EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
EM Identity and Description
EM ID
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EM-12 ![]() |
EM-84 | EM-97 | EM-99 | EM-113 |
EM-369 ![]() |
EM-380 ![]() |
EM-421 | EM-437 | EM-455 | EM-461 |
EM-485 ![]() |
EM-549 | EM-626 | EM-701 | EM-840 | EM-842 | EM-887 | EM-889 | EM-894 |
EM-992 ![]() |
EM-1019 |
EM Short Name
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Evoland v3.5 (bounded growth), Eugene, OR, USA | ACRU, South Africa | AnnAGNPS, Kaskaskia River watershed, IL, USA | Landscape importance for crops, Europe | Wetland conservation for birds, Midwestern USA | Envision, Puget Sound, WA, USA | VELMA plant-soil, Oregon, USA | Water storage in Lago Lucchetti, Puerto Rico, USA | InVESTv3.0 Water yield, Guánica Bay, Puerto Rico | Value of a reef dive site, St. Croix, USVI | Presence of Euchema sp., St. Croix, USVI | Yasso07 v1.0.1, Switzerland, site level | Nutrient Tracking Tool (NTT) | SolVES, Shoshone NF, WY | Blue-winged Teal recruits, CREP wetlands, IA, USA | Eastern bluebird abundance, Piedmont region, USA | Horned lark abundance, Piedmont region, USA | VELMA v. 2.0 disturbance | HWB poor health, Great Lakes waterfront, USA | HWB indicator-Adult success, Great Lakes, USA | DAISY model, Taastrup, Copenhagen | SMOKE emissions model, Asia |
EM Full Name
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Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA | ACRU (Agricultural Catchments Research Unit), South Africa | AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | Landscape importance for crop-based production, Europe | Prioritizing wetland conservation for birds, Midwestern USA | Envision, Puget Sound, WA, USA | VELMA (Visualizing Ecosystems for Land Management Assessments) plant-soil, Oregon, USA | Water storage in Lago Lucchetti, Puerto Rico, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs) v3.0 Water yield, Guánica Bay, Puerto Rico, USA | Value of a dive site (reef), St. Croix, USVI | Relative presence of Euchema sp. (on reef), St. Croix, USVI | Yasso07 v1.0.1 forest litter decomposition, Switzerland, site level | Nutrient Tracking Tool (NTT) | SolVES, Social Values for Ecosystem Services, Shoshone National Forest, WY | Blue-winged Teal duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Eastern bluebird abundance, Piedmont ecoregion, USA | Horned lark abundance, Piedmont ecoregion, USA | VELMA (Visualizing Ecosystems for Land Management Assessment) version 2.0 disturbance | Human well being indicator-poor health, Great Lakes waterfront, USA | Human well being indicator-Adult financial success, Great Lakes waterfront, USA | Ecosystem function and service quantification and valuation in a conventional winter wheat production system with DAISY model in Denmark | Development of an anthropogenic emissions processing system for Asia using SMOKE |
EM Source or Collection
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Envision | None | US EPA | EU Biodiversity Action 5 | None | Envision | US EPA | US EPA | US EPA | InVEST | US EPA | US EPA | None | None | None | None | None | None | US EPA | None | US EPA | None | None |
EM Source Document ID
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47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
271 | 137 | 228 | 122 |
313 ?Comment:Doc 314 is a secondary source. It is a webpage guide intended to provide support for developing an application using ENVISION. |
317 | 336 | 338 | 335 | 335 | 343 | 352 | 369 |
372 ?Comment:Document 373 is a secondary source for this EM. |
405 | 405 | 366 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
464 | 481 |
Document Author
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Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Haines-Young, R., Potschin, M. and Kienast, F. | Thogmartin, W. A., Potter, B. A. and Soulliere, G. J. | Bolte, J. and Vache, K. | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Bousquin, J., W.S. Fisher, J. Carriger, and E. Huertas | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Didion, M., B. Frey, N. Rogiers, and E. Thurig | Saleh, A. and O. Gallego | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | 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 | Riffel, S., Scognamillo, D., and L. W. Burger | Riffel, S., Scognamillo, D., and L. W. Burger | McKane, R. B., A. Brookes, K. Djang, M. Stieglitz, A. G. Abdelnour, F. Pan, J. J. Halama, P. B. Pettus and D. L. Phillips | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | Ghaley, B. B., & Porter, J. R. | Woo, J.H., Choi, K.C., Kim, H.K., Baek, B.H., Jang, M., Eum, J.H., Song, C.H., Ma, Y.I., Sunwoo, Y., Chang, L.S. and Yoo, S.H. |
Document Year
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2008 | 2008 | 2011 | 2012 | 2011 | 2010 | 2013 | 2014 | 2017 | 2014 | 2014 | 2014 | 2018 | 2014 | 2010 | 2008 | 2008 | 2014 | None | None | 2014 | 2012 |
Document Title
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Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Mapping ecosystem services for planning and management | AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Bridging the conservation design and delivery gap for wetland bird habitat maintenance and restoration in the midwestern United States | Envisioning Puget Sound Alternative Futures: PSNERP Final Report | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | A Bayesian belief network approach to explore alternative decisions for sediment control and water storage capacity at Lago Lucchetti, Puerto Rico | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Validating tree litter decomposition in the Yasso07 carbon model | Nutrient Tracking Tool (NTT) User Manual | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | VELMA Version 2.0 User Manual and Technical Documentation | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | Ecosystem function and service quantification and valuation in a conventional winter wheat production system with DAISY model in Denmark | Development of an anthropogenic emissions processing system for Asia using SMOKE |
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 | Documentation is peer-reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed but unpublished (explain in Comment) | Peer reviewed but unpublished (explain in Comment) | 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 report | Published journal manuscript | Published EPA report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | webpage | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published report | Journal manuscript submitted or in review | Journal manuscript submitted or in review | Published journal manuscript | Published journal manuscript |
EM ID
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EM-12 ![]() |
EM-84 | EM-97 | EM-99 | EM-113 |
EM-369 ![]() |
EM-380 ![]() |
EM-421 | EM-437 | EM-455 | EM-461 |
EM-485 ![]() |
EM-549 | EM-626 | EM-701 | EM-840 | EM-842 | EM-887 | EM-889 | EM-894 |
EM-992 ![]() |
EM-1019 |
http://evoland.bioe.orst.edu/ ?Comment:Software is likely available. |
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 | Not applicable | http://envision.bioe.orst.edu | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable | http://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | http://ntt.tiaer.tarleton.edu/welcomes/new?locale=en | Not applicable | Not applicable | Not applicable | Not applicable | https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | Not applicable | Not applicable | Not applicable | https://www.cmascenter.org/smoke/ | |
Contact Name
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Michael R. Guzy | Roland E Schulze | Yongping Yuan | Marion Potschin | Wayne Thogmartin, USGS |
John Bolte ?Comment:Phone# 541-737-2041 |
Alex Abdelnour | Susan Yee | Susan H. Yee | Susan H. Yee | Susan H. Yee | Markus Didion |
Ali Saleh ?Comment:Phone # 254-968-9079 |
Benson Sherrouse | David Otis | Sam Riffell | Sam Riffell | Robert B. McKane | Ted Angradi | Ted Angradi | Bhim Bahadur Ghaley | Jung-Hun Woo |
Contact Address
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Oregon State University, Dept. of Biological and Ecological Engineering | School of Bioresources Engineering and Environmental Hydrology, University of Natal, South Africa | U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Upper Midwest Environmental Sciences Center, 2630 Fanta Reed Road, La Crosse, WI 54603 | Oregon State University, Dept. of Biological & Ecological Engineering, 116C Gilmore Hall, Corvallis, OR 97333 | Dept. of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | NHEERL Gulf Ecology Division, Gulf Breeze FL 32561 | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Associate Director, Texas Institute for Applied Environmental Research, P.O. Box T410, Tarleton State University Stephenville, TX 76402 | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | U.S. EPA, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon 97333 | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen, Højbakkegård Allé 30, DK-2630 Taastrup, Denmark. | Department of Advanced Technology Fusion, Room 812, San-Hak Bldg., Konkuk University, Seoul, South Korea |
Contact Email
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Not reported | schulzeR@nu.ac.za | yuan.yongping@epa.gov | marion.potschin@nottingham.ac.uk | wthogmartin@usgs.gov | boltej@engr.orst.edu | abdelnouralex@gmail.com | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | markus.didion@wsl.ch | saleh@tarleton.edu | bcsherrouse@usgs.gov | dotis@iastate.edu | sriffell@cfr.msstate.edu | sriffell@cfr.msstate.edu | mckane.bob@epa.gov | tedangradi@gmail.com | tedangradi@gmail.com | bbg@life.ku.dk | jwoo@konkuk.ac.kr |
EM ID
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EM-12 ![]() |
EM-84 | EM-97 | EM-99 | EM-113 |
EM-369 ![]() |
EM-380 ![]() |
EM-421 | EM-437 | EM-455 | EM-461 |
EM-485 ![]() |
EM-549 | EM-626 | EM-701 | EM-840 | EM-842 | EM-887 | EM-889 | EM-894 |
EM-992 ![]() |
EM-1019 |
Summary Description
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**Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | AUTHOR'S DESCRIPTION (Doc ID 272): "ACRU is a daily timestep, physical conceptual and multipurpose model structured to simulate impacts of land cover/ use change. The model can output, inter alia, components of runoff, irrigation supply and demand, reservoir water budgets as well as sediment and crop yields." AUTHOR'S DESCRIPTION (Doc ID 271): "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…The total benefit to people of water supply is a function of both the quantity and quality with the ecosystem playing a key role in the latter. However, due to the lack of suitable national scale data on water quality for quantifying the service, runoff was used as an estimate of the benefit where runoff is the total water yield from a watershed including surface and subsurface flow. This assumes that runoff is positively correlated with quality, which is the case in South Africa (Allanson et al., 1990)…In South Africa, water resources are mapped in water management areas called catchments (vs. watersheds) where a catchment is defined as the area of land that is drained by a single river system, including its tributaries (DWAF, 2004). There are 1946 quaternary (4th order) catchments in South Africa, the smallest is 4800 ha and the average size is 65,000 ha. Schulze (1997) modelled annual runoff for each quaternary catchment. During modelling of runoff, he used rainfall data collected over a period of more than 30 years, as well as data on other climatic factors, soil characteristics and grassland as the land cover. In this study, median annual simulated runoff was used as a measure of surface water supply. The volume of runoff per quaternary catchment was calculated for surface water supply. The range (areas with runoff of 30 million m^3 or more) and hotspots (areas with runoff of 70 million m^3 or more) were defined using a combination of statistics and expert inputs due to a lack of published thresholds in the literature." | 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" | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Crop-based production” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain." AUTHOR'S DESCRIPTION: "The analysis for "Crop-based production" maps all the areas that are important for food crops produced through commercial agriculture." | ABSTRACT: "The U.S. Fish and Wildlife Service’s adoption of Strategic Habitat Conservation is intended to increase the effectiveness and efficiency of conservation delivery by targeting effort in areas where biological benefits are greatest. Conservation funding has not often been allocated in accordance with explicit biological endpoints, and the gap between conservation design (the identification of conservation priority areas) and delivery needs to be bridged to better meet conservation goals for multiple species and landscapes. We introduce a regional prioritization scheme for North American Wetlands Conservation Act funding which explicitly addresses Midwest regional goals for wetland-dependent birds. We developed decision-support maps to guide conservation of breeding and non-breeding wetland bird habitat. This exercise suggested ~55% of the Midwest consists of potential wetland bird habitat, and areas suited for maintenance (protection) were distinguished from those most suited to restoration. Areas with greater maintenance focus were identified for central Minnesota, southeastern Wisconsin, the Upper Mississippi and Illinois rivers, and the shore of western Lake Erie and Saginaw Bay. The shores of Lakes Michigan and Superior accommodated fewer waterbird species overall, but were also important for wetland bird habitat maintenance. Abundant areas suited for wetland restoration occurred in agricultural regions of central Illinois, western Iowa, and northern Indiana and Ohio. Use of this prioritization scheme can increase effectiveness, efficiency, transparency, and credibility to land and water conservation efforts for wetland birds in the Midwestern United States." | SUMMARY: "...the Puget Sound Nearshore Ecosystem Restoration Project, completed an analysis of alternative future regional trajectories of landscape change for the Puget Sound region. This effort developed three scenarios of change: 1) Status Quo, reflecting a continuation of current trends in the region, 2) Managed Growth, reflecting the adoption of an aggressive set of land use management policies focusing on protecting and restoring ecosystem function and concentrating growth within Urban Growth Areas (UGA) and near regional growth centers, and 3) Unmanaged Growth, reflecting a relaxation of land use restrictions with limited protection of ecosystem functions. Analyses assumed a fixed population growth rate across all three scenarios, defined by the Washington Office of Financial Management county level growth estimates. Scenarios were generated using a spatially- and temporally-explicit alternative futures analysis model, Envision, previously developed by Oregon State University researchers. The model accepts as input a vector-based representation of the landscape and associated datasets describing relevant landscape characteristics, descriptors of various processes influencing landscape change, and a set of policies, or decision alternatives, which reflect scenario-specific land management alternatives. The model generates 1) a set of spatial coverages (maps) reflecting scenario outcomes of a variety of landscape variables, most notably land use/land cover, shoreline modifications, and population projections, and 2) a set of summary statistics describing landscape change variables summarized across spatial reporting units. Analyses were run on each of such sub-basins in the Puget Sound, and aggregated to providing Sound-wide results. This information is being used by PSNERP to project future impairment of ecosystem functions, goods, and services. The Puget Sound Nearshore Ecosystem project data also provide inputs to calculate aspects of future nearshore process degradation. Impairment and degradation are primary factors being used to define future conditions for the PSNERP General Investigation Study." AUTHOR'S DESCRIPTION: "In this report, we document the application of an alternative futures analysis framework that incorporates these capabilities to the analysis of alternative future trajectories in the Puget Sound region. This framework, Envision (Bolte et al, 2007; Hulse et al. 2008) is a spatially and temporally explicit, standards-based, open source toolset specifically designed to facilitate alternative futures analyses. It employs a multiagent-based modeling approach that contains a robust capability for defining alternative management strategies and scenarios, incorporating a variety of landscape change processes, and creating maps of alternative landscape trajectories, expressed though a variety of metrics defined in an application-specific way." ABOUT ENVISION (ENVISION WEBSITE): "Central to Envision, and conceived at the s | ABSTRACT: "We used a new ecohydrological model, Visualizing Ecosystems for Land Management Assessments (VELMA), to analyze the effects of forest harvest on catchment carbon and nitrogen dynamics. We applied the model to a 10 ha headwater catchment in the western Oregon Cascade Range where two major disturbance events have occurred during the past 500 years: a stand-replacing fire circa 1525 and a clear-cut in 1975. Hydrological and biogeochemical data from this site and other Pacific Northwest forest ecosystems were used to calibrate the model. Model parameters were first calibrated to simulate the postfire buildup of ecosystem carbon and nitrogen stocks in plants and soil from 1525 to 1969, the year when stream flow and chemistry measurements were begun. Thereafter, the model was used to simulate old-growth (1969–1974) and postharvest (1975–2008) temporal changes in carbon and nitrogen dynamics…" AUTHOR'S DESCRIPTION: "The soil column model consists of three coupled submodels:...a plant-soil model (Figure (A3)) that simulates ecosystem carbon storage and the cycling of C and N between a plant biomass layer and the active soil pools. Specifically, the plant-soil model simulates the interaction among aboveground plant biomass, soil organic carbon (SOC), soil nitrogen including dissolved nitrate (NO3), ammonium (NH4), and organic nitrogen, as well as DOC (equations (A7)–(A12)). Daily atmospheric inputs of wet and dry nitrogen deposition are accounted for in the ammonium pool of the shallow soil layer (equation (A13)). Uptake of ammonium and nitrate by plants is modeled using a Type II Michaelis-Menten function (equation (A14)). Loss of plant biomass is simulated through a density-dependent mortality. The mortality rate and the nitrogen uptake rate mimic the exponential increase in biomass mortality and the accelerated growth rate, respectively, as plants go through succession and reach equilibrium (equations (A14)–(A18)). Vertical transport of nutrients from one layer to another in a soil column is a function of water drainage (equations (A19)–(A22)). Decomposition of SOC follows first-order kinetics controlled by soil temperature and moisture content as described in the terrestrial ecosystem model (TEM) of Raich et al. [1991] (equations (A23)–(A26)). Nitrification (equations (A27)–(A30)) and denitrification (equations (A31)–(A34)) were simulated using the equations from the generalized model of N2 and N2O production of Parton et al. [1996, 2001] and Del Grosso et al. [2000]. [12] The soil column model is placed within a catchment framework to create a spatially distributed model applicable to watersheds and landscapes. Adjacent soil columns interact with each other through the downslope lateral transport of water and nutrients (Figure (A1)). Surface and subsurface lateral flow are routed using a multiple flow direction method [Freeman, 1991; Quinn et al., 1991]. As with vertical drainage of soil water, lateral subsurface downslope flow i | AUTHORS DESCRIPTION: " A Bayesian belief network (BBN) was developed to characterize the effects of sediment accumulation on the water storage capacity of Lago Lucchetti (located in southwest Puerto Rico) and to forecast the life expectancy (usefulness) of the reservoir under different management scenarios. The conceptual approach included water and sediment delivery from two sources, from the Lucchetti watershed and from a tunnel linking Lago Lucchetti to three upstream reservoirs. Variables in the model included precipitation and erosion factors (soil type, landscape slope, and land use) applied to the Lucchetti watershed and to watersheds of the upstream reservoirs. The lack of available data for water and sediment flows in the watershed and through tunnels connecting the reservoirs led to several unique methods for network data acquisition. Status quo model runs demonstrated that sediment trapping has continuously declined in all four reservoirs since their construction and that every year a greater proportion of sediment is moving downstream through tunnels or spillways. The model compared favorably with incidental measured data in the region. Sensitivity analysis demonstrated that current sediment accumulation in Lago Lucchetti can be attributed in large part to sediment erosion from the Lucchetti watershed with only minor influence (~8%) from upstream reservoirs. Two decision scenarios were explored by including additional nodes for (1) partial and full conversion of sungrown land use to shadegrown coffee cultivation in the Lucchetti watershed and (2) partial or complete dredging the reservoir. Both management actions were examined singly and in combination for effects on reservoir life expectancy (beyond the year 2000) with varying water capacity targets. Using 50% water storage capacity as a target, model runs for status quo (no decision implemented) resulted in a probability range that averaged 5.57 years (or 50% water storage capacity in the year 2005). Partial and full conversion of land from sungrown to shadegrown coffee cultivation raised the life expectancy to 5.89 and 6.57 years, respectively. Partial and complete dredging of the reservoir resulted in a life expectancy of 37.3 and 40.7 years, and full implementation of both management options resulted in a life expectancy of 44.4 years." | 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: "Stakeholders identified an objective of meeting water demands for agriculture and domestic purposes, including irrigation, drinking water, or hydropower production…Geomorphology, climate, and vegetation determine the amount of water runoff from the landscape that could be available for consumptive uses. Long-term average water yield was estimated for each HUC12 sub-watershed as the difference between total precipitation and the amount absorbed by the different land cover classes using a reservoir hydropower production model (InVEST 3.0.0; Tallis et al., 2013)." | 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)…Another method to quantify recreational opportunities is to use survey data of tourists and recreational visitors to the reefs to generate statistical models to quantify the link between reef condition and production of recreation-related ecosystem services. Wielgus et al. (2003) used interviews with SCUBA divers in Israel to derive coefficients for a choice model in which willingness to pay for higher quality dive sites was determined in part by a weighted combination of factors identified with dive quality: Relative value of dive site = 0.1227(Scoral+Sfish+Acoral+Afish)+0.0565V where Scoral, Sfish are coral and fish richness, Acoral, Afish are abundances of fish and coral per square meter, and V is water visibility (meters)." | 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:...(4) density of Euchema sp. seaweed," | ABSTRACT: "...We examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to... (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests; and (iii) evaluate the suitability of Yasso07 for regional and national scale applications in Swiss forests." AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "The decomposition of below- and aboveground litter was studied over 10 years on five forest sites in Switzerland…" "At the time of this study, three parameter sets have been developed and published:... (3): Rantakari et al., 2012 (henceforth P12)… For the development of P12, Rantakari et al. (2012) obtained a subset of the previously used data which was restricted to European sites." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root lit-ter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The r | AUTHOR'S DESCRIPTION: "The Nutrient Tracking Tool (NTT) was designed and developed by the Texas Institute for Applied Environmental Research (TIAER), Tarleton State University with funding from USDA Office of Environmental Markets, USDA-NRCS Conservation Innovation Grants program, and various state agencies. NTT is a web-based, site-specific application that estimates nutrient and sediment losses at the field scale or at the small watershed scale. Agricultural producers and land managers can define a number of management scenarios and generate a report showing the expected nutrient loss differences between any selected scenarios for a given field or small watershed. NTT compares agricultural management systems to calculate a change in expected flow, nitrogen, phosphorus, sediment losses, and crop yield. Estimates are made using the APEX model (Williams et al. 2000). Results represent average losses from the field based on 35 years of simulated weather. NTT requires regional soils, climate and site-specific crop management information. NTT currently provides selections for all regions of U.S. and Puerto Rico territory, but it has only been validated for a limited number of states and counties. As validation becomes possible in other parts of the country, parameter files may be updated for additional counties in future versions. There are two versions of new NTT program available: The BASIC version is a user-friendly version of NTT that allows users to estimate N, P and sediment from crop and pasture lands. The Research and Education version of NTT (NTT-RE) was developed for researchers and educational institutes for teaching and training purposes. NTT-RE includes additional functions allowing the user to view and edit soil layers, view crop water and nutrient stresses, and modify and the APEX parameters file for calibration and validation purposes. The data sources and APEX simulations in both versions are identical. For more information regarding NTT, please refer to Saleh et al. (2011 and 2015)." | 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: "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 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 positivelyrelated to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds " | ABSTRACT:"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." | VELMA – Visualizing Ecosystems for Land Management Assessments - is a spatially distributed, eco-hydrological model that links a land surface hydrology model with a terrestrial biogeochemistry model for simulating the integrated responses of vegetation, soil, and water resources to interacting stressors. For example, VELMA can simulate how changes in climate and land use interact to affect soil water storage, surface and subsurface runoff, vertical drainage, evapotranspiration, vegetation and soil carbon and nitrogen dynamics, and transport of nitrate, ammonium, and dissolved organic carbon and nitrogen to water bodies. VELMA differs from other existing eco-hydrology models in its simplicity, flexibility, and theoretical foundation. The model has a user-friendly Graphics User Interface (GUI) for easy input of model parameter values. In addition, advanced visualization of simulation results can enhance understanding of results and underlying concepts. VELMA’s visualization and interactivity features are packaged in an open-source, open-platform programming environment (Java / Eclipse). The development team for VELMA version 2.0 includes Dr. Bob McKane and coworkers at the U.S. Environmental Protection Agency’s Western Ecology Division, Dr. Marc Stieglitz and coworkers at the Georgia Institute of Technology, and Dr. Feifei Pan at the University of North Texas. AUTHOR'S DESCRIPTION: "Understanding how disturbances such as harvest, fire and fertilization affect ecosystem services has been a major motivation in the development of VELMA. For example, how do disturbances such as forest harvest or the application of agronomic fertilizers affect hydrological and biogeochemical processes controlling water quality and quantity, carbon sequestration, production of greenhouse gases, etc.? Abdelnour et al. (2011, 2013) have already demonstrated the use of VELMA v1.0 to simulate the effects of forest clearcutting on ecohydrological processes that regulate a variety of ecosystem services. With the addition of a tissue-specific plant biomass (LSR) simulator and an enhanced GUI, VELMA v2.0 significantly expands the detail, flexibility, and ease of use for simulating disturbance effects. Currently available disturbance models include: - BurnDisturbanceModel, effects of fire. - GrazeDisturbanceModel, effects of grazing. - FertilizeLsrDisturbanceModel, effects of fertilizer applications. - HarvestLsrDisturbanceModel, effects of biomass harvest. Each of these disturbance models specifies where and when a disturbance event will occur. The Burn, Graze and Harvest models have options for specifying how much of each plant tissue and detritus pool (leaves, stems, roots) will be removed and where it goes (offsite and/or to a specified onsite C and N pools). The Fertilize model has options for applying nitrogen as ammonium, nitrate, urea and/or manure." | ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context." | ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context. " | With inevitable link between ecosystem function (EF), ecosystem services (ES) and agricultural productivity, there is a need for quantification and valuation of EF and ES in agro-ecosystems. Management practices have significant effects on soil organic matter (SOM), affecting productivity, EF and ES provision. The objective was to quantify two EF: soil water storage and nitrogen mineralization and three ES: food and fodder production and carbon sequestration, in a conventional winter wheat production system at 2.6% SOM compared to 50% lower (1.3%) and 50% higher (3.9%) SOM in Denmark by DAISY model. At 2.6% SOM, the food and fodder production was 6.49 and 6.86 t ha−1 year−1 respectively whereas carbon sequestration and soil water storage was 9.73 t ha−1 year−1 and 684 mm ha−1 year−1 respectively and nitrogen mineralisation was 83.58 kg ha−1 year−1. At 2.6% SOM, the two EF and three ES values were US$ 177 and US$ 2542 ha−1 year−1 respectively equivalent to US$ 96 and US$1370 million year−1 respectively in Denmark. The EF and ES quantities and values were positively correlated with SOM content. Hence, the quantification and valuation of EF and ES provides an empirical tool for optimising the EF and ES provision for agricultural productivity. | Air quality modeling is a useful methodology to investigate air quality degradation in various locations and to analyze effectiveness of emission reduction plans. A comprehensive air quality model usually requires a coordinated set of emissions input of all necessary chemical species. We have developed an anthropogenic emissions processing system for Asia in support of air quality modeling and analysis over Asia (named SMOKE-Asia). The SMOKE (Sparse Matrix Operator kernel Emissions) system, which was developed by U.S. EPA and has been maintained by the Carolina Environmental Program (CEP) of the University of North Carolina, was used to develop our emissions processing system. A merged version of INTEX 2006 and TRACE-P 2000 inventories was used as an initial Asian emissions inventory. The IDA (Inventory Data Analyzer) format was used to create SMOKE-ready emissions. Source Classification Codes (SCCs) and country/state/county (FIPS) code, which are the two key data fields of SMOKE IDA data structure, were created for Asia. The 38 SCCs and 2752 FIPS codes were allocated to our SMOKE-ready emissions for more comprehensive processing. US EPA’s MIMS (Multimedia Integrated Modeling System) Spatial Allocator software, along with many global and regional GIS shapes, were used to create spatial allocation profiles for Asia. Temporal allocation and chemical speciation profiles were partly regionalized using Asia-based studies. Initial data production using the developed SMOKE-Asia system was successfully performed. NOx and VOC emissions for the year 2009 were projected to be increased by 50% from those of 1997. The emission hotspots, such as large cities and large point sources, are distinguished in the domain due to spatial allocation. Regional emission peaks were distinguished due to temporally resolved emission information. The PAR (Paraffin carbon bond) and XYL (Xylene and other polyalkyl aromatics) showed the first and second largest emission rate among VOC species. Most of point source emissions are located in layers 3 to 4, which the altitude range reaches 310–550 m AGL. Qualitative inter-comparison between model output and ground/satellite measurement showed good agreements in terms of spatial and temporal patterns. We expect that the result of this study will provide better air quality modeling inputs, which will act as a major step to improve our understanding of Asian air quality. |
Specific Policy or Decision Context Cited
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Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None identified | Not reported | None identified | Strategic habitat conservation by USFW for Wetland Conservation Act funding | None identified | None identified | None reported | Meeting water demands | None identified | None identified | None identified | None identified | None | None identified | None reported | None reported | None identified | None identified | None identified | None identified | None provided |
Biophysical Context
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No additional description provided | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | Upper Mississipi River basin, elevation 142-194m, | No additional description provided | Boreal Hardwood Transition, Eastern Tallgrass Prairie, Prairie Hardwood Transition, Central Hardwoods | No additional description provided | Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter. | No additional reported | No additional description provided | No additional description provided | No additional description provided | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | No additional description provided | Rocky mountain conifer forests | Prairie Pothole Region of Iowa | Conservation Reserve Program lands left to go fallow | Conservation Reserve Program lands left to go fallow | No additional description provided | Waterfront districts on south Lake Michigan and south lake Erie | Waterfront districts on south Lake Michigan and south lake Erie | Agro-ecosystem test farm, Copenhagen, Denmark. | Asia |
EM Scenario Drivers
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Five scenarios that include urban growth boundaries and various combinations of unconstrainted development, fish conservation, agriculture and forest reserves. ?Comment:Additional alternatives included adding agricultural and forest reserves, and adding or removing urban growth boundaries to the three main scenarios. |
No scenarios presented | Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | No scenarios presented | Conservation efforts for: marsh-wetland breeding birds, regional marsh and open-water for non-breeding birds, mudflat/shallows for birds during non-breeding period. | Alternative future land management strategies (status quo, managed growth, unmanaged growth) | Forest management (harvest/no harvest) | Management alternatives | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | N/A | No scenarios presented | N/A | N/A | No scenarios presented | N/A | N/A | A soil organic matter value of 1.3% was used for this model run | NA |
EM ID
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EM-12 ![]() |
EM-84 | EM-97 | EM-99 | EM-113 |
EM-369 ![]() |
EM-380 ![]() |
EM-421 | EM-437 | EM-455 | EM-461 |
EM-485 ![]() |
EM-549 | EM-626 | EM-701 | EM-840 | EM-842 | EM-887 | EM-889 | EM-894 |
EM-992 ![]() |
EM-1019 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) | Method + Application | Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Model runs are for different sites (Beatenberg, Vordemwald, Bettlachstock, Schanis, and Novaggio) differentiated by climate and forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). |
Method Only | Method + Application | Method + Application | Method + Application | Method + Application | Method Only | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | 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 | New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing 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-12 ![]() |
EM-84 | EM-97 | EM-99 | EM-113 |
EM-369 ![]() |
EM-380 ![]() |
EM-421 | EM-437 | EM-455 | EM-461 |
EM-485 ![]() |
EM-549 | EM-626 | EM-701 | EM-840 | EM-842 | EM-887 | EM-889 | EM-894 |
EM-992 ![]() |
EM-1019 |
EM Temporal Extent
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1990-2050 | 1950-1993 | 1980-2006 | 2000 | 2007 | 2000-2060 | 1969-2008 | 1962-2006 | 2006 - 2012 | 2006-2007, 2010 | 2006-2007, 2010 | 2000-2010 | 35 yr | 2004-2008 | 1987-2007 | 2008 | 2008 | Not applicable | 2022 | 2022 | 2003-2013 | 1997-2009 |
EM Time Dependence
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time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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future time | future time | Not applicable | Not applicable | Not applicable | future time | future time | future time | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | past time |
EM Time Continuity
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discrete | discrete | Not applicable | Not applicable | Not applicable | discrete | discrete | discrete | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | continuous | continuous |
EM Temporal Grain Size Value
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2 | 1 | Not applicable | Not applicable | Not applicable | 1 | 1 | 1 | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Year | Day | Not applicable | Not applicable | Not applicable | Year | Day | Year | Not applicable | Not applicable | Not applicable | Year | Day | Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
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EM-12 ![]() |
EM-84 | EM-97 | EM-99 | EM-113 |
EM-369 ![]() |
EM-380 ![]() |
EM-421 | EM-437 | EM-455 | EM-461 |
EM-485 ![]() |
EM-549 | EM-626 | EM-701 | EM-840 | EM-842 | EM-887 | EM-889 | EM-894 |
EM-992 ![]() |
EM-1019 |
Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Geopolitical | Watershed/Catchment/HUC | Geopolitical | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Geopolitical | Not applicable | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Physiographic or ecological | Not applicable | Geopolitical | Geopolitical | Physiographic or ecological | Geopolitical |
Spatial Extent Name
em.detail.extentNameHelp
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Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | South Africa | East Fork Kaskaskia River watershed basin | The EU-25 plus Switzerland and Norway | Upper Mississippi River and Great Lakes Region | Puget Sound watershed | H. J. Andrews LTER WS10 | Guanica Bay watershed | Guanica Bay watershed | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Switzerland | Not applicable | National Forest | CREP (Conservation Reserve Enhancement Program | Piedmont Ecoregion | Piedmont Ecoregion | Not applicable | Great Lakes waterfront | Great Lakes waterfront | Taastrup experimental farm | Asia |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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10-100 km^2 | >1,000,000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 10,000-100,000 km^2 | 10-100 ha | 100-1000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | Not applicable | 1000-10,000 km^2. | 10,000-100,000 km^2 | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | Not applicable | 1000-10,000 km^2. | 1000-10,000 km^2. | 1-10 km^2 | 1000-10,000 km^2. |
EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-84 | EM-97 | EM-99 | EM-113 |
EM-369 ![]() |
EM-380 ![]() |
EM-421 | EM-437 | EM-455 | EM-461 |
EM-485 ![]() |
EM-549 | EM-626 | EM-701 | EM-840 | EM-842 | EM-887 | EM-889 | EM-894 |
EM-992 ![]() |
EM-1019 |
EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) ?Comment:Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | other or unclear (comment) | spatially lumped (in all cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | area, for pixel or radial feature | Irregular | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable | 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 | Not applicable | Not applicable | Not applicable |
Spatial Grain Size
em.detail.spGrainSizeHelp
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varies | Distributed by catchments with average size of 65,000 ha | 1 km^2 | 1 km x 1 km | 1 ha | Varies | 30 m x 30 m surface pixel and 2-m depth soil column | Not reported | 30 m x 30 m | 10 m x 10 m | 10 m x 10 m | Not applicable | Not applicable | 30m2 | multiple, individual, irregular sites | Not applicable | Not applicable | user defined | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-84 | EM-97 | EM-99 | EM-113 |
EM-369 ![]() |
EM-380 ![]() |
EM-421 | EM-437 | EM-455 | EM-461 |
EM-485 ![]() |
EM-549 | EM-626 | EM-701 | EM-840 | EM-842 | EM-887 | EM-889 | EM-894 |
EM-992 ![]() |
EM-1019 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Numeric | Numeric | Numeric | Logic- or rule-based | Analytic | Numeric | Numeric | Numeric | Numeric | Analytic | Analytic | Numeric | Numeric | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Logic- or rule-based | Analytic | Numeric |
EM Determinism
em.detail.deterStochHelp
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stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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Comment:Agent based modeling results in response indices. |
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EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-84 | EM-97 | EM-99 | EM-113 |
EM-369 ![]() |
EM-380 ![]() |
EM-421 | EM-437 | EM-455 | EM-461 |
EM-485 ![]() |
EM-549 | EM-626 | EM-701 | EM-840 | EM-842 | EM-887 | EM-889 | EM-894 |
EM-992 ![]() |
EM-1019 |
Model Calibration Reported?
em.detail.calibrationHelp
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Unclear | No | No | No | No | Unclear | Yes | Yes | No | Yes | Yes | No | Not applicable | No | Unclear | Yes | Yes | Not applicable | No | No | Unclear | Unclear |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | No | No | No | Not applicable | No | No | No | No | No | No | Not applicable | Yes | No | No | No | Not applicable | No | No | Unclear | Unclear |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | 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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No | No | Yes | Yes | No | Not applicable | No | No | No | Yes | Yes | Yes | Unclear | No | No | No | No | Not applicable | No | No | Yes | Yes |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | Yes | No | No | Not applicable | No | No | No | No | No | Yes | Not applicable | No | No | No | No | Not applicable | No | No | Unclear | Unclear |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No ?Comment:Sensitivity analysis performed for agent values only. |
No | Unclear | No | No | Not applicable | Yes | No | No | No | No | No | Not applicable | No | No | Yes | Yes | Not applicable | Yes | Yes | Unclear | Unclear |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-12 ![]() |
EM-84 | EM-97 | EM-99 | EM-113 |
EM-369 ![]() |
EM-380 ![]() |
EM-421 | EM-437 | EM-455 | EM-461 |
EM-485 ![]() |
EM-549 | EM-626 | EM-701 | EM-840 | EM-842 | EM-887 | EM-889 | EM-894 |
EM-992 ![]() |
EM-1019 |
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None | None |
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None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-12 ![]() |
EM-84 | EM-97 | EM-99 | EM-113 |
EM-369 ![]() |
EM-380 ![]() |
EM-421 | EM-437 | EM-455 | EM-461 |
EM-485 ![]() |
EM-549 | EM-626 | EM-701 | EM-840 | EM-842 | EM-887 | EM-889 | EM-894 |
EM-992 ![]() |
EM-1019 |
None | None | None | None | None |
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None | None |
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None | None | None | None | None | None | None | None | None | None |
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Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-84 | EM-97 | EM-99 | EM-113 |
EM-369 ![]() |
EM-380 ![]() |
EM-421 | EM-437 | EM-455 | EM-461 |
EM-485 ![]() |
EM-549 | EM-626 | EM-701 | EM-840 | EM-842 | EM-887 | EM-889 | EM-894 |
EM-992 ![]() |
EM-1019 |
Centroid Latitude
em.detail.ddLatHelp
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44.11 | -30 | 38.69 | 50.53 | 42.05 | 47.58 | 44.25 | 18.09 | 17.96 | 17.73 | 17.73 | 46.82 | Not applicable | 43.98 | 42.62 | 36.23 | 36.23 | Not applicable | 42.26 | 42.26 | 55.4 | 38.63 |
Centroid Longitude
em.detail.ddLongHelp
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-123.09 | 25 | -89.1 | 7.6 | -88.6 | -122.32 | -122.33 | -66.86 | -67.02 | -64.77 | -64.77 | 8.23 | Not applicable | 109.52 | -93.84 | -81.9 | -81.9 | Not applicable | -87.84 | -87.84 | 12.18 | 117.79 |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | None provided | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated | Provided | Estimated |
EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-84 | EM-97 | EM-99 | EM-113 |
EM-369 ![]() |
EM-380 ![]() |
EM-421 | EM-437 | EM-455 | EM-461 |
EM-485 ![]() |
EM-549 | EM-626 | EM-701 | EM-840 | EM-842 | EM-887 | EM-889 | EM-894 |
EM-992 ![]() |
EM-1019 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Aquatic Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Ground Water | Forests | Lakes and Ponds | Inland Wetlands | Near Coastal Marine and Estuarine | Open Ocean and Seas | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Forests | Inland Wetlands | Agroecosystems | Grasslands | Grasslands | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Agricultural-urban interface at river junction | Not reported | Row crop agriculture in Kaskaskia river basin | Not applicable | Not reported | Pacific NW US region, coastal to montane, urban to rural | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | Lago Luchetti | 13 LULC were used | Coral reefs | Coral reefs | forests | Agroecosystems | Montain forest | Wetlands buffered by grassland within agroecosystems | grasslands | grasslands | Terrestrial environment sub-classes | Lake Michigan & Lake Erie waterfront | Lake Michigan & Lake Erie waterfront | Agroecosystems | Asian atmosphere |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is 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 is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale 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 | Not applicable |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-84 | EM-97 | EM-99 | EM-113 |
EM-369 ![]() |
EM-380 ![]() |
EM-421 | EM-437 | EM-455 | EM-461 |
EM-485 ![]() |
EM-549 | EM-626 | EM-701 | EM-840 | EM-842 | EM-887 | EM-889 | EM-894 |
EM-992 ![]() |
EM-1019 |
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Species | Not applicable | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Species | Community | Not applicable | Not applicable | Individual or population, within a species | Species | Species | Not applicable | Not applicable | Not applicable |
Guild or Assemblage ?Comment:Microbrial biomass is lumped together, but specific crops are presented. |
Not applicable |
Taxonomic level and name of organisms or groups identified
EM-12 ![]() |
EM-84 | EM-97 | EM-99 | EM-113 |
EM-369 ![]() |
EM-380 ![]() |
EM-421 | EM-437 | EM-455 | EM-461 |
EM-485 ![]() |
EM-549 | EM-626 | EM-701 | EM-840 | EM-842 | EM-887 | EM-889 | EM-894 |
EM-992 ![]() |
EM-1019 |
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None Available | None Available | None Available |
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None Available | None Available | None Available | None Available | None Available |
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None Available | None Available | None Available |
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None Available | None Available | None Available |
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None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-12 ![]() |
EM-84 | EM-97 | EM-99 | EM-113 |
EM-369 ![]() |
EM-380 ![]() |
EM-421 | EM-437 | EM-455 | EM-461 |
EM-485 ![]() |
EM-549 | EM-626 | EM-701 | EM-840 | EM-842 | EM-887 | EM-889 | EM-894 |
EM-992 ![]() |
EM-1019 |
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None | None |
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None | 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-12 ![]() |
EM-84 | EM-97 | EM-99 | EM-113 |
EM-369 ![]() |
EM-380 ![]() |
EM-421 | EM-437 | EM-455 | EM-461 |
EM-485 ![]() |
EM-549 | EM-626 | EM-701 | EM-840 | EM-842 | EM-887 | EM-889 | EM-894 |
EM-992 ![]() |
EM-1019 |
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None | None |
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None |
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None |
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None | None | None |
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