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-65 | EM-94 | EM-97 |
EM-98 ![]() |
EM-111 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-349 ![]() |
EM-462 | EM-492 |
EM-541 ![]() |
EM-584 ![]() |
EM-626 |
EM-668 ![]() |
EM-700 |
EM-713 ![]() |
EM-718 ![]() |
EM-729 ![]() |
EM-820 | EM-876 | EM-947 |
EM Short Name
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Green biomass production, Central French Alps | Reduction in pesticide runoff risk, Europe | AnnAGNPS, Kaskaskia River watershed, IL, USA | PATCH, western USA | InVEST water yield, Hood Canal, WA, USA | Salmon habitat values, west coast of Canada | FORCLIM v2.9, Western OR, USA | InVEST (v1.004) Carbon, Indonesia | Value of finfish, St. Croix, USVI | EnviroAtlas - Restorable wetlands | InVEST fisheries, lobster, South Africa | Nutrient Tracking Tool (NTT), north central Texas, USA | SolVES, Shoshone NF, WY | Fish nutrient cycling , Ohio, USA | Mallard recruits, CREP wetlands, Iowa, USA | ESII Tool, Michigan, USA | WESP: Riparian & stream habitat, ID, USA | WESP: Urban Stormwater Treatment, ID, USA | MMI method for aquatic surveys | Neighborhood greenness and health, FL, USA | C-GEM, Lousiana continental shelf, USA |
EM Full Name
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Green biomass production, Central French Alps | Reduction in pesticide runoff risk, Europe | AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | PATCH (Program to Assist in Tracking Critical Habitat), western USA | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) water yield, Hood Canal, WA, USA | 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 | InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004) carbon storage and sequestration, Sumatra, Indonesia | Relative value of finfish (on reef), St. Croix, USVI | US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa | Nutrient Tracking Tool (NTT), Upper North Bosque River watershed, Texas, USA | SolVES, Social Values for Ecosystem Services, Shoshone National Forest, WY | Nutrient Cycling by gizzard shad, Ohio, USA | Mallard 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 | WESP: Urban Stormwater Treament, ID, USA | Multimetric Indice (MMI) method for large scale aquatic surveys | Neighborhood greenness and chronic health conditions in Medicare beneficiaries, Miami-Dade County, Florida, USA | Carbon Generic Estuary Model (C-GEM), Lousiana Continental Shelf, USA |
EM Source or Collection
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EU Biodiversity Action 5 | None | US EPA | US EPA | InVEST | None | US EPA | InVEST | US EPA | US EPA | EnviroAtlas | InVEST | None | None | None | None | None | None | None | US EPA | None | US EPA |
EM Source Document ID
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260 | 255 | 137 | 2 | 205 | 286 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
309 | 335 | 262 |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
354 | 369 | 385 |
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. |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
403 | 417 | 441 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lautenbach, S., Maes, J., Kattwinkel, M., Seppelt, R., Strauch, M., Scholz, M., Schulz-Zunkel, C., Volk, M., Weinert, J. and Dormann, C. | Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Carroll, C, Phillips, M. K. , Lopez-Gonzales, C. A and Schumaker, N. H. | Toft, J. E., Burke, J. L., Carey, M. P., Kim, C. K., Marsik, M., Sutherland, D. A., Arkema, K. K., Guerry, A. D., Levin, P. S., Minello, T. J., Plummer, M., Ruckelshaus, M. H., and Townsend, H. M. | 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. | 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. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | US EPA Office of Research and Development - National Exposure Research Laboratory | Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby | Saleh, A., O. Gallego, E. Osei, H. Lal, C. Gross, S. McKinney, and H. Cover | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | Vanni, M.J., Bowling, A.M., Dickman, E.M., Hale, R.S., Higgins, K.A., Horgan, M.J., Knoll, L.B., Renwick, W.H., and R.A. Stein | 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 | Murphy, C. and T. Weekley | Stoddard, J.L., Herlihy, A.T., Peck, D.V., Hughes, R.M., Whittier, T.R., and E. Tarquinio | Brown, S. C., J. Lombard, K. Wang, M. M. Byrne, M. Toro, E. Plater-Zyberk, D. J. Feaster, J. Kardys, M. I. Nardi, G. Perez-Gomez, H. M. Pantin, and J. Szapocznik | Jarvis, B.M., Lehrter, J.C., Lowe, L.L., Hagy, J.D., Wan, Y., Murrell, M.C., Ko, D.S., Penta, B., and R.W. Gould |
Document Year
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2011 | 2012 | 2011 | 2006 | 2013 | 2003 | 2007 | 2014 | 2014 | 2013 | 2018 | 2011 | 2014 | 2006 | 2010 | 2019 | 2012 | 2012 | 2008 | 2016 | 2020 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Mapping water quality-related ecosystem services: concepts and applications for nitrogen retention and pesticide risk reduction | AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | Defining recovery goals and strategies for endangered species: The wolf as a case study | From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA | Valuing freshwater salmon habitat on the west coast of Canada | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Ecosystem services reinforce Sumatran tiger conservation in land use plans | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | EnviroAtlas - National | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals | Nutrient Tracking Tool - a user-friendly tool for calculating nutrient reductions for water quality trading | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | Nutrient cycling by fish supports relatively more primary production as lake productivity increases | 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. | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | A process for creating multimetric indices for large-scale A process for creating multimetic indices for large-scale aquatic surveys | Neighborhood greenness and chronic health conditions in Medicare beneficiaries | Modeling spatiotemporal patterns of ecosystem metabolism and organic carbon dynamics affecting hypoxia on the Louisiana continental shelf |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published report | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-65 | EM-94 | EM-97 |
EM-98 ![]() |
EM-111 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-349 ![]() |
EM-462 | EM-492 |
EM-541 ![]() |
EM-584 ![]() |
EM-626 |
EM-668 ![]() |
EM-700 |
EM-713 ![]() |
EM-718 ![]() |
EM-729 ![]() |
EM-820 | EM-876 | EM-947 |
Not applicable | Not applicable | https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | https://www.epa.gov/enviroatlas | https://www.naturalcapitalproject.org/invest/ | http://ntt.tiaer.tarleton.edu/welcomes/new?locale=en | Not applicable | Not applicable | Not applicable | https://www.esiitool.com/ | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | |
Contact Name
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Sandra Lavorel | Sven Lautenbach | Yongping Yuan | Carlos Carroll | J.E. Toft | Duncan Knowler | Richard T. Busing | Nirmal K. Bhagabati | Susan H. Yee | EnviroAtlas Team | Michelle Ward | Ali Saleh | Benson Sherrouse | Michael Vanni | David Otis | Not reported | Chris Murphy | Chris Murphy | John Stoddard | Scott C. Brown | Brandon Jarvis |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany | U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | Klamath Center for Conservation Research, Orleans, CA 95556 | The Natural Capital Project, Stanford University, 371 Serra Mall, Stanford, CA 94305-5020, USA | 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 | The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Not reported | ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia | Texas Institute for Applied Environmental Research-Tarleton State University, Stephenville, TX 76401,USA | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | Dept of Environmental toxocology, C.emson Univ. Pendleton, South Carolina 29670, USA | 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 | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | 200 SW 35th St., Corvallis, OR 97333 | Department of Public Health Sciences, University of Miami Miller School of Medicine, 1120 NW 14th Street, Clinical Research Building (CRB), Room 1065, Miami FL 33136 | 1US EPA, Office of Research and Development, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA Office of Research and Development, U.S. EPA, Gulf Breeze, FL, USA |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | sven.lautenbach@ufz.de | yuan.yongping@epa.gov | carlos@cklamathconservation.org | jetoft@stanford.edu | djk@sfu.ca | rtbusing@aol.com | nirmal.bhagabati@wwfus.org | yee.susan@epa.gov | enviroatlas@epa.gov | m.ward@uq.edu.au | saleh@tiaer.tarleton.edu | bcsherrouse@usgs.gov | vannimj@muohio.edu | dotis@iastate.edu | Not reported | chris.murphy@idfg.idaho.gov | chris.murphy@idfg.idaho.gov | stoddard.john@epa.gov | sbrown@med.miami.edu | jarvis.brandon@epa.gov |
EM ID
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EM-65 | EM-94 | EM-97 |
EM-98 ![]() |
EM-111 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-349 ![]() |
EM-462 | EM-492 |
EM-541 ![]() |
EM-584 ![]() |
EM-626 |
EM-668 ![]() |
EM-700 |
EM-713 ![]() |
EM-718 ![]() |
EM-729 ![]() |
EM-820 | EM-876 | EM-947 |
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. 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., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production 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, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. 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 ecosystem properties. 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 (see Albert et al. 2010)." | AUTHOR'S DESCRIPTION: "We used a spatially explicit model to predict the potential exposure of small streams to insecticides (run-off potential – RP) as well as the resulting ecological risk (ER) for freshwater fauna on the European scale (Schriever and Liess 2007; Kattwinkel et al. 2011)...The recovery of community structure after exposure to insecticides is facilitated by the presence of undisturbed upstream stretches that can act as sources for recolonization (Niemi et al. 1990; Hatakeyama and Yokoyama 1997). In the absence of such sources for recolonization, the structure of the aquatic community at sites that are exposed to insecticides differs significantly from that of reference sites (Liess and von der Ohe 2005)...Hence, we calculated the ER depending on RP for insecticides and the amount of recolonization zones. ER gives the percentage of stream sites in each grid cell (10 × 10 km) in which the composition of the aquatic community deviated from that of good ecological status according to the WFD. In a second step, we estimated the service provided by the environment comparing the ER of a landscape lacking completely recolonization sources with that of the actual landscape configuration. Hence, the ES provided by non-arable areas (forests, pastures, natural grasslands, moors and heathlands) was calculated as the reduction of ER for sensitive species. The service can be thought of as a habitat provisioning/nursery service that leads to an improvement of ecological water quality." | AUTHORS' DESCRIPTION: "AnnAGNPS is an advanced simulation model developed by the USDA-ARS and Natural Resource Conservation Services (NRCS) to help evaluate watershed response to agricultural management practices. It is a continuous simulation, daily time step, pollutant loading model designed to simulate water, sediment and chemical movement from agricultural watersheds.p. 198" | **Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** AUTHORS' DESCRIPTION: "PATCH (program to assist in tracking critical habitat), the SEPM used here, is designed for studying territorial vertebrates. It links the survival and fecundity of individual animals to geographic information system (GIS) data on mortality risk and habitat productivity at the scale of an individual or pack territory. Territories are allocated by intersecting the GIS data with an array of hexagonal cells. The different habitat types in the GIS maps are assigned weights based on the relative levels of fecundity and survival expected in those habitat classes. Base survival and reproductive rates, derived from published field studies, are then supplied to the model as a population projection matrix. The model scales these base matrix values using the mean of the habitat weights within each hexagon, with lower means translating into lower survival rates or reproductive output. Each individual in the population is tracked through a yearly cycle of survival, fecundity, and dispersal events. Environmental stochasticity is incorporated by drawing each year’s base population matrix from a randomized set of matrices whose elements were drawn from a beta (survival) or normal (fecundity) distribution. Adult organisms are classified as either territorial or floaters. The movement of territorial individuals is governed by a parameter for site fidelity, but floaters must always search for available breeding sites. As pack size increases, pack members in the model have a greater tendency to disperse and search for new available breeding sites. Movement decisions use a directed random walk that combines varying proportions of randomness, correlation, and attraction to higher-quality habitat (Schumaker 1998)." | InVEST Water Yield and Scarcity Model Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "We modelled discharge and total nitrogen for the 153 perennial sub- watersheds in Hood Canal based on spatial variation in hydrological factors, land and water use, and vegetation.To do this, we reparame-terized a set of fresh water models available in the InVEST tool (Tallis and Polasky, 2009; Kareiva et al., 2011)… We modelled discharge using the InVESTWater Yield and Scarcity model. The model estimates discharge for user-defined subwatersheds based on the average annual precipitation, annual reference evapotranspiration, and a correction factor for vegetation type, soil depth, plant available water content, land use and land cover, root depth, elevation, saturated hydraulic conductivity, and consumptive water use" (2) | 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). | 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)... We mapped biomass carbon by assigning carbon values (in ton ha_1) for aboveground, belowground, and dead organic matter to each LULC class based on values from literature, as described in Tallis et al. (2010). We mapped soil carbon separately, as large quantities of carbon are stored in peat soil (Page et al., 2011). We estimated total losses in peat carbon over 50 years into the future scenarios, using reported annual emission rates for specific LULC transitions on peat (Uryu et al., 2008)...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." | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(5) value of finfish," | DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map." | AUTHOR'S DESCRIPTION: "Here we develop a method for assessing future scenarios of environmental management change that improve coastal ecosystem services and thereby, support the success of the SDGs. We illustrate application of the method using a case study of South Africa’s West Coast Rock Lobster fishery within the Table Mountain National Park (TMNP) Marine Protected Area...We calculated the retrospective and current value of the West Coast Rock Lobster fishery using published and unpublished data from various sources and combined the market worth of landed lobster from recreational fishers, small-scale fisheries (SSF), large-scale fisheries (LSF) and poachers. Then using the InVEST tool, we combined data to build scenarios that describe possible futures for the West Coast Rock Lobster fishery (see Table 1). The first scenario, entitled ‘Business as Usual’ (BAU), takes the current situation and most up-to-date data to model the future if harvest continues at the existing rate. The second scenario is entitled ‘Redirect the Poachers’ (RP), which attempts to model implementation of strict management, whereby poaching is minimised from the Marine Protected Area and other economic and nutritional sources are made available through government initiatives. The third scenario, entitled ‘Large Scale Cutbacks’ (LSC), excludes large-scale fisheries from harvesting West Coast Rock Lobster within the TMNP Marine Protected Area." | ABSTRACT: "The Nutrient Tracking Tool (NTrT) is an enhanced version of the Nitrogen Trading Tool, a user-friendly Web-based computer program originally developed by the USDA. The NTrT estimates nutrient (nitrogen and phosphorus) and sediment losses from fields managed under a variety of cropping patterns and management practices through its user-friendly, Web-based linkage to the Agricultural Policy Environmental eXtender (APEX) model. It also accesses the USDA Natural Resources Conservation Service’s Web Soil Survey to utilize their geographic information system interface for field and operation identification and load soil information. The NTrT provides farmers, government officials, and other users with a fast and efficient method of estimating nitrogen and phosphorus credits for water quality trading, as well as other water quality, water quantity, and farm production impacts associated with conservation practices. The information obtained from the tool can help farmers determine the most cost-effective conservation practice alternatives for their individual operations and provide them with more advantageous options in a water quality credit trading program. An application of the NTrT to evaluate conservation practices on fields receiving dairy manure in a north central Texas watershed indicates that phosphorus-based application rates, filter strips, forest buffers, and complete manure export off the farm all result in reduced phosphorus losses from the fields on which those practices were implemented. When compared to a base¬line condition that entailed manure application at the nitrogen agronomic rate of receiving crops, the reductions in total phosphorus losses associated with these practices ranged from 15% (2P Rate scenario) to 76% (forest buffer scenario)." AUTHOR'S DESCRIPTION: "This paper provides a brief overview of the NTrT and presents results of verification and application of the tool on a selected field on a test field in the Upper North Bosque River (UNBR) watershed in Texas…simulations for the baseline and all five alternative scenarios were replicated for each of 90 specific soil types in Erath County, Texas…results reported and discussed in this report represent the averages of the output for all soil types." | 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, social value information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems.” | ABSTRACT: "Animals can be important in nutrient cycling in particular ecosystems, but few studies have examined how this importance varies along environmental gradients. In this study we quantified the nutrient cycling role of an abundant detritivorous fish species, the gizzard shad (Dorosoma cepedianum), in reservoir ecosystems along a gradient of ecosystem productivity. Gizzard shad feed mostly on sediment detritus and excrete sediment-derived nutrients into the water column, thereby mediating a cross-habitat translocation of nutrients to phytoplankton. We quantified nitrogen and phosphorus cycling (excretion) rates of gizzard shad, as well as nutrient demand by phytoplankton, in seven lakes over a four-year period (16 lake-years). The lakes span a gradient of watershed land use (the relative amounts of land used for agriculture vs. forest) and productivity. As the watersheds of these lakes became increasingly dominated by agricultural land, primary production rates, lake trophic state indicators (total phosphorus and chlorophyll concentrations), and nutrient flux through gizzard shad populations all increased. Nutrient cycling by gizzard shad supported a substantial proportion of primary production in these ecosystems, and this proportion increased as watershed agriculture (and ecosystem productivity) increased. In the four productive lakes with agricultural watersheds (.78% agricultural land), gizzard shad supported on average 51% of phytoplankton primary production (range 27–67%). In contrast, in the three relatively unproductive lakes in forested or mixed-land-use watersheds (.47% forest, ,52% agricultural land), gizzard shad supported 18% of primary production (range 14–23%). Thus, along a gradient of forested to agricultural landscapes, both watershed nutrient inputs and nutrient translocation by gizzard shad increase, but our data indicate that the importance of nutrient translocation by gizzard shad increases more rapidly. Our results therefore support the hypothesis that watersheds and gizzard shad jointly regulate primary production in reservoir ecosystems " | 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. | 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: "Differences in sampling and laboratory protocols, differences in techniques used to evaluate metrics, and differing scales of calibration and application prohibit the use of many existing multimetric indices (MMIs) in large-scale bioassessments. We describe an approach to developing MMIs of ecological condition that is applicable to a variety of biological assemblage types and to spatially extensive (regional, national) aquatic resource surveys. The process involves testing the performance characteristics of candidate metrics in several categories that correspond to key dimensions of biotic condition. The performance characteristics include: information content (range), reproducibility, calibration for natural gradients, responsiveness to stressor gradients, and independence from other metrics. The best-performing metric from each category is included in the final MMI. The consistency of the process enables development of separate MMIs in different regions that can be combined in a national assessment and that are more comparable across regions and taxonomic groups than a set of independently developed MMIs would be. Range: Generally eliminate metrics if their range is <4 or if > 1/3 of samples have values = 0. Very few macroinvertebrate metrics are eliminated by this test. It does eliminate a large number of potentially poor metrics for assemblages with fewer taxa (e.g., fish). Reproducibility: We quantify metric reproducibility with a variant of the signal:noise ratio (S/N). S/N is the ratio of the variance among all sites (signal) to the variance of repeated visits to the same site (noise). S/N values 1 indicate that visiting a single site twice yields as much metric variability as visiting 2 different sites. Natural gradient calibration: Focusing solely on reference-site data and to quantify the remaining correspondence between the metric value and the natural gradient. Responsiveness: The ability of a metric to distinguish least-disturbed (reference) from most-disturbed sites. We identify the metrics that have the highest responsiveness (t-scores) within each class and aggregated ecoregion. Redundancy: We often consider metrics as too strongly correlated when their Pearson correlation coefficients at least-disturbed sites are > |0.71| (R2 = 0.5). | ABSTRACT: "Introduction: Prior studies suggest that exposure to the natural environment may impact health. The present study examines the association between objective measures of block-level greenness (vegetative presence) and chronic medical conditions, including cardiometabolic conditions, in a large population-based sample of Medicare beneficiaries in Miami-Dade County, Florida. Methods: The sample included 249,405 Medicare beneficiaries aged >=65 years whose location (ZIP+4) within Miami-Dade County, Florida, did not change, from 2010 to 2011. Data were obtained in 2013 and multilevel analyses conducted in 2014 to examine relationships between greenness, measured by mean Normalized Difference Vegetation Index from satellite imagery at the Census block level, and chronic health conditions in 2011, adjusting for neighborhood median household income, individual age, gender, race, and ethnicity. Results: Higher greenness was significantly associated with better health, adjusting for covariates: An increase in mean block-level Normalized Difference Vegetation Index from 1 SD less to 1 SD more than the mean was associated with 49 fewer chronic conditions per 1,000 individuals, which is approximately similar to a reduction in age of the overall study population by 3 years. This same level of increase in mean Normalized Difference Vegetation Index was associated with a reduced risk of diabetes by 14%, hypertension by 13%, and hyperlipidemia by 10%. Planned post-hoc analyses revealed stronger and more consistently positive relationships between greenness and health in lower- than higher-income neighborhoods. Conclusions: Greenness or vegetative presence may be effective in promoting health in older populations, particularly in poor neighborhoods, possibly due to increased time outdoors, physical activity, or stress mitigation." | ABSTRACT: "The hypoxic zone on the Louisiana Continental Shelf (LCS) forms each summer due to nutrient‐enhanced primary production and seasonal stratification associated with freshwater discharges from the Mississippi/Atchafalaya River Basin (MARB). Recent field studies have identified highly productive shallow nearshore waters as an important component of shelf‐wide carbon production contributing to hypoxia formation. This study applied a three‐dimensional hydrodynamic‐biogeochemical model named CGEM (Coastal Generalized Ecosystem Model) to quantify the spatial and temporal patterns of hypoxia, carbon production, respiration, and transport between nearshore and middle shelf regions where hypoxia is most prevalent. We first demonstrate that our simulations reproduced spatial and temporal patterns of carbon production, respiration, and bottom‐water oxygen gradients compared to field observations. We used multiyear simulations to quantify transport of articulate organic carbon (POC) from nearshore areas where riverine organic matter and phytoplankton carbon production are greatest. The spatial displacement of carbon production and respiration in our simulations was created by westward and offshore POC flux via phytoplankton carbon flux in the surface layer and POC flux in the bottom layer, supporting heterotrophic respiration on the middle shelf where hypoxia is frequently observed. These results support existing studies suggesting the importance of offshore carbon flux to hypoxia formation, particularly on the west shelf where hypoxic conditions are most variable. " |
Specific Policy or Decision Context Cited
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None identified | European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | Not reported | AUTHOR DESCRIPTION: "Comprehensive habitat and viability assessments. . . [more rigoursly defined] can clarify debate of goals for recovery of large carnivores"; Endangered Species Act and related litigation | Land use change | 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. | None identified | None Identified | Future rock lobster fisheries management | None identified | None | None identified | 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 identified | None identified | None identified | None reported |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Not applicable | Upper Mississipi River basin, elevation 142-194m, | Great Plains to Pacific Coast, northern Rocky Mountains, Pacific Northwest | Not additional description provided | No additional description provided | Coastal to montane, Pacific Northwest US (Oregon) forests. | 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 | No additional description provided | No additional description provided | The UNBR watershed is comprised primarily of two main physiographic areas, the West Cross Timbers and the Grand Prairie Land Resource Areas. In the West Cross Timbers, soils are primarily fine sandy loams with sandy clay subsoils. Soils in the Grand Prairie area, on the other hand, are typically calcareous clays and clay loams (Ward et al. 1992). | Rocky mountain conifer forests | Lakes | Prairie Pothole Region of Iowa | No additional description provided | restored, enhanced and created wetlands | restored, enhanced and created wetlands | Aquatic systems | No additional description provided | Louisiana coastal continental shelf |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | Population growth, road development (density) on public vs private land | Future land use and land cover; climate change | Habitat quality | Two scenarios modelled, forests with and without fire | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | No scenarios presented | No scenarios presented | Fisheries exploitation; fishing vulnerability (of age classes) | Conservation management strategies to reduce phosphorus losses | N/A | Lake productivity | No scenarios presented | Alternative restoration designs | Sites, function or habitat focus | Sites, function or habitat focus | Not applicable | No scenarios presented | Coastal Shelf location |
EM ID
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EM-65 | EM-94 | EM-97 |
EM-98 ![]() |
EM-111 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-349 ![]() |
EM-462 | EM-492 |
EM-541 ![]() |
EM-584 ![]() |
EM-626 |
EM-668 ![]() |
EM-700 |
EM-713 ![]() |
EM-718 ![]() |
EM-729 ![]() |
EM-820 | EM-876 | EM-947 |
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 | Method + Application (multiple runs exist) View EM Runs | 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 (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application | Method + Application |
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 | Application of existing model | New or revised model | Application of existing model | Application of existing 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 | New or revised model | Application of existing model | Application of existing model | WESP - Urban Stormwater Treatment | 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
EM Modeling Approach
EM ID
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EM-65 | EM-94 | EM-97 |
EM-98 ![]() |
EM-111 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-349 ![]() |
EM-462 | EM-492 |
EM-541 ![]() |
EM-584 ![]() |
EM-626 |
EM-668 ![]() |
EM-700 |
EM-713 ![]() |
EM-718 ![]() |
EM-729 ![]() |
EM-820 | EM-876 | EM-947 |
EM Temporal Extent
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2007-2009 | 2000 | 1980-2006 | 2000-2025 | 2005-7; 2035-45 | 1989-1999 | >650 yrs | 2008-2020 | 2006-2007, 2010 | 2006-2013 | 1986-2115 | 1960-2001 | 2004-2008 | 2000-2003 | 1987-2007 | Not reported | 2010-2011 | 2010-2011 | Not applicable | 2010-2011 | 2003-2007 |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable | future time | future time | Not applicable | Not applicable | Not applicable | Not applicable | past time | past time | Not applicable | Not applicable | past time |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Year | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Season |
EM ID
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EM-65 | EM-94 | EM-97 |
EM-98 ![]() |
EM-111 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-349 ![]() |
EM-462 | EM-492 |
EM-541 ![]() |
EM-584 ![]() |
EM-626 |
EM-668 ![]() |
EM-700 |
EM-713 ![]() |
EM-718 ![]() |
EM-729 ![]() |
EM-820 | EM-876 | EM-947 |
Bounding Type
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Physiographic or Ecological | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Not applicable | Geopolitical | Physiographic or ecological |
Spatial Extent Name
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Central French Alps | EU-27 | East Fork Kaskaskia River watershed basin | Western United States | Hood Canal | South Thompson watershed | Western Oregon, north of 43.00 N to Washington border | central Sumatra | Coastal zone surrounding St. Croix | conterminous United States | Table Mountain National Park Marine Protected Area | Upper North Bosque River watershed | National Forest | Lakes in Ohio | CREP (Conservation Reserve Enhancement Program | Dow Midland Operations facility ash pond and Posey Riverside (4-D property) | Wetlands in idaho | Wetlands in idaho | Not applicable | Miami-Dade County | Lousiana continental shelf |
Spatial Extent Area (Magnitude)
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10-100 km^2 | >1,000,000 km^2 | 100-1000 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 | 100,000-1,000,000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | 10,000-100,000 km^2 | 10-100 ha | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | Not applicable | 1000-10,000 km^2. | 100,000-1,000,000 km^2 |
EM ID
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EM-65 | EM-94 | EM-97 |
EM-98 ![]() |
EM-111 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-349 ![]() |
EM-462 | EM-492 |
EM-541 ![]() |
EM-584 ![]() |
EM-626 |
EM-668 ![]() |
EM-700 |
EM-713 ![]() |
EM-718 ![]() |
EM-729 ![]() |
EM-820 | EM-876 | EM-947 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially 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 lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | Not applicable | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | 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) | Not applicable | Not applicable | area, for pixel or radial feature | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature |
Spatial Grain Size
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20 m x 20 m | 10 km x 10 km | 1 km^2 | 504 km^2 | 30 m x 30 m | Not applicable | 0.08 ha | 30 m x 30 m | 10 m x 10 m | irregular | Not applicable | Not applicable | 30m2 | Not applicable | multiple, individual, irregular sites | map unit | Not applicable | Not applicable | Not applicable | Census block | regions of shelf |
EM ID
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EM-65 | EM-94 | EM-97 |
EM-98 ![]() |
EM-111 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-349 ![]() |
EM-462 | EM-492 |
EM-541 ![]() |
EM-584 ![]() |
EM-626 |
EM-668 ![]() |
EM-700 |
EM-713 ![]() |
EM-718 ![]() |
EM-729 ![]() |
EM-820 | EM-876 | EM-947 |
EM Computational Approach
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Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Numeric | Numeric | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Numeric |
EM Determinism
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deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-65 | EM-94 | EM-97 |
EM-98 ![]() |
EM-111 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-349 ![]() |
EM-462 | EM-492 |
EM-541 ![]() |
EM-584 ![]() |
EM-626 |
EM-668 ![]() |
EM-700 |
EM-713 ![]() |
EM-718 ![]() |
EM-729 ![]() |
EM-820 | EM-876 | EM-947 |
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | No | Unclear | Yes | Yes | No | No | Yes | No | No | Yes | No |
Yes ?Comment:Nitrogen and Phosphorus excretion rates were calibrated by lake and fish size class. |
Unclear | Unclear | No | No | Not applicable | Not applicable | Yes |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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Yes | No | No | No | No | No | No | No | No | No | No | No | Yes | No | No | No | No | No | Not applicable | No | No |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None | None | None | None | None | None | None |
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None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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Yes | Yes | Yes | No | Yes | No | Yes | No | Yes | No |
Yes ?Comment:A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data. |
No | No | No | No | Unclear | No | No | Not applicable | No | Yes |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | Yes | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | Not applicable | No | No |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | Unclear |
Yes ?Comment:No results reported. Just a general statement was made about PATCH sensitivity and that demographic parameters are more sensitive that variation in other parameters such as dispersadistance . Reference made to another publication Carroll et al. 2003. Use of population viability analysis and reserve slelection algorithms in regional conservation plans. Ecol. App. 13:1773-1789. |
Yes | Yes | No | No | No | No | No | No | No | No | No | No | No | No | Yes | No | Yes |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Unclear | No | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Yes | Not applicable | Unclear |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-65 | EM-94 | EM-97 |
EM-98 ![]() |
EM-111 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-349 ![]() |
EM-462 | EM-492 |
EM-541 ![]() |
EM-584 ![]() |
EM-626 |
EM-668 ![]() |
EM-700 |
EM-713 ![]() |
EM-718 ![]() |
EM-729 ![]() |
EM-820 | EM-876 | EM-947 |
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None |
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None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-65 | EM-94 | EM-97 |
EM-98 ![]() |
EM-111 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-349 ![]() |
EM-462 | EM-492 |
EM-541 ![]() |
EM-584 ![]() |
EM-626 |
EM-668 ![]() |
EM-700 |
EM-713 ![]() |
EM-718 ![]() |
EM-729 ![]() |
EM-820 | EM-876 | EM-947 |
None | None | None | None |
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None | None |
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None |
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None | None | None | None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-65 | EM-94 | EM-97 |
EM-98 ![]() |
EM-111 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-349 ![]() |
EM-462 | EM-492 |
EM-541 ![]() |
EM-584 ![]() |
EM-626 |
EM-668 ![]() |
EM-700 |
EM-713 ![]() |
EM-718 ![]() |
EM-729 ![]() |
EM-820 | EM-876 | EM-947 |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | 50.53 | 38.69 | 39.88 | 47.8 | 49.29 | 44.66 | 0 | 17.73 | 39.5 | -34.18 | 32.09 | 43.98 | 40.15 | 42.62 | 43.6 | 44.06 | 44.06 | Not applicable | 25.64 | 30.28 |
Centroid Longitude
em.detail.ddLongHelp
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6.4 | 7.6 | -89.1 | -113.81 | -122.7 | -123.8 | -122.56 | 102 | -64.77 | -98.35 | 18.35 | -98.12 | 109.52 | -82.95 | -93.84 | -84.24 | -114.69 | -114.69 | Not applicable | -80.5 | -88.39 |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated |
EM ID
em.detail.idHelp
?
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EM-65 | EM-94 | EM-97 |
EM-98 ![]() |
EM-111 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-349 ![]() |
EM-462 | EM-492 |
EM-541 ![]() |
EM-584 ![]() |
EM-626 |
EM-668 ![]() |
EM-700 |
EM-713 ![]() |
EM-718 ![]() |
EM-729 ![]() |
EM-820 | EM-876 | EM-947 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Rivers and Streams | Near Coastal Marine and Estuarine | Forests | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Agroecosystems | Near Coastal Marine and Estuarine | Agroecosystems | Forests | Lakes and Ponds | Inland Wetlands | Agroecosystems | Grasslands | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Inland Wetlands | Aquatic Environment (sub-classes not fully specified) | Created Greenspace | Near Coastal Marine and Estuarine |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows | Streams and near upstream environments | Row crop agriculture in Kaskaskia river basin | Not reported | glacier-carved saltwater fjord | Rivers and streams | Primarily conifer forest | 104 land use land cover classes | Coral reefs | Terrestrial | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | Rangeland and forage fields for dairy | Montain forest | Reservoirs | Wetlands buffered by grassland within agroecosystems | Ash pond and surrounding environment | created, restored and enhanced wetlands | created, restored and enhanced wetlands | Multiple | urban neighborhood greenspace | Louisiana continental shelf |
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 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 is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale 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 |
Other or unclear (comment) ?Comment:Used in both large and small scale context depending upon survey |
Ecological scale is coarser than that of 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-65 | EM-94 | EM-97 |
EM-98 ![]() |
EM-111 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-349 ![]() |
EM-462 | EM-492 |
EM-541 ![]() |
EM-584 ![]() |
EM-626 |
EM-668 ![]() |
EM-700 |
EM-713 ![]() |
EM-718 ![]() |
EM-729 ![]() |
EM-820 | EM-876 | EM-947 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Not applicable | Not applicable | Species | Not applicable |
Other (Comment) ?Comment:Coho salmon stock |
Species | Community | Guild or Assemblage | Not applicable | Individual or population, within a species | Not applicable | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-65 | EM-94 | EM-97 |
EM-98 ![]() |
EM-111 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-349 ![]() |
EM-462 | EM-492 |
EM-541 ![]() |
EM-584 ![]() |
EM-626 |
EM-668 ![]() |
EM-700 |
EM-713 ![]() |
EM-718 ![]() |
EM-729 ![]() |
EM-820 | EM-876 | EM-947 |
None Available | None Available | None Available |
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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 | 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-65 | EM-94 | EM-97 |
EM-98 ![]() |
EM-111 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-349 ![]() |
EM-462 | EM-492 |
EM-541 ![]() |
EM-584 ![]() |
EM-626 |
EM-668 ![]() |
EM-700 |
EM-713 ![]() |
EM-718 ![]() |
EM-729 ![]() |
EM-820 | EM-876 | EM-947 |
None |
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None |
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None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-65 | EM-94 | EM-97 |
EM-98 ![]() |
EM-111 ![]() |
EM-177 ![]() |
EM-186 ![]() |
EM-349 ![]() |
EM-462 | EM-492 |
EM-541 ![]() |
EM-584 ![]() |
EM-626 |
EM-668 ![]() |
EM-700 |
EM-713 ![]() |
EM-718 ![]() |
EM-729 ![]() |
EM-820 | EM-876 | EM-947 |
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None |
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None | None |
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None |
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None |
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None | None |
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