study improves upon previous methods used to value fish habitat in two major respects. First, we use a bioeconomic model of the coho fishery to derive estimates of value that are consistent with economic theory. Second, we estimate the value of changing the quality of fish habitat by using empirical analyses to link fish population dynamics with indices of land use in surrounding watersheds." | ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range…The simulation of both stand-replacing and partial-stand disturbances across western Oregon improved agreement between simulated and actual data." Western Oregon forested ecoregions (Omernick classification) were Coastal Volcanics (1d), Mid-coastal Sedimentary (1g), Willamette Valley (3), West Cascade Lowlands (4a), West Cascade Montane (4b), Cascade Crest (4c), East Cascade Ponderosa Pine (9d), and East Cascade Pumice Plateau (9e). | AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | 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. "InVEST Coastal Blue Carbon models the carbon cycle through a bookkeeping-type approach (Houghton, 2003). This approach simplifies the carbon cycle by accounting for storage in four main pools (aboveground biomass, belowground biomass, standing dead carbon and sediment carbon… Accumulation of carbon in coastal habitats occurs primarily in sediments (Pendleton et al., 2012). The model requires users to provide maps of coastal ecosystems that store carbon, such as mangroves and seagrasses. Users must also provide data on the amount of carbon stored in the four carbon pools and the rate of annual carbon accumulation in the sediments. If local information is not available, users can draw on the global database of values for carbon stocks and accumulation rates sourced from the peer-reviewed literature that is included in the model. If data from field studies or other local sources are available, these values should be used instead of those in the global database. The model requires land cover maps, which represent changes in human use patterns in coastal areas or changes to sea level, to estimate the amount of carbon lost or gained over a specified period of time. The model quantifies carbon storage across the land or seascape by summing the carbon stored in these four carbon pools. | ABSTRACT: "We used a new ecohydrological model, Visualizing Ecosystems for Land Management Assessments (VELMA), to analyze the effects of forest harvest on catchment carbon and nitrogen dynamics. We applied the model to a 10 ha headwater catchment in the western Oregon Cascade Range where two major disturbance events have occurred during the past 500 years: a stand-replacing fire circa 1525 and a clear-cut in 1975. Hydrological and biogeochemical data from this site and other Pacific Northwest forest ecosystems were used to calibrate the model. Model parameters were first calibrated to simulate the postfire buildup of ecosystem carbon and nitrogen stocks in plants and soil from 1525 to 1969, the year when stream flow and chemistry measurements were begun. Thereafter, the model was used to simulate old-growth (1969–1974) and postharvest (1975–2008) temporal changes in carbon and nitrogen dynamics…" AUTHOR'S DESCRIPTION: "The soil column model consists of three coupled submodels:...a plant-soil model (Figure (A3)) that simulates ecosystem carbon storage and the cycling of C and N between a plant biomass layer and the active soil pools. Specifically, the plant-soil model simulates the interaction among aboveground plant biomass, soil organic carbon (SOC), soil nitrogen including dissolved nitrate (NO3), ammonium (NH4), and organic nitrogen, as well as DOC (equations (A7)–(A12)). Daily atmospheric inputs of wet and dry nitrogen deposition are accounted for in the ammonium pool of the shallow soil layer (equation (A13)). Uptake of ammonium and nitrate by plants is modeled using a Type II Michaelis-Menten function (equation (A14)). Loss of plant biomass is simulated through a density-dependent mortality. The mortality rate and the nitrogen uptake rate mimic the exponential increase in biomass mortality and the accelerated growth rate, respectively, as plants go through succession and reach equilibrium (equations (A14)–(A18)). Vertical transport of nutrients from one layer to another in a soil column is a function of water drainage (equations (A19)–(A22)). Decomposition of SOC follows first-order kinetics controlled by soil temperature and moisture content as described in the terrestrial ecosystem model (TEM) of Raich et al. [1991] (equations (A23)–(A26)). Nitrification (equations (A27)–(A30)) and denitrification (equations (A31)–(A34)) were simulated using the equations from the generalized model of N2 and N2O production of Parton et al. [1996, 2001] and Del Grosso et al. [2000]. [12] The soil column model is placed within a catchment framework to create a spatially distributed model applicable to watersheds and landscapes. Adjacent soil columns interact with each other through the downslope lateral transport of water and nutrients (Figure (A1)). Surface and subsurface lateral flow are routed using a multiple flow direction method [Freeman, 1991; Quinn et al., 1991]. As with vertical drainage of soil water, lateral subsurface downslope flow i | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion, storm damage, or coastal inundation during extreme events (UNEP-WCMC (United Nations Environment Programme, World Conservation Monitoring Centre), 2006; WRI (World Resources Institute), 2009), but is often quantified as wave energy attenuation, an intermediate service that contributes to shoreline protection by reducing rates of erosion or coastal inundation (Principeet al., 2012). From earlier models Gourlay (1996, 1997), Sheppard et al. (2005) developed a tool that can be used to estimate the percent attenuation in wave energy or wave height due to the presence of the reef. The decay in wave heights over the reef is described by dHrs/dx = -(fw/3π) (Hrs2/ (ηr+hr+ηw)2 where Hrs is significant wave height, fw is friction over the reef top, hr is water depth over the reef flat, and ηw is meteorological surge. Wave set-up on the reef-flat, ηr…" | 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 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 (i) examine the effect of the application of three different published Yasso07 parameter sets on simulated decay rate; (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood 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)." "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 litter (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 result of the simulation was an annual estimate of the remaining fraction of the initial mass, which could then be compared with observed data." | DATA FACT SHEET: "This EnviroAtlas national map estimates the total acres of agricultural crops within each 12-digit hydrologic unit (HUC) that have varying amounts of nearby forested pollinator habitat. The crop types selected from the U.S. Department of Agriculture Cropland Data Layer (CDL) require (or would benefit from) the presence of pollinators, but crops may have no nearby native pollinator habitat. This metric is based on the average flight distance of native bees, both social and solitary, that nest in woodland habitats and forage on native plants and cultivated crops." "The metric was generated using the ESRI ArcMap Neighborhood Distance tool in conjunction with a blended landcover grid, which included the 2006 National Land Cover Dataset (NLCD) and U.S. Department of Agriculture National Agricultural Statistics Service Cropland Data Layer (CDL). Pollinator habitat is defined as trees (fruit, nut, deciduous, and evergreen) for nesting and associated woodland for additional pollen sources. Crops that either require or benefit from pollination were selected and a distance measure of 2.8 kilometers (the average bee species’ foraging distance from the nest4) was used to assess presence or absence of nearby native pollinator habitat. The total area of crops without nearby pollinator habitat was summarized by 12-digit HUC boundaries taken from the NHDPlusV2 Watershed Boundary Dataset (WBD Snapshot)." | ABSTRACT: "APEX is a tool for managing whole farms or small watersheds to obtain sustainable production efficiency and maintain environmental quality. APEX operates on a daily time step and is capable of performing long term simulations (1-4000 years) at the whole farm or small watershed level. The watershed may be divided into many homogeneous (soils, land use, topography, etc.) subareas (<4000). The routing component simulates flow from one subarea to another through channels and flood plains to the watershed outlet and transports sediment, nutrients, and pesticides. This allows evaluation of interactions between fields in respect to surface run-on, sediment deposition and degradation, nutrient and pesticide transport and subsurface flow. Effects of terrace systems, grass waterways, strip cropping, buffer strips/vegetated filter strips, crop rotations, plant competition, plant burning, grazing patterns of multiple herds, fertilizer, irrigation, liming, furrow diking, drainage systems, and manure management (feed yards and dairies with or without lagoons) can be simulated and assessed. Most recent developments in APEX1501 include: • Flexible grazing schedule of multiple owners and herds across landscape and paddocks. • Wind dust distribution from feedlots. • Manure erosion from feedlots and grazing fields. • Optional pipe and crack flow in soil due to tree root growth. • Enhanced filter strip consideration. • Extended lagoon pumping and manure scraping options. • Enhanced burning operation. • Carbon pools and transformation equations similar to those in the Century model with the addition of the Phoenix C/N microbial biomass model. • Enhanced water table monitoring. • Enhanced denitrification methods. • Variable saturation hydraulic conductivity method. • Irrigation using reservoir and well reserves. • Paddy module for use with rice or wetland areas." | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. 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... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Savannah Sparrow (Passerculus sandwichensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SASP density = e^(-1.581362 + 0.0229603 *bbspath + 0.01024* grass3200 + 0.0255867 * hay3200) | ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "While it might be assumed that the economic value of a beach day and the value of a lost beach day would be symmetric, they are not quite the same in our analysis. This is because the town has many fixed costs for beach management, including staff, facility maintenance, and other amenities. These fixed costs are offset by the daily parking fees charged to out-of-town visitors and the various beach stickers available for town residents. Assuming the town does not make a profit and just breaks even on beach parking fees in relation to the costs incurred to provide the services, the net economic value of a day without a closure (benefits less costs) would simply be the consumer surplus for the public. However, this amount is different than the net economic value lost due to a beach closure, which includes the lost consumer surplus as well as the lost revenue to the town. This revenue is money the town would have collected to cover costs and therefore is considered a loss (negative producer surplus). Therefore, a beach day affected by a closure is valued as a loss of consumer surplus plus lost parking revenue…" Equation 3, page 19, provides the resulting formula for the value lost from a beach closure. | 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: "The 2015 announcement of The Dow Chemical Company's (Dow) Valuing Nature Goal, which aims to identify $1 billion in business value from projects that are better for nature, gives nature a spot at the project design table. To support this goal, Dow and The Nature Conservancy have extended their long-standing collaboration and are now working to develop a defensible methodology to support the implementation of the goal. This paper reviews the nature valuation methodology framework developed by the Collaboration in support of the goal. The nature valuation methodology is a three-step process that engages Dow project managers at multiple stages in the project design and capital allocation processes. The three-step process identifies projects that may have a large impact on nature and then promotes the use of ecosystem service tools, such as the Ecosystem Services Identification and Inventory Tool, to enhance the project design so that it better supports ecosystem health. After reviewing the nature valuation methodology, we describe the results from a case study of redevelopment plans for a 23-acre site adjacent to Dow's Michigan Operations plant along the Tittabawassee River." AUTHOR'S DESCRIPTION: "The ESII Tool measures the environmental impact or proposed land changes through eight specific ecosystem services: (i) water provisioning, (ii) air quality control (nitrogen and particulate removal), (iii) climate regulation (carbon uptake and localized air temperature regulation), (iv) erosion regulation, (v) water quality control (nitrogen and filtration), (vi) water temperature regulation, (vii) water quantity control, and (viii) aesthetics (noise and visual). The ESII Tool allows for direct comparison of the performance of these eight ecosystem services both across project sites and across project design proposals within a site." "The team was also asked to use an iterative design process using the ESII Tool to create alternative restoration scenarios…The project team developed three alternative restoration designs: i) standard brownfield restoration (i.e., cap and plant grass) on the ash pond and 4-D property (referred to as SBR); ii) ecological restoration (i.e., excavate ash and associated soil for secured disposal in approved landfill and restore historic forest, prairie, wetland) of the ash pond only, with SBR on the 4-D property (referred to as ER); and iii) ecological restoration on the ash pond and 4- D property (referred to as ER+)." | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. " | ABSTRACT: "Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The Artificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multicriteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed. " | ABSTRACT: "The Tampa Bay estuary is a unique and valued ecosystem that currently thrives between subtropical and temperate climates along Florida’s west-central coast. The watershed is considered urbanized (42 % lands developed); however, a suite of critical coastal habitats still persists. Current management efforts are focused toward restoring the historic balance of these habitat types to a benchmark 1950s period. We have modeled the anticipated changes to a suite of habitats within the Tampa Bay estuary using the sea level affecting marshes model (SLAMM) under various sea level rise (SLR) scenarios. Modeled changes to the distribution and coverage of mangrove habitats within the estuary are expected to dominate the overall proportions of future critical coastal habitats. Modeled losses in salt marsh, salt barren, and coastal freshwater wetlands by 2100 will significantly affect the progress achieved in ‘‘Restoring the Balance’’ of these habitat types over recent periods…" | 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 7seminatural 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. " | 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." |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified | None identified | None Identified | Not applicable | None identified | None identified | None identified | None identified | None identified | None Identified | None identified | None identified | Economic value of protecting coastal beach water quality from contamination caused closures. | None identified | Use ESII to answer the following business decision question: how can Dow close the ash pond while enhancing ecosystem services to Dow and the community and creating local habitat, for a lesser overall cost to Dow than the option currently defined? | None identified | None reported | None identified | None identified | None identified | None identified |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | No additional description provided | Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm. | No additional description provided | Coastal to montane, Pacific Northwest US (Oregon) forests. | nearshore; <1.5 km offshore; <12 m depth | Land use land class; habitat type | Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter. | 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 | No additional description provided | Prairie pothole region of north-central Iowa | Four separate beaches within the community of Barnstable | Prairie Pothole Region of Iowa | No additional description provided | restored, enhanced and created wetlands | Conservation Reserve Program lands left to go fallow | Watersheds surrounding Santa Fe and Albuquerque, New Mexico | No additional description provided | Waterfront districts on south Lake Michigan and south lake Erie | Waterfront districts on south Lake Michigan and south lake Erie |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | Four carbon-planting systems including hardwood and mallee monoculture plantings, and mixed species ecological carbon plantings | Habitat quality | Two scenarios modelled, forests with and without fire | Not applicable | Land use land cover changes; habitat disturbance | Forest management (harvest/no harvest) | No scenarios presented | No scenarios presented |
No scenarios presented ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Alternative restoration designs | Sites, function or habitat focus | N/A | N/A | Varying sea level rise (baseline - 2m), and two habitat adaption strategies | N/A | N/A |
EM ID
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EM-71 | EM-82 | EM-119 |
EM-129 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-367 |
EM-380 ![]() |
EM-447 | EM-451 |
EM-467 ![]() |
EM-491 | EM-592 | EM-652 | EM-685 | EM-703 |
EM-713 ![]() |
EM-718 ![]() |
EM-838 | EM-861 |
EM-863 ![]() |
EM-886 | EM-889 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | 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) View EM Runs |
Method + Application (multiple runs exist) View EM Runs ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
Method + Application | Method Only | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application |
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 | 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 |
Application of existing model ?Comment:Models developed by Quamen (2007). |
New or revised model | New or revised model | Application of existing model | Application of existing model | New or revised model | Application of existing model | Application of existing model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
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EM-71 | EM-82 | EM-119 |
EM-129 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-367 |
EM-380 ![]() |
EM-447 | EM-451 |
EM-467 ![]() |
EM-491 | EM-592 | EM-652 | EM-685 | EM-703 |
EM-713 ![]() |
EM-718 ![]() |
EM-838 | EM-861 |
EM-863 ![]() |
EM-886 | EM-889 |
EM Temporal Extent
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2007-2008 | Not reported | 2000 | 2009-2050 | 1989-1999 | >650 yrs | 2006-2007 | Not applicable | 1969-2008 | 2006-2007, 2010 | 2006-2007, 2010 | 1993-2013 | 2001-2015 | Not applicable | 1992-2007 | July 1, 2011 to June 31, 2016 | 1987-2007 | Not reported | 2010-2011 | 2008 | 2011 | 2002-2100 | 2022 | 2022 |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-dependent | time-dependent | 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 |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | future time | Not applicable | past time | Not applicable | Not applicable | future time | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete | Not applicable | discrete | discrete | Not applicable | Not applicable | discrete | Not applicable | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | 1 | Not applicable | 1 | Not applicable | 1 | 1 | Not applicable | Not applicable | 1 | Not applicable | 1 | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Month | Not applicable | Year | Not applicable | Year | Day | Not applicable | Not applicable | Year | Not applicable | Day | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
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EM-71 | EM-82 | EM-119 |
EM-129 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-367 |
EM-380 ![]() |
EM-447 | EM-451 |
EM-467 ![]() |
EM-491 | EM-592 | EM-652 | EM-685 | EM-703 |
EM-713 ![]() |
EM-718 ![]() |
EM-838 | EM-861 |
EM-863 ![]() |
EM-886 | EM-889 |
Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Physiographic or Ecological | Physiographic or ecological | Physiographic or ecological | Physiographic or Ecological | Not applicable | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Geopolitical | Geopolitical | Not applicable | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Geopolitical | Geopolitical |
Spatial Extent Name
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Central French Alps | Central French Alps | The EU-25 plus Switzerland and Norway | Agricultural districts of the state of South Australia | South Thompson watershed | Western Oregon, north of 43.00 N to Washington border | St. Croix, U.S. Virgin Islands | Not applicable | H. J. Andrews LTER WS10 | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Switzerland | conterminous United States | Not applicable | CREP (Conservation Reserve Enhancement Program) wetland sites | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | CREP (Conservation Reserve Enhancement Program | Dow Midland Operations facility ash pond and Posey Riverside (4-D property) | Wetlands in idaho | Piedmont Ecoregion | Santa Fe Fireshed | Tampa Bay estuary watershed | Great Lakes waterfront | Great Lakes waterfront |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | 10,000-100,000 km^2 | 10-100 km^2 | Not applicable | 10-100 ha | 100-1000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | >1,000,000 km^2 | Not applicable | 1-10 km^2 | 10-100 ha | 10,000-100,000 km^2 | 10-100 ha | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | 1000-10,000 km^2. |
EM ID
em.detail.idHelp
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EM-71 | EM-82 | EM-119 |
EM-129 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-367 |
EM-380 ![]() |
EM-447 | EM-451 |
EM-467 ![]() |
EM-491 | EM-592 | EM-652 | EM-685 | EM-703 |
EM-713 ![]() |
EM-718 ![]() |
EM-838 | EM-861 |
EM-863 ![]() |
EM-886 | EM-889 |
EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all 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 distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | Not applicable | volume, for 3-D feature | other (specify), for irregular (e.g., stream reach, lake basin) | 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) | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable |
Spatial Grain Size
em.detail.spGrainSizeHelp
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20 m x 20 m | 20 m x 20 m | 1 km x 1 km | 1 ha x 1 ha | Not applicable | 0.08 ha | Not applicable | user-specified | 30 m x 30 m surface pixel and 2-m depth soil column | 10 m x 10 m | 10 m x 10 m | 5 sites | irregular | homogenous subareas | multiple, individual, irregular shaped sites | by beach site | multiple, individual, irregular sites | map unit | Not applicable | Not applicable | 30 m | 10 x 10 m | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-71 | EM-82 | EM-119 |
EM-129 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-367 |
EM-380 ![]() |
EM-447 | EM-451 |
EM-467 ![]() |
EM-491 | EM-592 | EM-652 | EM-685 | EM-703 |
EM-713 ![]() |
EM-718 ![]() |
EM-838 | EM-861 |
EM-863 ![]() |
EM-886 | EM-889 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Analytic | Logic- or rule-based | Numeric | Analytic | Numeric | Analytic | Analytic | Numeric | Analytic | Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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EM ID
em.detail.idHelp
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EM-71 | EM-82 | EM-119 |
EM-129 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-367 |
EM-380 ![]() |
EM-447 | EM-451 |
EM-467 ![]() |
EM-491 | EM-592 | EM-652 | EM-685 | EM-703 |
EM-713 ![]() |
EM-718 ![]() |
EM-838 | EM-861 |
EM-863 ![]() |
EM-886 | EM-889 |
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | No | Yes | Yes | No | Yes | Not applicable | Yes | Yes | Yes | No | No | Not applicable | Unclear | Yes | Unclear | Unclear | No | Yes | Unclear | No | No | No |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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Yes | No | No | No | No | No | Yes | Not applicable | No | No | No | No | No | Not applicable | No | No | No | No | No | No | No | No | No | No |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None |
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None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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No | No | Yes | No | No | Yes | No | Not applicable | No | Yes | Yes | Yes | No | Not applicable | Unclear | No | No | Unclear | No | No | No | No | No | No |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | No | No | Yes | Not applicable | No | No | No | No | No | Not applicable | No | No | No | No | No | No | No | No | No | No |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | No | Yes | No | No | Not applicable | Yes | No | No | No | No | Not applicable | No |
No ?Comment:n/a |
No | No | No | Yes | No | No | Yes | Yes |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable | Yes | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-71 | EM-82 | EM-119 |
EM-129 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-367 |
EM-380 ![]() |
EM-447 | EM-451 |
EM-467 ![]() |
EM-491 | EM-592 | EM-652 | EM-685 | EM-703 |
EM-713 ![]() |
EM-718 ![]() |
EM-838 | EM-861 |
EM-863 ![]() |
EM-886 | EM-889 |
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None | None |
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None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-71 | EM-82 | EM-119 |
EM-129 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-367 |
EM-380 ![]() |
EM-447 | EM-451 |
EM-467 ![]() |
EM-491 | EM-592 | EM-652 | EM-685 | EM-703 |
EM-713 ![]() |
EM-718 ![]() |
EM-838 | EM-861 |
EM-863 ![]() |
EM-886 | EM-889 |
None | None | None | None |
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None |
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None | None |
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None | None | None | None |
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None | None | None | None | None |
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None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-71 | EM-82 | EM-119 |
EM-129 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-367 |
EM-380 ![]() |
EM-447 | EM-451 |
EM-467 ![]() |
EM-491 | EM-592 | EM-652 | EM-685 | EM-703 |
EM-713 ![]() |
EM-718 ![]() |
EM-838 | EM-861 |
EM-863 ![]() |
EM-886 | EM-889 |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | 45.05 | 50.53 | -34.9 | 49.29 | 44.66 | 17.75 | -9999 | 44.25 | 17.73 | 17.73 | 46.82 | 39.5 | Not applicable | 42.62 | 41.64 | 42.62 | 43.6 | 44.06 | 36.23 | 35.86 | 27.76 | 42.26 | 42.26 |
Centroid Longitude
em.detail.ddLongHelp
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6.4 | 6.4 | 7.6 | 138.7 | -123.8 | -122.56 | -64.75 | -9999 | -122.33 | -64.77 | -64.77 | 8.23 | -98.35 | Not applicable | -93.84 | -70.29 | -93.84 | -84.24 | -114.69 | -81.9 | -105.76 | -82.54 | -87.84 | -87.84 |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | NAD83 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Provided | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated |
EM ID
em.detail.idHelp
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EM-71 | EM-82 | EM-119 |
EM-129 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-367 |
EM-380 ![]() |
EM-447 | EM-451 |
EM-467 ![]() |
EM-491 | EM-592 | EM-652 | EM-685 | EM-703 |
EM-713 ![]() |
EM-718 ![]() |
EM-838 | EM-861 |
EM-863 ![]() |
EM-886 | EM-889 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Rivers and Streams | Near Coastal Marine and Estuarine | Forests | Near Coastal Marine and Estuarine | Inland Wetlands | Near Coastal Marine and Estuarine | Rivers and Streams | Ground Water | Forests | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Forests | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Inland Wetlands | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Inland Wetlands | Agroecosystems | Grasslands | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows. | Subalpine terraces, grasslands, and meadows. | Not applicable | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Rivers and streams | Primarily conifer forest | stony coral reef | user specified | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | Coral reefs | Coral reefs | forests | Terrestrial | Terrestrial environment associated with agroecosystems | Grassland buffering inland wetlands set in agricultural land | Saltwater beach | Wetlands buffered by grassland within agroecosystems | Ash pond and surrounding environment | created, restored and enhanced wetlands | grasslands | watersheds | Esturary and associated urban and terrestrial environment | Lake Michigan waterfront | Lake Michigan & Lake Erie waterfront |
EM Ecological Scale
em.detail.ecoScaleHelp
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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 is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 | 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 |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-71 | EM-82 | EM-119 |
EM-129 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-367 |
EM-380 ![]() |
EM-447 | EM-451 |
EM-467 ![]() |
EM-491 | EM-592 | EM-652 | EM-685 | EM-703 |
EM-713 ![]() |
EM-718 ![]() |
EM-838 | EM-861 |
EM-863 ![]() |
EM-886 | EM-889 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Community | Not applicable | Species |
Other (Comment) ?Comment:Coho salmon stock |
Species | Guild or Assemblage | Not applicable | Not applicable | Community | Guild or Assemblage | Community | Guild or Assemblage | Not applicable | Species | Not applicable | Individual or population, within a species | Not applicable | Not applicable | Species | Not applicable | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-71 | EM-82 | EM-119 |
EM-129 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-367 |
EM-380 ![]() |
EM-447 | EM-451 |
EM-467 ![]() |
EM-491 | EM-592 | EM-652 | EM-685 | EM-703 |
EM-713 ![]() |
EM-718 ![]() |
EM-838 | EM-861 |
EM-863 ![]() |
EM-886 | EM-889 |
None Available | None Available | None Available |
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None Available | None Available | None Available | None Available | None Available |
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None Available |
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None Available |
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None Available | None Available |
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None Available | None Available | None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-71 | EM-82 | EM-119 |
EM-129 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-367 |
EM-380 ![]() |
EM-447 | EM-451 |
EM-467 ![]() |
EM-491 | EM-592 | EM-652 | EM-685 | EM-703 |
EM-713 ![]() |
EM-718 ![]() |
EM-838 | EM-861 |
EM-863 ![]() |
EM-886 | EM-889 |
None |
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None | 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-71 | EM-82 | EM-119 |
EM-129 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-367 |
EM-380 ![]() |
EM-447 | EM-451 |
EM-467 ![]() |
EM-491 | EM-592 | EM-652 | EM-685 | EM-703 |
EM-713 ![]() |
EM-718 ![]() |
EM-838 | EM-861 |
EM-863 ![]() |
EM-886 | EM-889 |
None | None |
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None |
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None | None | None | None | None |
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None |
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None | None | None | None |