EcoService Models Library (ESML)
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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.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-65 | EM-71 | EM-91 | EM-93 | EM-193 | EM-260 | EM-303 |
EM-369 
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EM-414 |
EM-542
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EM-719
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EM-890 | EM-941 | EM-964 | EM-1011 |
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EM Short Name
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Green biomass production, Central French Alps | Community flowering date, Central French Alps | RHyME2, Upper Mississippi River basin, USA | Stream nitrogen removal, Mississippi R. basin, USA | Cultural ecosystem services, Bilbao, Spain | Coral taxa and land development, St.Croix, VI, USA | Biological pest control, Uppland Province, Sweden | Envision, Puget Sound, WA, USA | SAV occurrence, St. Louis River, MN/WI, USA | Coastal protection in Belize | Seed mix for native plant establishment, IA, USA | HWB Blood pressure, Great Lakes waterfront, USA | ESTIMAP - Pollination potential, Iran | EcoSim II - method | WMOST method |
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EM Full Name
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Green biomass production, Central French Alps | Community weighted mean flowering date, Central French Alps | RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | Stream nitrogen removal, Upper Mississippi, Ohio and Missouri River sub-basins, USA | Cultural ecosystem services, Bilbao, Spain | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | Biological control of agricultural pests by natural predators, Uppland Province, Sweden | Envision, Puget Sound, WA, USA | Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA | Coastal Protection provided by Coral, Seagrasses and Mangroves in Belize: | Cost-effective seed mix design for native plant establishment, Iowa, USA | Human well being indicator- Blood pressure, Great Lakes waterfront, USA | ESTIMAP - Pollination potential, Iran | EcoSim II - method | Watershed Management Optimization Support Tool (WMOST) v1 method |
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EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | US EPA | US EPA |
None ?Comment:EU Mapping Studies |
US EPA | None | Envision | US EPA | InVEST | None | None | None | None | US EPA |
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EM Source Document ID
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260 | 260 | 123 | 52 | 191 | 96 | 299 |
313 ?Comment:Doc 314 is a secondary source. It is a webpage guide intended to provide support for developing an application using ENVISION. |
330 | 350 | 394 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
434 | 448 | 477 |
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Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C. | Hill, B. and Bolgrien, D. | Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Jonsson, M., Bommarco, R., Ekbom, B., Smith, H.G., Bengtsson, J., Caballero-Lopez, B., Winqvist, C., and Olsson, O. | Bolte, J. and Vache, K. | Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke | Guannel, G., Arkema, K., Ruggiero, P., and G. Verutes | Meissen, J. | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | Rahimi, E., Barghjelveh, S., and P. Dong | Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell | United States EPA |
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Document Year
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2011 | 2011 | 2013 | 2011 | 2013 | 2011 | 2014 | 2010 | 2013 | 2016 | 2018 | None | 2020 | 2000 | 2013 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads | Nitrogen removal by streams and rivers of the Upper Mississippi River basin | Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Ecological production functions for biological control services in agricultural landscapes | Envisioning Puget Sound Alternative Futures: PSNERP Final Report | Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary | The Power of Three: Coral Reefs, Seagrasses and Mangroves Protect Coastal Regions and Increase Their Resilience | Cost-effective seed mix design and first-year management | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran) | Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II | Watershed Management Optimization Support Tool (WMOST) v1 User manual |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Documentation is 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 and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published 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 report | Journal manuscript submitted or in review | Published journal manuscript | Published journal manuscript | Published EPA report |
Software and Access
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EM ID
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EM-65 | EM-71 | EM-91 | EM-93 | EM-193 | EM-260 | EM-303 |
EM-369
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EM-414 |
EM-542
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EM-719
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EM-890 | EM-941 | EM-964 | EM-1011 |
| Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://envision.bioe.orst.edu | Not applicable | Not identified in paper | Not applicable | Not applicable | Not applicable | https://ecopath.org/downloads/ | https://cfpub.epa.gov/si/si_public_record_report.cfm?Lab=NHEERL&dirEntryId=262280 | |
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Contact Name
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Sandra Lavorel | Sandra Lavorel | Liem Tran | Brian Hill | Izaskun Casado-Arzuaga | Leah Oliver | Mattias Jonsson |
John Bolte ?Comment:Phone# 541-737-2041 |
Ted R. Angradi | Greg Guannel | Justin Meissen | Ted Angradi | Ehsan Rahini | Carl Walters | Naomi Detenbeck |
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Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA | Mid-Continent Ecology Division NHEERL, ORD. USEPA 6201 Congdon Blvd. Duluth, MN 55804, USA | Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain | National Health and Environmental Research Effects Laboratory | Department of Ecology, Swedish University of Agricultural Sciences, PO Box 7044, SE-750 07 Uppsala, Sweden | Oregon State University, Dept. of Biological & Ecological Engineering, 116C Gilmore Hall, Corvallis, OR 97333 | U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA | The Nature Conservancy, Coral Gables, FL. USA | Tallgrass Prairie Center, University of Northern Iowa | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran | Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4 | NHEERL, Atlantic Ecology Division Narragansett, RI 02882 |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | ltran1@utk.edu | hill.brian@epa.gov | izaskun.casado@ehu.es | leah.oliver@epa.gov | mattias.jonsson@slu.se | boltej@engr.orst.edu | angradi.theodore@epa.gov | greg.guannel@gmail.com | Not reported | tedangradi@gmail.com | ehsanrahimi666@gmail.com | c.walters@oceans.ubc.ca | detenbeck.naomi@epa.gov |
EM Description
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EM ID
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EM-65 | EM-71 | EM-91 | EM-93 | EM-193 | EM-260 | EM-303 |
EM-369
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EM-414 |
EM-542
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EM-719
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EM-890 | EM-941 | EM-964 | EM-1011 |
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Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | ABSTRACT: "We describe a framework called Regional Hydrologic Modeling for Environmental Evaluation (RHyME2) for hydrologic modeling across scales. Rooted from hierarchy theory, RHyME2 acknowledges the rate-based hierarchical structure of hydrological systems. Operationally, hierarchical constraints are accounted for and explicitly described in models put together into RHyME2. We illustrate RHyME2with a two-module model to quantify annual nutrient loads in stream networks and watersheds at regional and subregional levels. High values of R2 (>0.95) and the Nash–Sutcliffe model efficiency coefficient (>0.85) and a systematic connection between the two modules show that the hierarchy theory-based RHyME2 framework can be used effectively for developing and connecting hydrologic models to analyze the dynamics of hydrologic systems." Two EMs will be entered in EPF-Library: 1. Regional scale module (Upper Mississippi River Basin) - this entry 2. Subregional scale module (St. Croix River Basin) | 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)." | ABSTRACT "This paper presents a method to quantify cultural ecosystem services (ES) and their spatial distribution in the landscape based on ecological structure and social evaluation approaches. The method aims to provide quantified assessments of ES to support land use planning decisions. A GIS-based approach was used to estimate and map the provision of recreation and aesthetic services supplied by ecosystems in a peri-urban area located in the Basque Country, northern Spain. Data of two different public participation processes (frequency of visits to 25 different sites within the study area and aesthetic value of different landscape units) were used to validate the maps. Three maps were obtained as results: a map showing the provision of recreation services, an aesthetic value map and a map of the correspondences and differences between both services. The data obtained in the participation processes were found useful for the validation of the maps. A weak spatial correlation was found between aesthetic quality and recreation provision services, with an overlap of the highest values for both services only in 7.2 % of the area. A consultation with decision-makers indicated that the results were considered useful to identify areas that can be targeted for improvement of landscape and recreation management." | 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: "We develop a novel, mechanistic landscape model for biological control of cereal aphids, explicitly accounting for the influence of landscape composition on natural enemies varying in mobility, feeding rates and other life history traits. Finally, we use the model to map biological control services across cereal fields in a Swedish agricultural region with varying landscape complexity. The model predicted that biological control would reduce crop damage by 45–70% and that the biological control effect would be higher in complex landscapes. In a validation with independent data, the model performed well and predicted a significant proportion of biological control variation in cereal fields. However, much variability remains to be explained, and we propose that the model could be improved by refining the mechanistic understanding of predator dynamics and accounting for variation in aphid colonization." | 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: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake." | AUTHOR'S DESCRIPTION: "Natural habitats have the ability to protect coastal communities against the impacts of waves and storms, yet it is unclear how different habitats complement each other to reduce those impacts. Here, we investigate the individual and combined coastal protection services supplied by live corals on reefs, seagrass meadows, and mangrove forests during both non-storm and storm conditions, and under present and future sea-level conditions. Using idealized profiles of fringing and barrier reefs, we quantify the services supplied by these habitats using various metrics of inundation and erosion. We find that, together, live corals, seagrasses, and mangroves supply more protection services than any individual habitat or any combination of two habitats. Specifically, we find that, while mangroves are the most effective at protecting the coast under non-storm and storm conditions, live corals and seagrasses also moderate the impact of waves and storms, thereby further reducing the vulnerability of coastal regions. Also, in addition to structural differences, the amount of service supplied by habitats in our analysis is highly dependent on the geomorphic setting, habitat location and forcing conditions: live corals in the fringing reef profile supply more protection services than seagrasses; seagrasses in the barrier reef profile supply more protection services than live corals; and seagrasses, in our simulations, can even compensate for the long-term degradation of the barrier reef. Results of this study demonstrate the importance of taking integrated and place-based approaches when quantifying and managing for the coastal protection services supplied by ecosystems." | AUTHOR'S DESCRIPTION: "Restoring ecosystem services at scale requires executing conservation programs in a way that is resource and cost efficient as well as ecologically effective…Seed mix design is one of the largest determinants of project cost and ecological outcomes for prairie reconstructions. In particular, grass-to-forb seeding ratio affects cost since forb seed can be much more expensive relative to grass species (Prairie Moon Nursery 2012). Even for seed mixes with the same overall seeding rates, a mix with a low grass-to-forb seeding ratio is considerably more expensive than one with a high grass-to-forb ratio. Seeding rates for different plant functional groups that are too high or low may also adversely affect ecological outcomes…First-year management may also play a role in cost-effective prairie reconstruction. Post-agricultural sites where restoration typically occurs are often quickly dominated by fast-growing annual weeds by the time sown prairie seeds begin germinating (Smith et al. 2010)… Williams and others (2007) showed that prairie seedlings sown into established warm-season grasses were reliant on high light conditions created by frequently mowing tall vegetation in order to survive in subsequent years…Our objective was to compare native plant establishment and cost effectiveness with and without first-year mowing for three different seed mixes that differed in grass to forb ratio and soil type customization. With knowledge of plant establishment, cost effectiveness, and mowing management outcomes, conservation practitioners will be better equipped to restore prairie efficiently and successfully." | 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: ". ..we used the ESTIMAP model to improve the results of the Lonsdorf model. For this, we included the effects of roads, railways, rivers, wetlands, lakes, altitude, climate, and ecosystem boundaries in the ESTIMAP modeling and compared the results with the Lonsdorf model. The results of the Lonsdorf model showed that the majority of Iran had a very low potential for providing pollination service and only three percent of the northern and western parts of Iran had high potential. However, the results of the ESTIMAP model showed that 16% of Iran had a high potential to provide pollination that covers most of the northern and southern parts of the country. The results of the ESTIMAP model for pollination mapping in Iran showed the Lonsdorf model of estimating pollination service can be improved through considering other relevant factors." | ABSTRACT: " EcoSim II uses results from the Ecopath procedure for trophic mass-balance analysis to define biomass dynamics models for predicting temporal change in exploited ecosystems. Key populations can be repre- sented in further detail by using delay-difference models to account for both biomass and numbers dynamics. A major problem revealed by linking the population and biomass dynamics models is in representation of population responses to changes in food supply; simple proportional growth and reproductive responses lead to unrealistic predic- tions of changes in mean body size with changes in fishing mortality. EcoSim II allows users to specify life history mechanisms to avoid such unrealistic predictions: animals may translate changes in feed- ing rate into changes in reproductive rather than growth rates, or they may translate changes in food availability into changes in foraging time that in turn affects predation risk. These options, along with model relationships for limits on prey availabil- ity caused by predation avoidance tactics, tend to cause strong compensatory responses in modeled populations. It is likely that such compensatory responses are responsible for our inability to find obvious correlations between interacting trophic components in fisheries time-series data. But Eco- sim II does not just predict strong compensatory responses: it also suggests that large piscivores may be vulnerable to delayed recruitment collapses caused by increases in prey species that are in turn competitors/predators of juvenile piscivores " | ABSTRACT: "The Watershed Management Optimization Support Tool (WMOST) is intended to be used as a screening tool as part of an integrated watershed management process such as that described in EPA’s watershed planning handbook (EPA 2008).1 The objective of WMOST is to serve as a public-domain, efficient, and user-friendly tool for local water resources managers and planners to screen a widerange of potential water resources management options across their watershed or jurisdiction for costeffectiveness as well as environmental and economic sustainability (Zoltay et al 2010). Examples of options that could be evaluated with the tool include projects related to stormwater, water supply, wastewater and water-related resources such as Low-Impact Development (LID) and land conservation. The tool is intended to aid in evaluating the environmental and economic costs, benefits, trade-offs and co-benefits of various management options. In addition, the tool is intended to facilitate the evaluation of low impact development (LID) and green infrastructure as alternative or complementary management options in projects proposed for State Revolving Funds (SRF). WMOST is a screening model that is spatially lumped with a daily or monthly time step. The model considers water flows but does not yet consider water quality. The optimization of management options is solved using linear programming. The target user group for WMOST consists of local water resources managers, including municipal water works superintendents and their consultants. This document includes a user guide and presentation of two case studies as examples of how to apply WMOST. Theoretical documentation is provided in a separate report (EPA/600/R-13/151). " |
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Specific Policy or Decision Context Cited
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None identified | None identified | Not reported | Not applicable | Land management, ecosystem management, response to EU 2020 Biodiversity Strategy | Not applicable | None identified | None identified | None identified | Future rock lobster fisheries management | Seed mix design and management practices for native plant restoration | None identified | None reported | None | Not applicable |
|
Biophysical Context
|
Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | No additional description provided | Agricultural landuse , 1st-10th order streams | Northern Spain; Bizkaia region | nearshore; <1.5 km offshore; <12 m depth | Spring-sown cereal croplands, where the bird chearry-oat aphid is a key aphid pest. The aphid colonizes the crop during late May and early June, depending on weather and location. The colonization phase is followed by a brief phase of rapid exponential population growth by wingless aphids, continuing until about the time of crop heading, in late June or early July. After heading, aphid populations usually decline rapidly in the crop due to decreased plant quality and migration to grasslands. The aphids are attacked by a complex of arthropod natural enemies, but parasitism is not important in the region and therefore not modelled here. | No additional description provided | submerged aquatic vegetation | barrier reef and fringing reef in nearshore coastal marine system | The soils underlying the study site are primarily poorly drained Clyde clay loams, with a minor component of somewhat poorly drained Floyd loams in the northwest (NRCS 2016). Topographically, the study site is level, and slopes do not exceed 5% grade. Land use prior to this experiment was agricultural, with corn and soybeans consistently grown in rotation at the site. | Waterfront districts on south Lake Michigan and south lake Erie | None additional | None, Ocean ecosystems | None |
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EM Scenario Drivers
em.detail.scenarioDriverHelp
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No scenarios presented | No scenarios presented | No scenarios presented | Not applicable | No scenarios presented | Not applicable | No scenarios presented | Alternative future land management strategies (status quo, managed growth, unmanaged growth) | No scenarios presented | Reef type, Sea level increase, storm conditions, seagrass conditions, coral conditions, vegetation types and conditions | No scenarios presented | N/A | N/A | N/A | None |
EM Relationship to Other EMs or Applications
|
EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-91 | EM-93 | EM-193 | EM-260 | EM-303 |
EM-369
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EM-414 |
EM-542
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EM-719
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EM-890 | EM-941 | EM-964 | EM-1011 |
|
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method Only | Method Only |
|
New or Pre-existing EM?
em.detail.newOrExistHelp
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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 | 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 |
Related EMs (for example, other versions or derivations of this EM) described in ESML
em.detail.relatedEmHelp
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EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 | EM-91 | EM-93 | EM-193 | EM-260 | EM-303 |
EM-369
|
EM-414 |
EM-542
|
EM-719
|
EM-890 | EM-941 | EM-964 | EM-1011 |
|
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-260 | Doc-260 | Doc-269 | Doc-123 | Doc-154 | Doc-155 | None | None | None |
Doc-314 | Doc-47 ?Comment:Doc 314 is a secondary source. It is a webpage guide intended to provide support for developing an application using ENVISION. |
None | None | Doc-395 | Doc-422 | Doc-432 | None | None |
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EM ID for related EM
em.detail.relatedEmEmIdHelp
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EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | None | None | None | None | EM-12 | EM-333 | None | None | EM-728 | EM-886 | EM-888 | EM-889 | EM-891 | EM-893 | EM-894 | EM-895 | EM-939 | EM-1055 | None |
EM Modeling Approach
EM Relationship to Time
|
EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-91 | EM-93 | EM-193 | EM-260 | EM-303 |
EM-369
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EM-414 |
EM-542
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EM-719
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EM-890 | EM-941 | EM-964 | EM-1011 |
|
EM Temporal Extent
em.detail.tempExtentHelp
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2007-2009 | 2007-2008 | 1987-1997 | 2000-2008 | 2000 - 2007 | 2006-2007 | 2009 | 2000-2060 | 2010 - 2012 | 2005-2013 | 2015-2017 | 2022 | 2020 | Not applicable | Not applicable |
|
EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent |
|
EM Time Reference (Future/Past)
em.detail.futurePastHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | both |
Not applicable ?Comment:method description |
|
EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete | discrete | Not applicable | Not applicable |
discrete ?Comment:Modeller dependent |
discrete |
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EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | 1 | 1 | Not applicable | Not applicable | 1 | 1 |
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EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Second | Year | Not applicable | Not applicable | Day | Month |
EM Spatial Extent
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EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-91 | EM-93 | EM-193 | EM-260 | EM-303 |
EM-369
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EM-414 |
EM-542
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EM-719
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EM-890 | EM-941 | EM-964 | EM-1011 |
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Bounding Type
em.detail.boundingTypeHelp
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Physiographic or Ecological | Physiographic or Ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Geopolitical | Physiographic or Ecological | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical | Other | Geopolitical | Geopolitical | Other | Not applicable |
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Spatial Extent Name
em.detail.extentNameHelp
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Central French Alps | Central French Alps | Upper Mississippi River basin; St. Croix River Watershed | Upper Mississippi, Ohio and Missouri River sub-basins | Bilbao Metropolitan Greenbelt | St.Croix, U.S. Virgin Islands | Uppland province | Puget Sound watershed | St. Louis River Estuary | Coast of Belize | Iowa State University Northeast Research and Demonstration Farm | Great Lakes waterfront | Iran | Not applicable | Not applicable |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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10-100 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 10-100 km^2 | 10,000-100,000 km^2 | 10,000-100,000 km^2 | 10-100 km^2 | 100-1000 km^2 | <1 ha | 1000-10,000 km^2. | >1,000,000 km^2 | Not applicable | Not applicable |
Spatial Distribution of Computations
|
EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-91 | EM-93 | EM-193 | EM-260 | EM-303 |
EM-369
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EM-414 |
EM-542
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EM-719
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EM-890 | EM-941 | EM-964 | EM-1011 |
|
EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially 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) ?Comment:BH: Each individual transect?s data was parceled into location reports, and that each report?s ?quadrat? area was dependent upon the angle of the hydroacoustic sampling beam. The spatial grain is 0.07 m^2, 0.20 m^2 and 0.70 m^2 for depths of 1 meter, 2 meters and 3 meters, respectively. |
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) ?Comment:Varies by inputs, but results are for areas of country |
spatially lumped (in all cases) | spatially lumped (in all cases) |
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Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | area, for pixel or radial feature | NHDplus v1 | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | Irregular | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | Not applicable | Not applicable |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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20 m x 20 m | 20 m x 20 m | NHDplus v1 | 1 km | 2 m x 2 m | Not applicable | 25 m x 25 m | Varies | 0.07 m^2 to 0.70 m^2 | 1 meter | 20 ft x 28 ft | Not applicable | ha^2 | Not applicable | Not applicable |
EM Structure and Computation Approach
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EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-91 | EM-93 | EM-193 | EM-260 | EM-303 |
EM-369
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EM-414 |
EM-542
|
EM-719
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EM-890 | EM-941 | EM-964 | EM-1011 |
|
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Numeric |
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EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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Model Checking Procedures Used
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EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-91 | EM-93 | EM-193 | EM-260 | EM-303 |
EM-369
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EM-414 |
EM-542
|
EM-719
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EM-890 | EM-941 | EM-964 | EM-1011 |
|
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | Yes | No | No | Yes | No | Unclear | Yes | No | Not applicable | No | No | No | Not applicable |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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Yes | Yes | Yes | No | No | Yes | No | Not applicable | Yes | No | Not applicable | No | No | No | Not applicable |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None |
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None | None |
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None | None | None | None | None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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Yes | No | No | No | Yes | No | Yes | Not applicable | Yes |
No ?Comment:Used the SWAN model (see below for referenece) with Generation 1 or 2 wind-wave formulations to validate the wave development portion of the model. Booij N, Ris RC, Holthuijsen LH. A third-generation wave model for coastal regions 1. Model description and validation. J Geophys Res. American Geophysical Union; 1999;104: 7649?7666. |
No | No | No | Not applicable | Not applicable |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | Yes | No | Yes | No | Not applicable | No | No | Not applicable | No | No | Not applicable | Not applicable |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No |
No ?Comment:Some model coefficients serve, by their magnitude, to indicate the proportional impact on the final result of variation in the parameters they modify. |
Unclear | No | No |
Yes ?Comment:AUTHOR'S NOTE: "Varying aphid fecundity, overall predator abundances and attack rates affected the biological control effect, but had little influence on the relative differences between landscapes with high and low levels of biological control. The model predictions were more sensitive to changing the predators' landscape relations, but, with few exceptions, did not dramatically alter the overall patterns generated by the model." |
Not applicable | No | No | Not applicable | Yes | No | Not applicable | Not applicable |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
em.detail.relationToSpaceTerrestrialHelp
?
| EM-65 | EM-71 | EM-91 | EM-93 | EM-193 | EM-260 | EM-303 |
EM-369
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EM-414 |
EM-542
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EM-719
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EM-890 | EM-941 | EM-964 | EM-1011 |
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None |
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Comment:Model for Iran - no form preset id for country |
None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
?
| EM-65 | EM-71 | EM-91 | EM-93 | EM-193 | EM-260 | EM-303 |
EM-369
|
EM-414 |
EM-542
|
EM-719
|
EM-890 | EM-941 | EM-964 | EM-1011 |
| None | None | None | None | None |
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None |
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None |
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None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-91 | EM-93 | EM-193 | EM-260 | EM-303 |
EM-369
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EM-414 |
EM-542
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EM-719
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EM-890 | EM-941 | EM-964 | EM-1011 |
|
Centroid Latitude
em.detail.ddLatHelp
?
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45.05 | 45.05 | 42.5 | 36.98 | 43.25 | 17.75 | 59.52 | 47.58 | 46.72 | 18.63 | 42.93 | 42.26 | 32.29 | Not applicable | Not applicable |
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Centroid Longitude
em.detail.ddLongHelp
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6.4 | 6.4 | -90.63 | -89.13 | -2.92 | -64.75 | 17.9 | -122.32 | -96.13 | -88.22 | -92.57 | -87.84 | 53.68 | Not applicable | Not applicable |
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Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | NAD83 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | Not applicable |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Provided | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Not applicable | Not applicable |
Environments and Scales Modeled
|
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 | EM-91 | EM-93 | EM-193 | EM-260 | EM-303 |
EM-369
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EM-414 |
EM-542
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EM-719
|
EM-890 | EM-941 | EM-964 | EM-1011 |
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Rivers and Streams | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Near Coastal Marine and Estuarine | Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Open Ocean and Seas | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | None | Not applicable | none | stony coral reef | Spring-sown cereal croplands and surrounding grassland and non-arable land | Pacific NW US region, coastal to montane, urban to rural | Freshwater estuarine system | coral reefs | Research farm in historic grassland | Lake Michigan & Lake Erie waterfront | terrestrial land types | Pelagic | watershed |
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EM Ecological Scale
em.detail.ecoScaleHelp
?
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Not applicable | Not applicable | Ecosystem | 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 corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
em.detail.nameOfOrgsOrGroupsHelp
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EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 | EM-91 | EM-93 | EM-193 | EM-260 | EM-303 |
EM-369
|
EM-414 |
EM-542
|
EM-719
|
EM-890 | EM-941 | EM-964 | EM-1011 |
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Community | Community | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Individual or population, within a species | Not applicable | Not applicable | Guild or Assemblage | Community | Not applicable | Not applicable |
Other (Comment) ?Comment:Varied levels of taxonomic order |
Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
?
| EM-65 | EM-71 | EM-91 | EM-93 | EM-193 | EM-260 | EM-303 |
EM-369
|
EM-414 |
EM-542
|
EM-719
|
EM-890 | EM-941 | EM-964 | EM-1011 |
| None Available | None Available | 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 |
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.detail.cicesHelp
?
| EM-65 | EM-71 | EM-91 | EM-93 | EM-193 | EM-260 | EM-303 |
EM-369
|
EM-414 |
EM-542
|
EM-719
|
EM-890 | EM-941 | EM-964 | EM-1011 |
| None | None |
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None |
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None |
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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>
fegs1Help
?
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
?
| EM-65 | EM-71 | EM-91 | EM-93 | EM-193 | EM-260 | EM-303 |
EM-369
|
EM-414 |
EM-542
|
EM-719
|
EM-890 | EM-941 | EM-964 | EM-1011 |
|
None | None |
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None | None |
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None |
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None |