EcoService Models Library (ESML)
loading
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
em.detail.idHelp
?
|
EM-12 ![]() |
EM-69 | EM-83 | EM-93 | EM-97 |
EM-129 ![]() |
EM-133 |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-320 | EM-450 | EM-456 | EM-590 | EM-629 | EM-653 |
EM-661 ![]() |
EM-672 ![]() |
EM-699 |
EM-713 ![]() |
EM-743 ![]() |
EM-796 ![]() |
EM-838 | EM-1005 |
EM Short Name
em.detail.shortNameHelp
?
|
Evoland v3.5 (bounded growth), Eugene, OR, USA | Soil carbon content, Central French Alps | Soil carbon and plant traits, Central French Alps | Stream nitrogen removal, Mississippi R. basin, USA | AnnAGNPS, Kaskaskia River watershed, IL, USA | 3-PG, South Australia | Flood regulation supply-demand, Etropole, Bulgaria | Salmon habitat values, west coast of Canada | FORCLIM v2.9, Western OR, USA | Coral and land development, St.Croix, VI, USA | Coastal protection, Europe | Decrease in wave runup, St. Croix, USVI | Reef dive site favorability, St. Croix, USVI | Fish species richness, Puerto Rico, USA | SolVES, Pike & San Isabel NF, WY | Natural amenities and population migration, USA | Alwife phosphorus flux in lakes, Connecticut, USA | Alewife nutrients in stream food web, CT, USA | Fish species richness, St. John, USVI, USA | ESII Tool, Michigan, USA | WESP: Irrigation water, ID, USA | Wildflower mix supporting bees, MI, USA | Eastern meadowlark abundance, Piedmont region, USA | ComunityViz - land-sea planning submodel |
EM Full Name
em.detail.fullNameHelp
?
|
Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA | Soil carbon content, Central French Alps | Soil carbon potential estimated from plant functional traits, Central French Alps | Stream nitrogen removal, Upper Mississippi, Ohio and Missouri River sub-basins, USA | AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | 3-PG (Physiological Principles Predicting Growth), South Australia | Flood regulation supply vs. demand, Municipality of Etropole, Bulgaria | 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 | Coastal protection, Europe | Decrease in wave runup (by reef), St. Croix, USVI | Dive site favorability (reef), St. Croix, USVI | Fish species richness, Puerto Rico, USA | SolVES, Social Values for Ecosystem Services, Pike and San Isabel National Forest, CO | Natural amenities and rural population migration, USA | Net phosphorus flux in freshwater lakes from alewives, Connecticut, USA | Alewife derived nutrients in stream food web, Connecticut, USA | Fish species richness, St. John, USVI, USA | ESII (Ecosystem Services Identification and Inventory) Tool, Michigan, USA | WESP: Irrigation return water treatment, Idaho, USA | Wildflower planting mix supporting bees in agricultural landscapes, MI, USA | Eastern meadowlark abundance, Piedmont ecoregion, USA | A technical guide to the integrated land-sea planning toolkit |
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
Envision | EU Biodiversity Action 5 | EU Biodiversity Action 5 | US EPA | US EPA | None | EU Biodiversity Action 5 | None | US EPA | US EPA | EU Biodiversity Action 5 | US EPA | US EPA | None | None | USDA Forest Service | None | None | None | None | None | None | None | None |
EM Source Document ID
|
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
260 | 260 | 52 | 137 | 243 | 248 | 286 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
96 | 296 | 335 | 335 | 355 | 369 | 375 | 383 | 384 | 355 |
392 ?Comment:Document 391 is an additional source for this EM. |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
400 | 405 | 473 |
Document Author
em.detail.documentAuthorHelp
?
|
Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Hill, B. and Bolgrien, D. | Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Crossman, N. D., Bryan, B. A., and Summers, D. M. | Nedkov, S., Burkhard, B. | 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. | Liquete, C., Zulian, G., Delgado, I., Stips, A., and Maes, J. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | Cordell H. K., V. Heboyan, F. Santos, J. C. Bergstrom | West, D. C., A. W. Walters, S. Gephard, and D. M. Post | Walters, A. W., R. T. Barnes, and D. M. Post | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Guertin, F., K. Halsey, T. Polzin, M. Rogers, and B. Witt | Murphy, C. and T. Weekley | Williams, N.M., Ward, K.L., Pope, N., Isaacs, R., Wilson, J., May, E.A., Ellis, J., Daniels, J., Pence, A., Ullmann, K., and J. Peters | Riffel, S., Scognamillo, D., and L. W. Burger | Crist, P., Madden, K., Varley, I., Eslinger, D., Walker, D., Anderson, A., Morehead, S. and Dunton, K., |
Document Year
em.detail.documentYearHelp
?
|
2008 | 2011 | 2011 | 2011 | 2011 | 2011 | 2012 | 2003 | 2007 | 2011 | 2013 | 2014 | 2014 | 2007 | 2014 | 2011 | 2010 | 2009 | 2007 | 2019 | 2012 | 2015 | 2008 | 2009 |
Document Title
em.detail.sourceIdHelp
?
|
Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Nitrogen removal by streams and rivers of the Upper Mississippi River basin | AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | Carbon payments and low-cost conservation | Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria | 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 | Assessment of coastal protection as an ecosystem service in Europe | 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 | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | Natural amenities and rural population migration | Nutrient loading by anadromous alewife (Alosa pseudoharengus): contemporary patterns and predictions for restoration efforts | Anadromous alewives (Alosa pseudoharengus) contribute marine-derived nutrients to coastal stream food webs | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | 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. | Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Integrated Land-Sea Planning: A Technical Guide to the Integrated Land-Sea Planning Toolkit. |
Document Status
em.detail.statusCategoryHelp
?
|
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 | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
em.detail.commentsOnStatusHelp
?
|
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 journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published report |
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-69 | EM-83 | EM-93 | EM-97 |
EM-129 ![]() |
EM-133 |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-320 | EM-450 | EM-456 | EM-590 | EM-629 | EM-653 |
EM-661 ![]() |
EM-672 ![]() |
EM-699 |
EM-713 ![]() |
EM-743 ![]() |
EM-796 ![]() |
EM-838 | EM-1005 |
http://evoland.bioe.orst.edu/ ?Comment:Software is likely available. |
Not applicable | 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/ | http://www.csiro.au/products/3PGProductivity#a1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.esiitool.com/ | Not applicable | Not applicable | Not applicable | https://repositories.lib.utexas.edu/bitstreams/3dee92a8-9373-4bcc-be25-eda74e81fabf/download | |
Contact Name
em.detail.contactNameHelp
?
|
Michael R. Guzy | Sandra Lavorel | Sandra Lavorel | Brian Hill | Yongping Yuan | Anders Siggins | Stoyan Nedkov | Duncan Knowler | Richard T. Busing | Leah Oliver | Camino Liquete | Susan H. Yee | Susan H. Yee | Simon Pittman | Benson Sherrouse | Ken Cordell | Derek C. West | Annika W. Walters | Simon Pittman | Not reported | Chris Murphy | Neal Williams | Sam Riffell |
Patrick Crist ?Comment:No contact information provided |
Contact Address
|
Oregon State University, Dept. of Biological and Ecological Engineering | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Mid-Continent Ecology Division NHEERL, ORD. USEPA 6201 Congdon Blvd. Duluth, MN 55804, USA | U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | Not reported | National Institute of Geophysics, Geodesy and Geography, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, bl.3, 1113 Sofia, Bulgaria | 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 | European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, I-21027 Ispra, VA, Italy | 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 | 1305 East-West Highway, Silver Spring, MD 20910, USA | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | U.S. Department of Agriculture, Forest Service, Southern Research Station, Athens, GA 30602 | Dept. of Ecology and Evolutionary Biology, Yale University, 165 Prospect Street, New Haven, CT 06511, USA | Dept. of Ecology and Evolutionary Biology, Yale University, New Haven CT 06511 | 1305 East-West Highway, Silver Spring, MD 20910, USA | Not reported | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616 | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | None provided |
Contact Email
|
Not reported | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | hill.brian@epa.gov | yuan.yongping@epa.gov | Anders.Siggins@csiro.au | snedkov@abv.bg | djk@sfu.ca | rtbusing@aol.com | leah.oliver@epa.gov | camino.liquete@gmail.com | yee.susan@epa.gov | yee.susan@epa.gov | simon.pittman@noaa.gov | bcsherrouse@usgs.gov | Not reported | derek.west@yale.edu | annika.walters@yale.edu | simon.pittman@noaa.gov | Not reported | chris.murphy@idfg.idaho.gov | nmwilliams@ucdavis.edu | sriffell@cfr.msstate.edu | patrick@planitfwd.com |
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-69 | EM-83 | EM-93 | EM-97 |
EM-129 ![]() |
EM-133 |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-320 | EM-450 | EM-456 | EM-590 | EM-629 | EM-653 |
EM-661 ![]() |
EM-672 ![]() |
EM-699 |
EM-713 ![]() |
EM-743 ![]() |
EM-796 ![]() |
EM-838 | EM-1005 |
Summary Description
em.detail.summaryDescriptionHelp
?
|
**Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. 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, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in soil carbon 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…Soil carbon for each pixel was calculated and mapped using model estimates...This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on soil carbon. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The soil carbon ecosystem service map was a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to the soil carbon ecosystem service are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions." | ABSTRACT: "We used stream chemistry and hydrogeomorphology data from 549 stream and 447 river sites to estimate NO3–N removal in the Upper Mississippi, Missouri, and Ohio Rivers. We used two N removal models to predict NO3–N input and removal. NO3–N input ranged from 0.01 to 338 kg/km*d in the Upper Mississippi River to 0.01–54 kg/ km*d in the Missouri River. Cumulative river network NO3–N input was 98700–101676 Mg/year in the Ohio River, 85,961–89,288 Mg/year in the Upper Mississippi River, and 59,463–61,541 Mg/year in the Missouri River. NO3–N output was highest in the Upper Mississippi River (0.01–329 kg/km*d ), followed by the Ohio and Missouri Rivers (0.01–236 kg/km*d ) sub-basins. Cumulative river network NO3–N output was 97,499 Mg/year for the Ohio River, 84,361 Mg/year for the Upper Mississippi River, and 59,200 Mg/year for the Missouri River. Proportional NO3–N removal (PNR) based on the two models ranged from 0.01 to 0.28. NO3–N removal was inversely correlated with stream order, and ranged from 0.01 to 8.57 kg/km*d in the Upper Mississippi River to 0.001–1.43 kg/km*d in the Missouri River. Cumulative river network NO3–N removal predicted by the two models was: Upper Mississippi River 4152 and 4152 Mg/year, Ohio River 3743 and 378 Mg/year, and Missouri River 2,277 and 197 Mg/year. PNR removal was negatively correlated with both stream order (r = −0.80–0.87) and the percent of the catchment in agriculture (r = −0.38–0.76)." | 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" | 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: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. Maps of demands for flood regulating ecosystem services in the study region were compiled based on a digital elevation model, land use information and accessibility data. Finally, the flood regulating ecosystem service supply and demand data were merged in order to produce a map showing regional supply-demand balances.The flood regulation ecosystem service demand map shows that areas of low or no relevant demands far exceed the areas of high and very high demands, which comprise only 0.6% of the municipality’s area. According to the flood regulation supply-demand balance map, areas of high relevant demands are located in places of low relevant supply capacities" AUTHOR'S DESCRIPTION: "A similar relative scale ranging from 0 to 5 was applied to assess the demands for flood regulation. A 0-value indicates that there is no relevant demand for flood regulation and 5 would indicate the highest demand for flood regulation within the case study region. Values of 2, 3 and 4 represent respective intermediate demands. The calculations were based on the assumption that the most vulnerable areas would have the highest demand for flood regulation. The vulnerability, defined as “the characteristics and circumstances of a community, system or asset that make it susceptible to the damaging effects of a hazard” (UN/ISDR, 2009), has different dimensions (e.g. social, economic, environmental, institutional). The most vulnerable places in the case study area were defined by using different sources of demographic, statistical, topographic and economic data (Nikolova et al., 2009). These areas will have the highest (5-value) demand for flood regulation…For analyzing source and sink dynamics and to identify flows of ecosystem services, the information in the matrixes and in the maps of ecosystem service supply and demand can be merged (Burkhard et al., 2012). As the landscapes’ flood regulation supply and demand are not analyzed and modeled in the same units it is not possible to calculate the balance between them quantitatively. Using the relative scale (0–5) it becomes possible to compare them and to calculate supply-demand budgets. Although this does not providea clear indication of whether there is excess supply or demand, the resulting map shows where areas of qualitatively high demand correspond with low supply and vice versa." | 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) | ABSTRACT: "Mapping and assessment of ecosystem services is essential to provide scientific support to global and EU biodiversity policy. Coastal protection has been mostly analysed in the frame of coastal vulnerability studies or in local, habitat-specific assessments. This paper provides a conceptual and methodological approach to assess coastal protection as an ecosystem service at different spatial–temporal scales, and applies it to the entire EU coastal zone. The assessment of coastal protection incorporates 14 biophysical and socio-economic variables from both terrestrial and marine datasets. Those variables define three indicators: coastal protection capacity, coastal exposure and human demand for protection. A questionnaire filled by coastal researchers helped assign ranks to categorical parameters and weights to the individual variables. The three indicators are then framed into the ecosystem services cascade model to estimate how coastal ecosystems provide protection, in particular describing the service function, flow and benefit. The results are comparative and aim to support integrated land and marine spatial planning. The main drivers of change for the provision of coastal protection come from the widespread anthropogenic pressures in the European coastal zone, for which a short quantitative analysis is provided." | 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...Wave run-up, R, can be estimated as R = H(tan α/(√H/Ho) where H is the wave height nearshore, Ho is the deep water wave height, and α is the angle of the beach slope. R may be corrected by a multiplier depending on the porosity of the shoreline surface...The contribution of each grid cell to reduction in wave run-up would depend on its contribution to wave height attenuation (Eq. (S3))." | 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)…In lieu of surveys of diver opinion, recreational opportunities can also be estimated by actual field data of coral condition at preferred dive sites. A few studies have directly examined links between coral condition and production of recreational opportunities through field monitoring in an attempt to validate perceptions of recreational quality (Pendleton, 1994; Williams and Polunin, 2002; Leeworthy et al., 2004; Leujakand Ormond, 2007; Uyarraetal., 2009). Uyarraetal. (2009) used surveys to determine reef attributes related to diver perceptions of most and least favorite dive sites. Field data was used to narrow down the suite of potential preferred attributes to those that reflected actual site condition. We combined these attributes to form an index of dive site favorability: Dive site favorability = ΣipiRi where pi is the proportion of respondents indicating each attribute i that affected dive enjoyment positively. Ri is the mean relative magnitude of measured variables used to quantify each descriptive attribute i, including ‘fish abundance’ (pi=0.803), quantified by number of fish schools and fish species richness, and | ABSTRACT: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems." | [ABSTRACT: " "Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders.With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems. | ABSTRACT: "Research suggests that significant relationships exist between rural population change and natural amenities. Thus, understanding and predicting domestic migration trends as a function of changes in natural amenities is important for effective regional growth and development policies and strategies. In this study, we first estimated an econometric model which showed the effects of natural amenities, such as climate and landscape variables, on rural population migration patterns in the United States between 1990 and 2007. The estimated model was then used to predict the effects of changes in these variables on rural county net migration and population growth to 2060 under alternative future climate and land use projections. Results suggest that people prefer rural areas with mild winters and cooler summers; thus we can expect a direct impact of climate change on population migration when areas associated with these conditions change. Results also suggest preference for varied landscapes that feature a mix of forest land and open space (e g , pasture and range land). During the projection period from 2010 to 2060 in the United States, changes in natural amenities were predicted to have positive effects on rural population migration trends in most parts of the Intermountain and Pacific Northwest regions, and some parts of the Southeastern, South Central, and Northeastern U S regions (e g , Southern Appalachian Mountains, Ozark Mountains, northern New England). Changes in natural amenities were predicted to have negative effects on rural population migration trends during the projection period in Midwestern regions (e g , Great Plains and North Central regions)." AUTHOR'S DESCRIPTION: "This model was estimated for 2,014 rural counties in the continental United States using various national data bases and sources. The estimated model was then used to predict the effects of changes in these variables on rural county net migration and population growth to 2060 under alternative future climate and land use projections." | ABSTRACT: "Anadromous alewives (Alosa pseudoharengus) have the potential to alter the nutrient budgets of coastal lakes as they migrate into freshwater as adults and to sea as juveniles. Alewife runs are generally a source of nutrients to the freshwater lakes in which they spawn, but juveniles may export more nutrients than adults import in newly restored populations. A healthy run of alewives in Connecticut imports substantial quantities of phosphorus; mortality of alewives contributes 0.68 g P_fish–1, while surviving fish add 0.18 g P, 67% of which is excretion. Currently, alewives contribute 23% of the annual phosphorus load to Bride Lake, but this input was much greater historically, with larger runs of bigger fish contributing 2.5 times more phosphorus in the 1960s..." AUTHOR'S DESCRIPTION: "Here, we evaluate the patterns of net nutrient loading by alewives over a range of population sizes. We concentrate on phosphorus, as it is generally the nutrient that limits production in the lake ecosystems in which alewives spawn (Schindler 1978). First, we estimate net alewife nutrient loading and parameterize an alewife nutrient loading model using data from an existing run of anadromous alewives in Bride Lake. We then compare the current alewife nutrient load to that in the 1960s when alewives were more numerous and larger. Next, since little is known about the actual patterns of nutrient loading during restoration, we predict the net nutrient loading for a newly restored population across a range of adult escapement… Anadromous fish move nutrients both into and out of freshwater ecosystems, although inputs are typically more obvious and much better studied (Moore and Schindler 2004). Net loading into freshwater ecosystems is fully described as inputs due to adult mortality, gametes, and direct excretion of nutrients minus the removal of nutrients from freshwater ecosystems by juvenile fish when they emigrate… Our research was conducted at Bride Lake and Linsley Pond in Connecticut. Bride Lake contains an anadromous alewife population that we used to both evaluate contemporary and historic net nutrient loading by an alewife population and parameterize our general alewife nutrient loading model." | ABSTRACT: "Diadromous fish are an important link between marine and freshwater food webs. Pacific salmon (Oncorhynchus spp.) strongly impact nutrient dynamics in inland waters and anadromous alewife (Alosa pseudoharengus) may play a similar ecological role along the Atlantic coast. The annual spawning migration of anadromous alewife contributes, on average, 1050 g of nitrogen and 120 g of phosphorus to Bride Brook, Connecticut, USA, through excretion and mortality each year. Natural abundance stable isotope analyses indicate that this influx of marine-derived nitrogen is rapidly incorporated into the stream food web. An enriched d15N signal, indicative of a marine origin, is present at all stream trophic levels with the greatest level of enrichment coincident with the timing of the anadromous alewife spawning migration. There was no significant effect of this nutrient influx on water chemistry, leaf decomposition, or periphyton accrual. Dam removal and fish ladder construction will allow anadromous alewife to regain access to historical freshwater spawning habitats, potentially impacting food web dynamics and nutrient cycling in coastal freshwater systems." AUTHOR'S DESCRIPTION: "Here, we examine the effect of alewife-contributed marine- derived nutrients to coastal stream ecosystems in southern New England. We take a comparative approach examining streams with and without anadromous alewife runs. We use natural abundance stable isotope analyses to assess the incorporation of marine-derived nitrogen and carbon into stream food webs." | ABSTRACT: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems." | 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: " Global trends in pollinator-dependent crops have raised awareness of the need to support managed and wild bee populations to ensure sustainable crop production. Provision of sufficient forage resources is a key element for promoting bee populations within human impacted landscapes, particularly those in agricultural lands where demand for pollination service is high and land use and management practices have reduced available flowering resources. Recent government incentives in North America and Europe support the planting of wildflowers to benefit pollinators; surprisingly, in North America there has been almost no rigorous testing of the performance of wildflower mixes, or their ability to support wild bee abundance and diversity. We tested different wildflower mixes in a spatially replicated, multiyear study in three regions of North America where production of pollinatordependent crops is high: Florida, Michigan, and California. In each region, we quantified flowering among wildflower mixes composed of annual and perennial species, and with high and low relative diversity. We measured the abundance and species richness of wild bees, honey bees, and syrphid flies at each mix over two seasons. In each region, some but not all wildflower mixes provided significantly greater floral display area than unmanaged weedy control plots. Mixes also attracted greater abundance and richness of wild bees, although the identity of best mixes varied among regions. By partitioning floral display size from mix identity we show the importance of display size for attracting abundant and diverse wild bees. Season-long monitoring also revealed that designing mixes to provide continuous bloom throughout the growing season is critical to supporting the greatest pollinator species richness. Contrary to expectation, perennials bloomed in their first season, and complementarity in attraction of pollinators among annuals and perennials suggests that inclusion of functionally diverse species may provide the greatest benefit. Wildflower mixes may be particularly important for providing resources for some taxa, such as bumble bees, which are known to be in decline in several regions of North America. No mix consistently attained the full diversity that was planted. Further study is needed on how to achieve the desired floral display and diversity from seed mixes. " Additional information in supplemental Appendices online: http://dx.doi.org/10.1890/14-1748.1.sm | 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. " | CommunityViz® is an advanced yet easy-to-use GIS software extension that is designed to help people visualize, analyze, and communicate about important planning decisions. Widely adopted by land-use planners, it supports informed, collaborative decision-making by illustrating and analyzing alternative planning scenarios. It features flexible and interactive analysis tools, a rich set of presentation tools, and several options for 3D visualization of future places. In the land-sea toolkit, CommunityViz (sometimes referred to as “Cviz”) serves as the platform for creating land use scenarios. It models how urban growth could occur over time as the result of present-day decisions regarding land use and regulation. The resulting future growth conditions are passed to NatureServe Vista (Vista) and NSPECT for impact assessment, and those results can be returned to CommunityViz for display and for guidance in development of revisions to planning scenarios. Throughout the integration process, CommunityViz provides the ability to assess a variety of socio-economic indicators attached to the land-use scenarios. |
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None identified | None identified | Not applicable | Not reported | None identified | None identified | None identified | None Identified | Not applicable | Supports global and EU biodiversity policy | None identified | None identified | None provided | None | None identified | Restoration and management of diadromous fish runs in coastal New England | Nutrients and water quality related to anadromous alewife restoration efforts | None provided | 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 identrified | None reported | None provided |
Biophysical Context
|
No additional description provided | Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Agricultural landuse , 1st-10th order streams | Upper Mississipi River basin, elevation 142-194m, | 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. | Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak. | No additional description provided | Coastal to montane, Pacific Northwest US (Oregon) forests. | nearshore; <1.5 km offshore; <12 m depth | No additional description provided | No additional description provided | No additional description provided | Hard and soft benthic habitat types approximately to the 33m isobath | Rocky mountain conifer forests | No additional description provided | Bride Lake is 28.7 ha and linked to Long Island Sound by the 3.3 km Bride Brook. | No additional description provided | Hard and soft benthic habitat types approximately to the 33m isobath | No additional description provided | restored, enhanced and created wetlands | field plots near agricultural fruit and vegetable research farms | Conservation Reserve Program lands left to go fallow | Not applicable |
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
Five scenarios that include urban growth boundaries and various combinations of unconstrainted development, fish conservation, agriculture and forest reserves. ?Comment:Additional alternatives included adding agricultural and forest reserves, and adding or removing urban growth boundaries to the three main scenarios. |
No scenarios presented | No scenarios presented | Not applicable | Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | Four carbon-planting systems including hardwood and mallee monoculture plantings, and mixed species ecological carbon plantings | No scenarios presented | Habitat quality | Two scenarios modelled, forests with and without fire | Not applicable | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | N/A | Climate projections based on the CGCM 3 1 general circulation model of moderate warming (IPCC). The A1B scenario assumes a growing world population that peaks in the mid-century and balanced technological growth. | current and historical run size | No scenarios presented | No scenarios presented | Alternative restoration designs | Sites, function or habitat focus | Varied wildflower planting mixes of annuals and perennials | N/A | No scenarios presented |
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-69 | EM-83 | EM-93 | EM-97 |
EM-129 ![]() |
EM-133 |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-320 | EM-450 | EM-456 | EM-590 | EM-629 | EM-653 |
EM-661 ![]() |
EM-672 ![]() |
EM-699 |
EM-713 ![]() |
EM-743 ![]() |
EM-796 ![]() |
EM-838 | EM-1005 |
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application (multiple runs exist) View EM Runs | Method + Application | 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 | 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 + Application | Method + Application | Method + Application | 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 (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only |
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-69 | EM-83 | EM-93 | EM-97 |
EM-129 ![]() |
EM-133 |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-320 | EM-450 | EM-456 | EM-590 | EM-629 | EM-653 |
EM-661 ![]() |
EM-672 ![]() |
EM-699 |
EM-713 ![]() |
EM-743 ![]() |
EM-796 ![]() |
EM-838 | EM-1005 |
EM Temporal Extent
em.detail.tempExtentHelp
?
|
1990-2050 | 2007-2009 | Not reported | 2000-2008 | 1980-2006 | 2009-2050 | Not reported | 1989-1999 | >650 yrs | 2006-2007 | 1992-2010 | 2006-2007, 2010 | 2006-2007, 2010 | 2000-2005 | 2004-2008 | 1982-2060 | 1960"s and early 2000's | 2005-2006 (March-July) | 2000-2005 | Not reported | 2010-2012 | 2010-2011 | 2008 | Not applicable |
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
future time | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | past time | Not applicable | Not applicable | past time | past time | Not applicable | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable |
other or unclear (comment) ?Comment:Sampling conducted at discrete time periods during Alewife migration. Three sampling periods are presented in this entry. |
Not applicable | Not applicable | Not applicable | discrete | Not applicable | other or unclear (comment) |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
2 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Year | Not applicable | Not applicable | Not applicable | Not applicable | Month | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-69 | EM-83 | EM-93 | EM-97 |
EM-129 ![]() |
EM-133 |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-320 | EM-450 | EM-456 | EM-590 | EM-629 | EM-653 |
EM-661 ![]() |
EM-672 ![]() |
EM-699 |
EM-713 ![]() |
EM-743 ![]() |
EM-796 ![]() |
EM-838 | EM-1005 |
Bounding Type
em.detail.boundingTypeHelp
?
|
Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or Ecological | Geopolitical | Physiographic or ecological | Physiographic or ecological | Physiographic or Ecological | Geopolitical | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Geopolitical | Physiographic or ecological | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) |
Point or points ?Comment:This is a guess based on information in the document. 3 field sites were separated by up to 9km |
Physiographic or ecological | Not applicable |
Spatial Extent Name
em.detail.extentNameHelp
?
|
Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Central French Alps | Central French Alps | Upper Mississippi, Ohio and Missouri River sub-basins | East Fork Kaskaskia River watershed basin | Agricultural districts of the state of South Australia | Municipality of Etropole | South Thompson watershed | Western Oregon, north of 43.00 N to Washington border | St. Croix, U.S. Virgin Islands | Shoreline of the European Union-27 | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | SW Puerto Rico, | National Park | continental United States | Bride Lake and Linsley Pond | New London County, Connecticut, USA | SW Puerto Rico, | Dow Midland Operations facility ash pond and Posey Riverside (4-D property) | Wetlands in idaho | Agricultural plots | Piedmont Ecoregion | Not applicable |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
10-100 km^2 | 10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 10,000-100,000 km^2 | 10-100 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | >1,000,000 km^2 | 10-100 ha | 1000-10,000 km^2. | 100-1000 km^2 | 10-100 ha | 100,000-1,000,000 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-69 | EM-83 | EM-93 | EM-97 |
EM-129 ![]() |
EM-133 |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-320 | EM-450 | EM-456 | EM-590 | EM-629 | EM-653 |
EM-661 ![]() |
EM-672 ![]() |
EM-699 |
EM-713 ![]() |
EM-743 ![]() |
EM-796 ![]() |
EM-838 | EM-1005 |
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially distributed (in at least some cases) ?Comment:Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Distributed by land cover and soil type polygons |
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 lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | other or unclear (comment) |
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | map scale, for cartographic feature | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | Not applicable | Not applicable |
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
varies | 20 m x 20 m | 20 m x 20 m | 1 km | 1 km^2 | 1 ha x 1 ha | Distributed by irregular land cover and soil type polygons | Not applicable | 0.08 ha | Not applicable | Irregular | 10 m x 10 m | 10 m x 10 m | not reported | 30m2 | varies | Not applicable | variable stream lengths | not reported | map unit | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-69 | EM-83 | EM-93 | EM-97 |
EM-129 ![]() |
EM-133 |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-320 | EM-450 | EM-456 | EM-590 | EM-629 | EM-653 |
EM-661 ![]() |
EM-672 ![]() |
EM-699 |
EM-713 ![]() |
EM-743 ![]() |
EM-796 ![]() |
EM-838 | EM-1005 |
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Not applicable | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic |
EM Determinism
em.detail.deterStochHelp
?
|
stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | Not applicable | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
Comment:Agent based modeling results in response indices. |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-69 | EM-83 | EM-93 | EM-97 |
EM-129 ![]() |
EM-133 |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-320 | EM-450 | EM-456 | EM-590 | EM-629 | EM-653 |
EM-661 ![]() |
EM-672 ![]() |
EM-699 |
EM-713 ![]() |
EM-743 ![]() |
EM-796 ![]() |
EM-838 | EM-1005 |
Model Calibration Reported?
em.detail.calibrationHelp
?
|
Unclear | No | No | No | No | Yes | No | Yes | No | Yes | No | Yes | Yes | No | No | Yes | Yes | Not applicable | No | Unclear | No | No | Yes | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
No | Yes | No | No | No | No | No | No | No | Yes | No | No | No | Yes | Yes | No | No | Not applicable | Yes | No | No | No | No | Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
None |
|
None | None | None | None | None | None | None |
|
None | None | None |
|
|
None | None | None |
|
None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
?
|
No | Yes | No | No | Yes | No | No | No | Yes | No | No | Yes | Yes | Yes | No | No | No | Not applicable | Yes | Unclear | No | No | No | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
No | No | No | Yes | Yes | No | No | No | No | Yes | No | No | No | No | No | No | No | Not applicable | No | No | No | No | No | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
No ?Comment:Sensitivity analysis performed for agent values only. |
No | No | Unclear | Unclear | No | No | Yes | No | No | No | No | No | Yes | No | No | Yes | Not applicable | Yes | No | No | No | Yes | Not applicable |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Unclear | Not applicable | No | Not applicable | Not applicable | Not applicable | Unclear | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-12 ![]() |
EM-69 | EM-83 | EM-93 | EM-97 |
EM-129 ![]() |
EM-133 |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-320 | EM-450 | EM-456 | EM-590 | EM-629 | EM-653 |
EM-661 ![]() |
EM-672 ![]() |
EM-699 |
EM-713 ![]() |
EM-743 ![]() |
EM-796 ![]() |
EM-838 | EM-1005 |
|
|
|
|
|
|
|
|
|
None |
|
None | None | None |
|
|
|
|
None |
|
|
|
|
None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-12 ![]() |
EM-69 | EM-83 | EM-93 | EM-97 |
EM-129 ![]() |
EM-133 |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-320 | EM-450 | EM-456 | EM-590 | EM-629 | EM-653 |
EM-661 ![]() |
EM-672 ![]() |
EM-699 |
EM-713 ![]() |
EM-743 ![]() |
EM-796 ![]() |
EM-838 | EM-1005 |
None | None | None | None | None | None | None |
|
None |
|
|
|
|
|
None | None | None |
|
|
None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-69 | EM-83 | EM-93 | EM-97 |
EM-129 ![]() |
EM-133 |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-320 | EM-450 | EM-456 | EM-590 | EM-629 | EM-653 |
EM-661 ![]() |
EM-672 ![]() |
EM-699 |
EM-713 ![]() |
EM-743 ![]() |
EM-796 ![]() |
EM-838 | EM-1005 |
Centroid Latitude
em.detail.ddLatHelp
?
|
44.11 | 45.05 | 45.05 | 36.98 | 38.69 | -34.9 | 42.8 | 49.29 | 44.66 | 17.75 | 48.2 | 17.73 | 17.73 | 17.9 | 38.7 | 39.8 | 41.33 | 41.78 | 17.79 | 43.6 | 44.06 | 43.87 | 36.23 | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
?
|
-123.09 | 6.4 | 6.4 | -89.13 | -89.1 | 138.7 | 24 | -123.8 | -122.56 | -64.75 | 16.35 | -64.77 | -64.77 | 67.11 | 105.89 | -98.55 | -72.24 | -72.17 | -64.62 | -84.24 | -114.69 | -85.64 | -81.9 | Not applicable |
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | NAD83 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Estimated | Provided | Provided | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-69 | EM-83 | EM-93 | EM-97 |
EM-129 ![]() |
EM-133 |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-320 | EM-450 | EM-456 | EM-590 | EM-629 | EM-653 |
EM-661 ![]() |
EM-672 ![]() |
EM-699 |
EM-713 ![]() |
EM-743 ![]() |
EM-796 ![]() |
EM-838 | EM-1005 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Rivers and Streams | Agroecosystems | Forests | Agroecosystems | Rivers and Streams | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Rivers and Streams | Near Coastal Marine and Estuarine | Forests | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Forests | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Rivers and Streams | Lakes and Ponds | Rivers and Streams | Near Coastal Marine and Estuarine | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Agroecosystems | Grasslands | Not applicable |
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Agricultural-urban interface at river junction | Subalpine terraces, grasslands, and meadows. | Subalpine terraces, grasslands, and meadows. | Not applicable | Row crop agriculture in Kaskaskia river basin | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Mountainous flood-prone region | Rivers and streams | Primarily conifer forest | stony coral reef | Coastal zones | Coral reefs | Coral reefs | shallow coral reefs | Montain forest | Terrestrial environments including water bodies and coastlines | Coastal lakes and ponds and associated streams | Coastal streams | shallow coral reefs | Ash pond and surrounding environment | created, restored and enhanced wetlands | Agricultural landscape | grasslands | None |
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-69 | EM-83 | EM-93 | EM-97 |
EM-129 ![]() |
EM-133 |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-320 | EM-450 | EM-456 | EM-590 | EM-629 | EM-653 |
EM-661 ![]() |
EM-672 ![]() |
EM-699 |
EM-713 ![]() |
EM-743 ![]() |
EM-796 ![]() |
EM-838 | EM-1005 |
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Not applicable | Community | Community | Not applicable | Not applicable | Species | Not applicable |
Other (Comment) ?Comment:Coho salmon stock |
Species | Guild or Assemblage | Not applicable | Not applicable | Guild or Assemblage | Guild or Assemblage | Not applicable | Not applicable | Individual or population, within a species | Individual or population, within a species | Guild or Assemblage | Not applicable | Not applicable | Species | Species | Community |
Taxonomic level and name of organisms or groups identified
EM-12 ![]() |
EM-69 | EM-83 | EM-93 | EM-97 |
EM-129 ![]() |
EM-133 |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-320 | EM-450 | EM-456 | EM-590 | EM-629 | EM-653 |
EM-661 ![]() |
EM-672 ![]() |
EM-699 |
EM-713 ![]() |
EM-743 ![]() |
EM-796 ![]() |
EM-838 | EM-1005 |
|
None Available | None Available | None Available | None Available |
|
None Available |
|
|
|
None Available | None Available | 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-12 ![]() |
EM-69 | EM-83 | EM-93 | EM-97 |
EM-129 ![]() |
EM-133 |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-320 | EM-450 | EM-456 | EM-590 | EM-629 | EM-653 |
EM-661 ![]() |
EM-672 ![]() |
EM-699 |
EM-713 ![]() |
EM-743 ![]() |
EM-796 ![]() |
EM-838 | EM-1005 |
|
None |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
None |
|
|
|
|
|
|
<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-12 ![]() |
EM-69 | EM-83 | EM-93 | EM-97 |
EM-129 ![]() |
EM-133 |
EM-177 ![]() |
EM-186 ![]() |
EM-194 | EM-320 | EM-450 | EM-456 | EM-590 | EM-629 | EM-653 |
EM-661 ![]() |
EM-672 ![]() |
EM-699 |
EM-713 ![]() |
EM-743 ![]() |
EM-796 ![]() |
EM-838 | EM-1005 |
|
None | None |
|
|
|
|
|
None |
|
|
|
|
|
|
None | None | None |
|
|
None |
|
|
None |