EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
EM Identity and Description
EM ID
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EM-65 | EM-71 | EM-79 | EM-91 | EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-605 ![]() |
EM-618 ![]() |
EM-628 | EM-647 |
EM-728 ![]() |
EM-779 ![]() |
EM-836 | EM-904 | EM-941 | EM-947 |
EM-1013 ![]() |
EM-1017 |
EM Short Name
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Green biomass production, Central French Alps | Community flowering date, Central French Alps | Divergence in flowering date, Central French Alps | RHyME2, Upper Mississippi River basin, USA | Cultural ecosystem services, Bilbao, Spain | Coral taxa and land development, St.Croix, VI, USA | InVEST (v1.004) sediment retention, Indonesia | EPA H2O, Tampa Bay Region, FL,USA | SAV occurrence, St. Louis River, MN/WI, USA | Reef snorkeling opportunity, St. Croix, USVI | Value of finfish, St. Croix, USVI | VELMA v2.0, Ohio, USA | WaterWorld v2, Santa Basin, Peru | SolVES, Bridger-Teton NF, WY | EcoAIM v.1.0 APG, MD | Seed mix and mowing in prairie reconstruction, USA | Arthropod flower preference, CA, USA | Bird abundance on restored landfills, UK | Drag coefficient Laminaria hyperborea | ESTIMAP - Pollination potential, Iran | C-GEM, Lousiana continental shelf, USA | CommunityViz, Albany county, Wyoming | N-SPECT, Sediment and runoff, Isfahan, Iran |
EM Full Name
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Green biomass production, Central French Alps | Community weighted mean flowering date, Central French Alps | Functional divergence in flowering date, Central French Alps | RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | Cultural ecosystem services, Bilbao, Spain | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs v1.004) sediment retention, Sumatra, Indonesia | EPA H2O, Tampa Bay Region, FL, USA | Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA | Relative snorkeling opportunity (in reef), St. Croix, USVI | Relative value of finfish (on reef), St. Croix, USVI | Visualizing Ecosystems for Land Management Assessments (VELMA) v2.0, Shayler Crossing watershed, Ohio, USA | WaterWorld v2, Santa Basin, Peru | SolVES, Social Values for Ecosystem Services, Bridger-Teton National Forest, WY | EcoAIM v.1.0, Aberdeen Proving Ground, MD | Seed mix design and first year management in prairie reconstruction, IA, USA | Arthropod flower type preference, California, USA | Bird abundance on restored landfills compared to paired reference sites, East Midlands, UK | Drag coefficient Laminaria hyperborea | ESTIMAP - Pollination potential, Iran | Carbon Generic Estuary Model (C-GEM), Lousiana Continental Shelf, USA | Wyoming Community Viz TM Partnership Phase I Pilot: Aquifer Protection and Community Viz TM in Albany County, Wyoming. | Investigation of runoff and sediment yield using N-SPECT model in Pelasjan (Eskandari), Isfahan, Iran |
EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | US EPA |
None ?Comment:EU Mapping Studies |
US EPA | InVEST | US EPA | US EPA | US EPA | US EPA | US EPA | None | None | None | None | None | None | None | None | US EPA | None | None |
EM Source Document ID
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260 | 260 | 260 | 123 | 191 | 96 | 309 | 321 | 330 | 335 | 335 |
359 ?Comment:Document #366 is a supporting document for this EM. McKane et al. 2014, VELMA Version 2.0 User Manual and Technical Documentation. |
368 | 369 | 374 | 395 | 399 | 406 | 424 | 434 | 441 |
479 ?Comment:Published as a report by the University of Wyoming, but no record of peer review. |
480 |
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. | 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. | Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Bhagabati, N. K., Ricketts, T., Sulistyawan, T. B. S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A., Tallis, H., and Wolney, S. | Ranade, P., Soter, G., Russell, M., Harvey, J., and K. Murphy | Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Hoghooghi, N., H. E. Golden, B. P. Bledsoe, B. L. Barnhart, A. F. Brookes, K. S. Djang, J. J. Halama, R. B. McKane, C. T. Nietch, and P. P. Pettus | Van Soesbergen, A. and M. Mulligan | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | Booth, P., Law, S. , Ma, J. Turnley, J., and J.W. Boyd | Meissen, J. C., A. J. Glidden, M. E. Sherrard, K. J. Elgersma, and L. L. Jackson | Lundin, O., Ward, K.L., and N.M. Williams | Rahman, M. L., S. Tarrant, D. McCollin, and J. Ollerton | Mendez, F. J. and I. J. Losada | Rahimi, E., Barghjelveh, S., and P. Dong | Jarvis, B.M., Lehrter, J.C., Lowe, L.L., Hagy, J.D., Wan, Y., Murrell, M.C., Ko, D.S., Penta, B., and R.W. Gould | Lieske, S. N., Mullen, S., Knapp, M., & Hamerlinck, J. D. | Khalili, S., Jamali, A.A., Hasanzadeh, M. and Morovvati, A., |
Document Year
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2011 | 2011 | 2011 | 2013 | 2013 | 2011 | 2014 | 2015 | 2013 | 2014 | 2014 | 2018 | 2018 | 2014 | 2014 | 2019 | 2018 | 2011 | 2004 | 2020 | 2020 | 2003 | 2015 |
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 | 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 | 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 | Ecosystem services reinforce Sumatran tiger conservation in land use plans | EPA H20 User Manual | Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary | 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 | Cumulative effects of low impact development on watershed hydrology in a mixed land-cover system | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | Implementation of EcoAIM - A Multi-Objective Decision Support Tool for Ecosystem Services at Department of Defense Installations | Seed mix design and first year management influence multifunctionality and cost-effectiveness in prairie reconstruction | Indentifying native plants for coordinated hanbitat manegement of arthroppod pollinators, herbivores and natural enemies | The conservation value of restored landfill sites in the East Midlands, UK for supporting bird communities in the East Midlands, UK for supporting bird communities | An empirical model to estimate the propagation of random breaking and nonbreaking waves over vegetation fields | Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran) | Modeling spatiotemporal patterns of ecosystem metabolism and organic carbon dynamics affecting hypoxia on the Louisiana continental shelf | Wyoming Community Viz TM Partnership Phase I Pilot: Aquifer Protection and Community Viz TM in Albany County, Wyoming | Investigation of runoff and sediment yield using N-SPECT model in Pelasjan (Eskandari), Isfahan, Iran. |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript |
EM ID
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EM-65 | EM-71 | EM-79 | EM-91 | EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-605 ![]() |
EM-618 ![]() |
EM-628 | EM-647 |
EM-728 ![]() |
EM-779 ![]() |
EM-836 | EM-904 | EM-941 | EM-947 |
EM-1013 ![]() |
EM-1017 |
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | http://www.epa.gov/ged/tbes/EPAH2O | Not applicable | Not applicable | Not applicable | https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | www.policysupport.org/waterworld | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://communityviz.com/ | https://coast.noaa.gov/digitalcoast/tools/qnspect.html | |
Contact Name
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Sandra Lavorel | Sandra Lavorel | Sandra Lavorel | Liem Tran | Izaskun Casado-Arzuaga | Leah Oliver | Nirmal K. Bhagabati | Marc J. Russell, Ph.D. | Ted R. Angradi | Susan H. Yee | Susan H. Yee | Heather Golden | Arnout van Soesbergen | Benson Sherrouse | Pieter Booth | Justin Meissen | Ola Lundin | Lutfor Rahman | F. J. Mendez | Ehsan Rahini | Brandon Jarvis | Scott Lieske | Ali Akbar Jamali |
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 | 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 | 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 | The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | USEPA GED, One Sabine Island Dr., Gulf Breeze, FL 32561 | 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 | 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 | National Exposure Research Laboratory, Office of Research and Development, US EPA, Cincinnati, OH 45268, USA | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | Exponent Inc., Bellevue WA | Tallgrass Prairie Center, 2412 West 27th Street, Cedar Falls, IA 50614-0294, USA | Department of Ecology, Swedish Univ. of Agricultural Sciences, Uppsala, Sweden | Landscape and Biodiversity Research Group, School of Science and Technology, The University of Northampton, Avenue Campus, Northampton NN2 6JD, UK | Not reported | Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran | 1US EPA, Office of Research and Development, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA Office of Research and Development, U.S. EPA, Gulf Breeze, FL, USA | Department of Agricultural & Applied Economics University of Wyoming, Laramie WY 82071 | Department of Watershed MGT, Maybod Branch, Islamic Azad University, Maybod, Iran |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | ltran1@utk.edu | izaskun.casado@ehu.es | leah.oliver@epa.gov | nirmal.bhagabati@wwfus.org | russell.marc@epa.gov | angradi.theodore@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | Golden.Heather@epa.gov | arnout.van_soesbergen@kcl.ac.uk | bcsherrouse@usgs.gov | pbooth@ramboll.com | justin.meissen@uni.edu | ola.lundin@slu.se | lutfor.rahman@northampton.ac.uk | mendezf@unican.es | ehsanrahimi666@gmail.com | jarvis.brandon@epa.gov | lieske@uwyo.edu | jamaliaa@maybodiau.ac.ir |
EM ID
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EM-65 | EM-71 | EM-79 | EM-91 | EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-605 ![]() |
EM-618 ![]() |
EM-628 | EM-647 |
EM-728 ![]() |
EM-779 ![]() |
EM-836 | EM-904 | EM-941 | EM-947 |
EM-1013 ![]() |
EM-1017 |
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: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Functional divergence of flowering date 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 "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) | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "...Here we use simple spatial analyses on readily available datasets to compare the distribution of five ecosystem services with tiger habitat in central Sumatra. We assessed services and habitat in 2008 and the changes in these variables under two future scenarios: a conservation-friendly Green Vision, and a Spatial Plan developed by the Indonesian government..." AUTHOR'S DESCRIPTION: "We used a modeling tool, InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004; Tallis et al., 2010), to map and quantify tiger habitat quality and five ecosystem services. InVEST maps ecosystem services and the quality of species habitat as production functions of LULC using simple biophysical models. Models were parameterized using data from regional agencies, literature surveys, global databases, site visits and prior field experience (Table 1)... The sediment retention model is based on the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978). It estimates erosion as ton y^-1 of sediment load, based on the energetic ability of rainfall to move soil, the erodibility of a given soil type, slope, erosion protection provided by vegetated LULC, and land management practices. The model routes sediment originating on each land parcel along its flow path, with vegetated parcels retaining a fraction of sediment with varying efficiencies, and exporting the remainder downstream. ...Although InVEST reports ecosystem services in biophysical units, its simple models are best suited to understanding broad patterns of spatial variation (Tallis and Polasky, 2011), rather than for precise quantification. Additionally, we lacked field measurements against which to calibrate our outputs. Therefore, we focused on relative spatial distribution across the landscape, and relative change to scenarios." | AUTHORS DESCRIPTION: "EPA H2O is a GIS based demonstration tool for assessing ecosystem goods and services (EGS). It was developed as a preliminary assessment tool in support of research being conducted in the Tampa Bay watershed. It provides information, data, approaches and guidance that communities can use to examine alternative land use scenarios in the context of nature’s benefits to the human community. . . EPA H2O allows users for the Tampa Bay estuary and its watershed to: • Gain a greater understanding of the significance of EGS, • Explore the spatial distribution of EGS and other ecosystem features, • Obtain map and summary statistics of EGS production's potential value, • Analyze and compare potential impacts from predicted development scenarios or user specified changes in land use patterns on EGS production's potential value EPA H2O is designed for analyzing data at neighborhood to regional scales.. . The tool is transportable to other locations if the required data are available. . . . | 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." | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…Synthesis of scientific literature and expert opinion can be used to estimate the relative potential for recreational opportunities across different benthic habitat types (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to recreational opportunities as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative recreational opportunity j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A.palmata, Montastraea reef, patch reef, and dense or sparse gorgonians), and Mij is the magnitude associated with each habitat for a given metric j: snorkeling opportunity" | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(5) value of finfish," | ABSTRACT: "Low Impact Development (LID) is an alternative to conventional urban stormwater management practices, which aims at mitigating the impacts of urbanization on water quantity and quality. Plot and local scale studies provide evidence of LID effectiveness; however, little is known about the overall watershed scale influence of LID practices. This is particularly true in watersheds with a land cover that is more diverse than that of urban or suburban classifications alone. We address this watershed-scale gap by assessing the effects of three common LID practices (rain gardens, permeable pavement, and riparian buffers) on the hydrology of a 0.94 km2 mixed land cover watershed. We used a spatially-explicit ecohydrological model, called Visualizing Ecosystems for Land Management Assessments (VELMA), to compare changes in watershed hydrologic responses before and after the implementation of LID practices. For the LID scenarios, we examined different spatial configurations, using 25%, 50%, 75% and 100% implementation extents, to convert sidewalks into rain gardens, and parking lots and driveways into permeable pavement. We further applied 20 m and 40 m riparian buffers along streams that were adjacent to agricultural land cover…" AUTHOR'S DESCRIPTION: "VELMA’s modeling domain is a three-dimensional matrix that includes information regarding surface topography, land use, and four soil layers. VELMA uses a distributed soil column framework to model the lateral and vertical movement of water and nutrients through the four soil layers. A soil water balance is solved for each layer. The soil column model is placed within a watershed framework to create a spatially distributed model applicable to watersheds (Figure 2, shown here with LID practices). Adjacent soil columns interact through down-gradient water transport. Water entering each pixel (via precipitation or flow from an adjacent pixel) can either first infiltrate into the implemented LID and the top soil layer, and then to the downslope pixel, or continue its downslope movement as the lateral surface flow. Surface and subsurface lateral flow are routed using a multiple flow direction method, as described in Abdelnour et al. [21]. A detailed description of the processes and equations can be found in McKane et al. [32], Abdelnour et al. [21], Abdelnour et al. [40]." | ABSTRACT: "Water resources in the Santa basin in the Peruvian Andes are increasingly under pressure from climate change and population increases. Impacts of temperature-driven glacier retreat on stream flow are better studied than those from precipitation changes, yet present and future water resources are mostly dependent on precipitation which is more difficult to predict with climate models. This study combines a broad range of projections from climate models with a hydrological model (WaterWorld), showing a general trend towards an increase in water availability due to precipitation increases over the basin. However, high uncertainties in these projections necessitate the need for basin-wide policies aimed at increased adaptability." AUTHOR'S DESCRIPTION: "WaterWorld is a fully distributed, process-based hydrological model that utilises remotely sensed and globally available datasets to support hydrological analysis and decision-making at national and local scales globally, with a particular focus on un-gauged and/or data-poor environments, which makes it highly suited to this study. The model (version 2) currently runs on either 10 degree tiles, large river basins or countries at 1-km2 resolution or 1 degree tiles at 1-ha resolution utilising different datasets. It simulates a hydrological baseline as a mean for the period 1950-2000 and can be used to calculate the hydrological impact of scenarios of climate change, land use change, land management options, impacts of extractives (oil & gas and mining) and impacts of changes in population and demography as well as combinations of these. The model is ‘self parameterising’ (Mulligan, 2013a) in the sense that all data required for model application anywhere in the world is provided with the model, removing a key barrier to model application. However, if users have better data than those provided, it is possible to upload these to WaterWorld as GIS files and use them instead. Results can be viewed visually within the web browser or downloaded as GIS maps. The model’s equations and processes are described in more detail in Mulligan and Burke (2005) and Mulligan (2013b). The model parameters are not routinely calibrated to observed flows as it is designed for hydrological scenario analysis in which the physical basis of its parameters must be retained and the model is also often used in un-gauged basins. Calibration is inappropriate under these circumstances (Sivapalan et al., 2003). The freely available nature of the model means that anyone can apply it and replicate the results shown here. WaterWorld’s (V2) snow and ice module is capable of simulating the processes of melt water production, snow fall and snow pack, making this version highly suited to the current application. The model component is based on a full energy-balance for snow accumulation and melting based on Walter et al., (2005) with input data provided globally by the SimTerra database (Mulligan, 2011) upon which the model r | [ABSTRACT: " "Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders.With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems. | [ABSTRACT: "This report describes the demonstration of the EcoAIM decision support framework and GIS-based tool. EcoAIM identifies and quantifies the ecosystem services provided by the natural resources at the Aberdeen Proving Ground (APG). A structured stakeholder process determined the mission and non-mission priorities at the site, elicited the natural resource management decision process, identified the stakeholders and their roles, and determine the ecosystem services of priority that impact missions and vice versa. The EcoAIM tool was customized to quantify in a geospatial context, five ecosystem services – vista aesthetics, landscape aesthetics, recreational opportunities, habitat provisioning for biodiversity and nutrient sequestration. The demonstration included a Baseline conditions quantification of ecosystem services and the effects of a land use change in the Enhanced Use Lease parcel in cantonment area (Scenario 1). Biodiversity results ranged widely and average scores decreased by 10% after Scenario 1. Landscape aesthetics scores increased by 10% after Scenario 1. Final scores did not change for recreation or nutrient sequestration because scores were outside the boundaries of the baseline condition. User feedback after the demonstration indicated positive reviews of EcoAIM as being useful and usable for land use decisions and particularly for use as a communication tool. " | ABSTRACT: "Agricultural intensification continues to diminish many ecosystem services in the North American Corn Belt. Conservation programs may be able to combat these losses more efficiently by developing initiatives that attempt to balance multiple ecological benefits. In this study, we examine how seed mix design and first year management influence three ecosystem services commonly provided by tallgrass prairie reconstructions (erosion control, weed resistance, and pollinator resources). We established research plots with three seed mixes, with and without first year mowing. The grass-dominated “Economy” mix had 21 species and a 3:1 grass-to-forb seeding ratio. The forb-dominated “Pollinator”mix had 38 species and a 1:3 grass-to-forb seeding ratio. The grass:forb balanced “Diversity” mix, which was designed to resemble regional prairie remnants, had 71 species and a 1:1 grass-to-forb ratio. To assess ecosystem services, we measured native stem density, cover, inflorescence production, and floral richness from 2015 to 2018. The Economy mix had high native cover and stem density, but produced few inflorescences and had low floral richness. The Pollinator mix had high inflorescence production and floral richness, but also had high bare ground and weed cover. The Diversity mix had high inflorescence production and floral richness (comparable to the Pollinator mix) and high native cover and stem density (comparable to the Economy mix). First year mowing accelerated native plant establishment and inflorescence production, enhancing the provisioning of ecosystem services during the early stages of a reconstruction. Our results indicate that prairie reconstructions with thoughtfully designed seed mixes can effectively address multiple conservation challenges." | ABSTRACT: " Plant species differed in attractiveness for each arthropod functional group. Floral area of the focal plant species positively affected honeybee, predator, and parasitic wasp attractiveness. Later bloom period was associated with lower numbers of parasitic wasps. Flower type (actinomorphic, composite, or zygomorphic) predicted attractiveness for honeybees, which preferred actinomorphic over composite flowers and for parasitic wasps, which preferred composite flowers over actinomorphic flowers. 4. Across plant species, herbivore, predator, and parasitic wasp abundances were positively correlated, and honeybee abundance correlated negatively to herbivore abundance. 5. Synthesis and applications. We use data from our common garden experiment to inform evidence-based selection of plants that support pollinators and natural enemies without enhancing potential pests. We recommend selecting plant species with a high floral area per ground area unit, as this metric predicts the abundances of several groups of beneficial arthropods. Multiple correlations between functionally important arthropod groups across plant species stress the importance of a multifunctional approach to arthropod habitat management. " Changes in arthropod abundance were estimated for flower type (entered as separate runs); Actinomorphic, Composite, Zygomorphic. 43 plant species evaluated included Amsinckia intermedia, Calandrinia menziesii, Nemophila maculata, Nemophila menziesii, Phacelia ciliata, Achillea millefolium, Collinsia heterophylla, Fagopyrum esculentum, Lasthenia fremontii, Lasthenia glabrata, Limnanthes alba, Lupinus microcarpus densiflorus, Lupinus succelentus, Phacelia californica, Phacelia campanularia, Phacelia tanacetifolia, Salvia columbariae, Sphaeralcea ambigua, Trifolium fucatum, Trifolium gracilentum, Antirrhinum conutum, Clarkia purpurea, Clarkia unguiculata, Clarkia williamsonii, Eriophyllum lanatum, Eschscholzia californica, Monardella villosa, Scrophularia californica, Asclepia eriocarpa, Asclepia fascicularis, Camissoniopsis Cheiranthifolia, Eriogonum fasciculatum, Gilia capitata, Grindelia camporum, Helianthus annuus, Lupinus formosus, Malacothrix saxatilis, Oenothera elata, Helianthus bolanderi, Helianthus californicus, Madia elegans, Trichostema lanceolatum, Heterotheca grandiflora." | ABSTRACT: "There has been a rapid decline of grassland bird species in the UK over the last four decades. In order to stem declines in biodiversity such as this, mitigation in the form of newly created habitat and restoration of degraded habitats is advocated in the UK biodiversity action plan. One potential restored habitat that could support a number of bird species is re-created grassland on restored landfill sites. However, this potential largely remains unexplored. In this study, birds were counted using point sampling on nine restored landfill sites in the East Midlands region of the UK during 2007 and 2008. The effects of restoration were investigated by examining bird species composition, richness, and abundance in relation to habitat and landscape structure on the landfill sites in comparison to paired reference sites of existing wildlife value. Twelve bird species were found in total and species richness and abundance on restored landfill sites was found to be higher than that of reference sites. Restored landfill sites support both common grassland bird species and also UK Red List bird species such as skylark Alauda arvensis, grey partridge Perdix perdix, lapwing Vanellus vanellus, tree sparrow, Passer montanus, and starling Sturnus vulgaris. Size of the site, percentage of bare soil and amount of adjacent hedgerow were found to be the most influential habitat quality factors for the distribution of most bird species. Presence of open habitat and crop land in the surrounding landscape were also found to have an effect on bird species composition. Management of restored landfill sites should be targeted towards UK Red List bird species since such sites could potentially play a significant role in biodiversity action planning." AUTHOR'S DESCRIPTION: "Mean number of birds from multiple visits were used for data analysis. To analyse the data generalized linear models (GLMs) were constructed to compare local habitat and landscape parameters affecting different species, and to establish which habitat and landscape characteristics explained significant changes in the frequency of occurrence for each species. To ensure analyses focused on resident species, habitat associations were modelled for those seven bird species which were recorded at least three times in the surveys. The analysis was carried out with the software R (R Development Core Team 2003). Nonsignificant predictors (independent variables) were removed in a stepwise manner (least significant factor first). For distribution pattern of bird species, data were initially analysed using detrended correspondence analysis. Redundancy analysis (RDA) was performed on the same data using CANOCO for Windows version 4.0 (ter Braak and Smilauer 2002)." | ABSTRACT: "In this work, a model for wave transformation on vegetation fields is presented. The formulation includes wave damping and wave breaking over vegetation fields at variable depths. Based on a nonlinear formulation of the drag force, either the transformation of monochromatic waves or irregular waves can be modelled considering geometric and physical characteristics of the vegetation field. The model depends on a single parameter similar to the drag coefficient, which is parameterized as a function of the local Keulegan–Carpenter number for a specific type of plant. Given this parameterization, determined with laboratory experiments for each plant type, the model is able to reproduce the root-mean-square wave height transformation observed in experimental data with reasonable accuracy." AUTHOR'S DESCRIPTION: "Therefore, a relation between C˜D and some nondimensional flow parameters is desirable to characterize hydrodynamically the L. hyperborea model plants for predictable purposes." | 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: "The hypoxic zone on the Louisiana Continental Shelf (LCS) forms each summer due to nutrient‐enhanced primary production and seasonal stratification associated with freshwater discharges from the Mississippi/Atchafalaya River Basin (MARB). Recent field studies have identified highly productive shallow nearshore waters as an important component of shelf‐wide carbon production contributing to hypoxia formation. This study applied a three‐dimensional hydrodynamic‐biogeochemical model named CGEM (Coastal Generalized Ecosystem Model) to quantify the spatial and temporal patterns of hypoxia, carbon production, respiration, and transport between nearshore and middle shelf regions where hypoxia is most prevalent. We first demonstrate that our simulations reproduced spatial and temporal patterns of carbon production, respiration, and bottom‐water oxygen gradients compared to field observations. We used multiyear simulations to quantify transport of articulate organic carbon (POC) from nearshore areas where riverine organic matter and phytoplankton carbon production are greatest. The spatial displacement of carbon production and respiration in our simulations was created by westward and offshore POC flux via phytoplankton carbon flux in the surface layer and POC flux in the bottom layer, supporting heterotrophic respiration on the middle shelf where hypoxia is frequently observed. These results support existing studies suggesting the importance of offshore carbon flux to hypoxia formation, particularly on the west shelf where hypoxic conditions are most variable. " | The Wyoming Community VizTM Partnership was established in 2001 to promote the use of geographic information system-based planning support systems and related decision support technologies in community land-use planning and economic development activities in the State of Wyoming. Partnership members include several state agencies, local governments and several nongovernment organizations. Partnership coordination is provided by the Wyoming Rural Development Council. Research and technical support is coordinated by the Wyoming Geographic Information Science Center’s Spatial Decision Support System Research Program at the University of Wyoming. In June 2002, the Partnership initiated a three-phase plan to promote Community VizTM based planning support systems in Wyoming. Phase I of the Partnership plan was a “proof of concept” pilot project set in Albany County in southeastern Wyoming. The goal of the project was to demonstrate the application of Community VizTM to a Wyoming-specific issue (in this case, aquifer protection) and to determine potential challenges for broader adoption in terms of data requirements, computing infrastructure and technological expertise. The results of the Phase I pilot project are detailed in this report. Efforts are currently underway to secure funding for Phase II of the plan, which expands the use of Community VizTM into four additional Wyoming communities. Specific Phase II objectives are to expand the type and number of issues addressed by Community VizTM and increase the use of Community VizTM in the planning process. As a part of Phase II the Partnership will create a technical assistance network aimed at assisting communities with the technical challenges in applying the software to their planning issues. The third phase will expand the program to more communities in the state, maintain the technical assistance network, and monitor the impact of Community VizTM on the planning process. | Identifying and quantifying the runoff and Sediment yield are the necessary measures in the issues of soil erosion in a watershed. Pelasjan watershed located in West of Isfahan and it is one of the sub basins of Zayanderud which is taken as the study area. In this study the amount of runoff and Sediment yield has been evaluated using the Nonpoint-Source Pollution and Erosion Comparison Tools (N-SPECT) model which is an extension to ArcGIS software. The input layer maps in the GIS environment, including land use, the rain erosion, vegetation, soil erodibility, contour map and watershed boundary map were prepared. By entering the input data and running N-SPECT model, runoff and Sediment yield raster maps of the study area were obtained. To evaluate the model and data comparing, the values obtained from the model and the actual data values of runoff and Sediment yield were converted to the eigenvalues. Special amount of runoff from the model equals 1483 m3/ha/year and the actual runoff is equivalent to 1253 m3/ha/year for 21 water years ,from 1991 to 2012. From the values obtained by the model and the actual data it can be concluded that the model is sufficiently accurate for estimating runoff since the actual runoff value and the value obtained from the model are close to each other and statistically, there is no significant difference between them during this 21 water year. In relation to a Sediment yield, the amount obtained from the model was 7.8 ton/ha/year and the average amount of Sediment yield for 21 water years is 2.1 ton/ha/year, which by comparing with the values obtained for Sediment yield it can be concluded that the model overestimates about three times from the actual amount and there is a significant difference between the real data and data obtained by model so the model has not been very successful in Sediment yield estimating. One of the advantages of this model for estimating runoff and Sediment yield is point to point estimation of runoff and Sediment yield in output maps of the region. This model is particularly recommended for harsh and difficult access regions of the watershed. |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | Not reported | Land management, ecosystem management, response to EU 2020 Biodiversity Strategy | Not applicable | This analysis provided input to government-led spatial planning and strategic environmental assessments in the study area. This region contains some of the last remaining forest habitat of the critically endangered Sumatran tiger, Panthera tigris sumatrae. | None reported | None identified | None identified | None identified | None identified | None identified | None | None reported | None identified | None reported | None identified | None identified | None reported | None reported | None provided | None provided |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | No additional description provided | Northern Spain; Bizkaia region | nearshore; <1.5 km offshore; <12 m depth | Six watersheds in central Sumatra covering portions of Riau, Jambi and West Sumatra provinces. The Barisan mountain range comprises the western edge of the watersheds, while peat swamps predominate in the east. | Not applicable | submerged aquatic vegetation | No additional description provided | No additional description provided | The Shayler Crossing (SHC) watershed is a subwatershed of the East Fork Little Miami River Watershed in southwest Ohio, USA and falls within the Till Plains region of the Central Lowland physiographic province. The Till Plains region is a topographically young and extensive flat plain, with many areas remaining undissected by even the smallest stream. The bedrock is buried under a mantle of glacial drift 3–15 m thick. The Digital Elevation Model (DEM) has a maximum value of ~269 m (North American_1983 datum) within the watershed boundary (Figure 1). The soils are primarily the Avonburg and Rossmoyne series, with high silty clay loam content and poor to moderate infiltration. Average annual precipitation for the period, 1990 through 2011, was 1097.4 _ 173.5 mm. Average annual air temperature for the same period was 12 _C Mixed land cover suburban watershed. The primary land uses consist of 64.1% urban or developed area (including 37% lawn, 12% building, 6.5% street, 6.4% sidewalk, and 2.1% parking lot and driveway), 23% agriculture, and 13% deciduous forest. Total imperviousness covers approximately 27% of the watershed area. | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | Rocky mountain conifer forests | Chesapeake bay coastal plain, elev. 60ft. | The site, located at the Iowa State University Northeast Research and Demonstration Farm near Nashua, Iowa, is relatively level with slopes not exceeding a 5% grade. Soil composition is primarily poorly drained Clyde clay loams with a minor component of somewhat poorly drained Floyd loams. Sub-surface tile drains exist on site and are spaced approximately 18–24m apart. The land was used for corn and soybean production prior to site establishment in 2015. | Mediteranean climate | The study area covered mainly Northamptonshire and parts of Bedfordshire, Buckinghamshire and Warwickshire, ranging from 51o58’44.74” N to 52o26’42.18” N and 0o27’49.94” W to 1o19’57.67” W. This region has countryside of low, undulating hills separated by valleys and lies entirely within the great belt of scarplands formed by rocks of Jurassic age which stretch across England from Yorkshire to Dorset (Beaver 1943; Sutherland 1995; Wilson 1995). | No additional description provided | None additional | Louisiana coastal continental shelf | Groundwater recharge area, City of Laramie | Pelasjan watershed, Zagros mountain range |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Not applicable | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | Land Use, EGS algorithm values, | No scenarios presented | No scenarios presented | No scenarios presented | Three types of Low Impact Development (LID) practices (rain gardens, permeable pavements, forested riparian buffers) applied a different conversion levels. | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | N/A | N/A | Seed mix design | Arthropod groups | No scenarios presented | No scenarios presented | N/A | Coastal Shelf location | Current conditions | No scenarios presented |
EM ID
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EM-65 | EM-71 | EM-79 | EM-91 | EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-605 ![]() |
EM-618 ![]() |
EM-628 | EM-647 |
EM-728 ![]() |
EM-779 ![]() |
EM-836 | EM-904 | EM-941 | EM-947 |
EM-1013 ![]() |
EM-1017 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | 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 | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application | 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 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | Application of existing model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-65 | EM-71 | EM-79 | EM-91 | EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-605 ![]() |
EM-618 ![]() |
EM-628 | EM-647 |
EM-728 ![]() |
EM-779 ![]() |
EM-836 | EM-904 | EM-941 | EM-947 |
EM-1013 ![]() |
EM-1017 |
Document ID for related EM
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Doc-260 | Doc-260 | Doc-269 | Doc-260 | Doc-269 | Doc-123 | None | None | Doc-338 | None | None | None | None | Doc-13 | Doc-366 | None | None | None | Doc-394 | None | None | Doc-424 | Doc-432 | None | Doc-473 | Doc-473 |
EM ID for related EM
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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 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83 | None | None | None | EM-435 | None | None | None | None | EM-375 | EM-377 | EM-378 | EM-884 | EM-883 | EM-887 | None | EM-629 | EM-626 | None | EM-719 | None | EM-837 | EM-896 | EM-897 | EM-939 | None | EM-1003 | EM-1007 | EM-1003 |
EM Modeling Approach
EM ID
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EM-65 | EM-71 | EM-79 | EM-91 | EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-605 ![]() |
EM-618 ![]() |
EM-628 | EM-647 |
EM-728 ![]() |
EM-779 ![]() |
EM-836 | EM-904 | EM-941 | EM-947 |
EM-1013 ![]() |
EM-1017 |
EM Temporal Extent
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2007-2009 | 2007-2008 | 2007-2008 | 1987-1997 | 2000 - 2007 | 2006-2007 | 2008-2020 | Not applicable | 2010 - 2012 | 2006-2007, 2010 | 2006-2007, 2010 | Jan 1, 2009 to Dec 31, 2011 | 1950-2071 | 2004-2008 | 2014 | 2015-2018 | 2015-2016 | Not applicable | Not applicable | 2020 | 2003-2007 | 2000 | 1991-2012 |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | Not applicable | time-stationary | time-dependent | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | both | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable |
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 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable |
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 | Not applicable | Not applicable | Not applicable | Not applicable | Day | Month | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Season | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-79 | EM-91 | EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-605 ![]() |
EM-618 ![]() |
EM-628 | EM-647 |
EM-728 ![]() |
EM-779 ![]() |
EM-836 | EM-904 | EM-941 | EM-947 |
EM-1013 ![]() |
EM-1017 |
Bounding Type
em.detail.boundingTypeHelp
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Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Watershed/Catchment/HUC | Geopolitical | Physiographic or Ecological | Watershed/Catchment/HUC |
Geopolitical ?Comment:Extent was Tampa Bay area in example, but boundary can be geopolitical or watershed derived. |
Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Geopolitical | Geopolitical | Other | Point or points | Multiple unrelated locations (e.g., meta-analysis) | Not applicable | Geopolitical | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC |
Spatial Extent Name
em.detail.extentNameHelp
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Central French Alps | Central French Alps | Central French Alps | Upper Mississippi River basin; St. Croix River Watershed | Bilbao Metropolitan Greenbelt | St.Croix, U.S. Virgin Islands | central Sumatra | Tampa Bay region | St. Louis River Estuary | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Shayler Crossing watershed, a subwatershed of the East Fork Little Miami River Watershed | Santa Basin | National Park | Aberdeen Proving Ground | Iowa State University Northeast Research and Demonstration Farm near Nashua, Iowa | Harry Laidlaw Jr. Honey Bee Research facility | East Midland | Not applicable | Iran | Lousiana continental shelf | Laramie City's aquifer protection area | Pelasjan watershed |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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10-100 km^2 | 10-100 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | 10-100 km^2 | 100-1000 km^2 | 100-1000 km^2 | 10-100 ha | 10,000-100,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | <1 ha | <1 ha | 1000-10,000 km^2. | Not applicable | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 10-100 km^2 | 1000-10,000 km^2. |
EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-79 | EM-91 | EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-605 ![]() |
EM-618 ![]() |
EM-628 | EM-647 |
EM-728 ![]() |
EM-779 ![]() |
EM-836 | EM-904 | EM-941 | EM-947 |
EM-1013 ![]() |
EM-1017 |
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 distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:500m x 500m is also used for some computations. The evaluation does include some riparian buffers which are linear features along streams. |
spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
spatially distributed (in at least some cases) ?Comment:Varies by inputs, but results are for areas of country |
spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | NHDplus v1 | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable |
Spatial Grain Size
em.detail.spGrainSizeHelp
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20 m x 20 m | 20 m x 20 m | 20 m x 20 m | NHDplus v1 | 2 m x 2 m | Not applicable | 30 m x 30 m | 30m x 30m | 0.07 m^2 to 0.70 m^2 | 10 m x 10 m | 10 m x 10 m | 10m x 10m | 1 km2 | 30m2 | 100m x 100m | 6.1 m x 8.53 m | Not applicable | multiple unrelated sites | Not applicable | ha^2 | regions of shelf | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-79 | EM-91 | EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-605 ![]() |
EM-618 ![]() |
EM-628 | EM-647 |
EM-728 ![]() |
EM-779 ![]() |
EM-836 | EM-904 | EM-941 | EM-947 |
EM-1013 ![]() |
EM-1017 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Numeric | Numeric | Analytic | Numeric | Analytic | Analytic | Numeric | Numeric | Numeric | Analytic |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-79 | EM-91 | EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-605 ![]() |
EM-618 ![]() |
EM-628 | EM-647 |
EM-728 ![]() |
EM-779 ![]() |
EM-836 | EM-904 | EM-941 | EM-947 |
EM-1013 ![]() |
EM-1017 |
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | No | Yes | No | Yes | No | No | Yes | Yes | Yes | Yes | No | No |
No ?Comment:Nutrient sequestion submodel ( EPA's P8 model has been long used) |
Not applicable | Not applicable | Not applicable | Yes | No | Yes | Unclear | Unclear |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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Yes | Yes | Yes | Yes | No | Yes | No | No | Yes | No | No |
Yes ?Comment:Goodness of fit for calibrated (2009-2010) and observed streamflow. |
No | Yes | Not applicable | No | Not applicable | Not applicable | Not applicable | No | No | No | No |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None |
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None | None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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Yes | No | No | No | Yes | No | No | No | Yes | Yes | Yes | Yes | Yes | No | No | No | Not applicable | Not applicable | Unclear | No | Yes | Unclear | Unclear |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | No | Yes | No | No | No | No | No | No | No | No | No | No | No | Not applicable | No | No | No | Unclear | Unclear |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | 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. |
No | No | No | No | No | No | No | No | No | No |
Unclear ?Comment:Just cannot tell, but no mention of sensitivity was made. |
No | No | Not applicable | No | No | 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 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-65 | EM-71 | EM-79 | EM-91 | EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-605 ![]() |
EM-618 ![]() |
EM-628 | EM-647 |
EM-728 ![]() |
EM-779 ![]() |
EM-836 | EM-904 | EM-941 | EM-947 |
EM-1013 ![]() |
EM-1017 |
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None |
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None | None |
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None |
Comment:Model for Iran - no form preset id for country |
None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-65 | EM-71 | EM-79 | EM-91 | EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-605 ![]() |
EM-618 ![]() |
EM-628 | EM-647 |
EM-728 ![]() |
EM-779 ![]() |
EM-836 | EM-904 | EM-941 | EM-947 |
EM-1013 ![]() |
EM-1017 |
None | None | None | None | None |
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None | None | None |
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None | None | None | None | None | None | None |
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None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-79 | EM-91 | EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-605 ![]() |
EM-618 ![]() |
EM-628 | EM-647 |
EM-728 ![]() |
EM-779 ![]() |
EM-836 | EM-904 | EM-941 | EM-947 |
EM-1013 ![]() |
EM-1017 |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | 45.05 | 45.05 | 42.5 | 43.25 | 17.75 | 0 | 28.05 | 46.72 | 17.73 | 17.73 | 39.19 | -9.05 | 43.93 | 39.46 | 42.93 | 38.54 | 52.22 | Not applicable | 32.29 | 30.28 | 41.31 | 32.26 |
Centroid Longitude
em.detail.ddLongHelp
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6.4 | 6.4 | 6.4 | -90.63 | -2.92 | -64.75 | 102 | -82.52 | -96.13 | -64.77 | -64.77 | -84.29 | -77.81 | 110.24 | 76.12 | -92.57 | -121.79 | -0.91 | Not applicable | 53.68 | -88.39 | -105.46 | 50.22 |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | NAD83 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Provided | Provided | Estimated | Provided | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Provided | Provided | Estimated | Not applicable | Estimated | Estimated | Estimated | Provided |
EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-79 | EM-91 | EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-605 ![]() |
EM-618 ![]() |
EM-628 | EM-647 |
EM-728 ![]() |
EM-779 ![]() |
EM-836 | EM-904 | EM-941 | EM-947 |
EM-1013 ![]() |
EM-1017 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | 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 | 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 | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | 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 | Near Coastal Marine and Estuarine | Rivers and Streams | Ground Water | Forests | Agroecosystems | Created Greenspace | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Forests | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Forests | Created Greenspace | Grasslands | Scrubland/Shrubland | Agroecosystems | Grasslands | Agroecosystems | Created Greenspace | Grasslands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Subalpine terraces, grasslands, and meadows | None | none | stony coral reef | 104 land use land cover classes | All terestrial landcover and waterbodies | Freshwater estuarine system | Coral reefs | Coral reefs | Mixed land cover suburban watershed | tropical, coastal to montane | Montain forest | Coastal Plain | prairie/grassland reconstruction at demonstration farm site | Agricultural fields | restored landfills and conserved grasslands | Near Coastal Marine and Estuarine | terrestrial land types | Louisiana continental shelf | watershed | Desert mountains watershed |
EM Ecological Scale
em.detail.ecoScaleHelp
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Not applicable | Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Ecosystem | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale 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 |
Other or unclear (comment) ?Comment:Variable data was derived from multiple climate data stations distrubuted across the study area. The location and distribution of the data stations was not provided. |
Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-79 | EM-91 | EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-605 ![]() |
EM-618 ![]() |
EM-628 | EM-647 |
EM-728 ![]() |
EM-779 ![]() |
EM-836 | EM-904 | EM-941 | EM-947 |
EM-1013 ![]() |
EM-1017 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Community | Community | Not applicable | Not applicable | Guild or Assemblage | Community | Not applicable | Not applicable | Guild or Assemblage | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Not applicable | Community | Guild or Assemblage | Individual or population, within a species | Species | Not applicable | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-65 | EM-71 | EM-79 | EM-91 | EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-605 ![]() |
EM-618 ![]() |
EM-628 | EM-647 |
EM-728 ![]() |
EM-779 ![]() |
EM-836 | EM-904 | EM-941 | EM-947 |
EM-1013 ![]() |
EM-1017 |
None Available | None Available | None Available | None Available | None Available |
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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 | None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-65 | EM-71 | EM-79 | EM-91 | EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-605 ![]() |
EM-618 ![]() |
EM-628 | EM-647 |
EM-728 ![]() |
EM-779 ![]() |
EM-836 | EM-904 | EM-941 | EM-947 |
EM-1013 ![]() |
EM-1017 |
None | 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-65 | EM-71 | EM-79 | EM-91 | EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-605 ![]() |
EM-618 ![]() |
EM-628 | EM-647 |
EM-728 ![]() |
EM-779 ![]() |
EM-836 | EM-904 | EM-941 | EM-947 |
EM-1013 ![]() |
EM-1017 |
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None | None | None |
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None |
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None | None |
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None |
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None | None |
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None |