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
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EM ID
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EM-59 |
EM-63 | EM-66 | EM-70 | EM-88 | EM-92 | EM-320 |
EM-321 |
EM-439 |
EM-480 |
EM-630 | EM-658 | EM-712 |
EM-774 |
EM-788 |
EM-844 | EM-893 |
EM-897 |
EM-990 |
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EM Short Name
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EnviroAtlas-Air pollutant removal | EnviroAtlas - Natural biological nitrogen fixation | Litter biomass production, Central French Alps | Plant species diversity, Central French Alps | Area and hotspots of carbon storage, South Africa | Runoff potential of pesticides, Europe | Coastal protection, Europe | Erosion prevention by vegetation, Portel, Portugal | WaSSI, Conterminous USA | Yasso07 - Land use SOC dynamics, China | WaterWorld v2, Santa Basin, Peru | Polyscape, Wales | ESII Tool method | Plant-pollinator networks at reclaimed mine, USA | Wild bees over 26 yrs of restored prairie, IL, USA | Common yellowthroat abun, Piedmont region, USA | HWB indicator-ADI, Great Lakes, USA | Random wave transformation L. hyperborea field | Atlantis ecosystem economics submodel |
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EM Full Name
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US EPA EnviroAtlas - Pollutants (air) removed annually by tree cover; Example is shown for Durham NC and vicinity, USA | US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Litter biomass production, Central French Alps | Plant species diversity, Central French Alps | Area and hotspots of carbon storage, South Africa | Runoff potential of pesticides, Europe | Coastal protection, Europe | Soil erosion prevention provided by vegetation cover, Portel municipality, Portugal | Water Supply Stress Index, Conterminous USA | Yasso07 - Land use dynamics of Soil Organic Carbon in the Loess Plateau, China | WaterWorld v2, Santa Basin, Peru | Polyscape, Wales | ESII (Ecosystem Services Identification & Inventory) Tool method | Restoration of plant-pollinator networks at reclaimed strip mine, Ohio, USA | Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA | Common yellowthroat abundance, Piedmont ecoregion, USA | Human well being indicator- Area Deprivation Index (ADI) , Great Lakes waterfront, USA | Random wave transformation on Laminaria hyperboria field | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
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EM Source or Collection
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US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Eco model. |
US EPA | EnviroAtlas | EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | None | EU Biodiversity Action 5 | EU Biodiversity Action 5 |
USDA Forest Service ?Comment:While the user guide on which model entry is based has not been peer reviewed, several peer reviewed journal articles describing this USA HUC8 version of WaSSI have been published. |
None | None | None | None | None | None | None | US EPA | None | None |
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EM Source Document ID
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223 |
262 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
260 | 260 | 271 | 254 | 296 | 281 | 341 | 344 | 368 | 379 |
391 ?Comment:Website for online user support |
397 | 401 | 405 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
424 | 463 |
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Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | US EPA Office of Research and Development - National Exposure Research Laboratory | 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. | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Schriever, C. A., and Liess, M. | Liquete, C., Zulian, G., Delgado, I., Stips, A., and Maes, J. | Guerra, C.A., Pinto-Correia, T., Metzger, M.J. | Peter Caldwell, Ge Sun, Steve McNulty, Jennifer Moore Myers, Erika Cohen, Robert Herring, Erik Martinez | Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu | Van Soesbergen, A. and M. Mulligan | Jackson, B., T. Pagella, F. Sinclair, B. Orellana, A. Henshaw, B. Reynolds, N. Mcintyre, H. Wheater, and A. Eycott | EcoMetrix Solutions Group (ESG) | Cusser, S. and K. Goodell | Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree | Riffel, S., Scognamillo, D., and L. W. Burger | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | Mendez, F. J. and I. J. Losada | Fulton, E.A., Link, J.S., Kaplan, I.C., Savina‐Rolland, M., Johnson, P., Ainsworth, C., Horne, P., Gorton, R., Gamble, R.J., Smith, A.D. and Smith, D.C. |
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Document Year
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2013 | 2013 | 2011 | 2011 | 2008 | 2007 | 2013 | 2014 | 2013 | 2015 | 2018 | 2013 | 2016 | 2013 | 2017 | 2008 | None | 2004 | 2011 |
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Document Title
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EnviroAtlas - Featured Community | EnviroAtlas - National | 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 | Mapping ecosystem services for planning and management | Mapping ecological risk of agricultural pesticide runoff | Assessment of coastal protection as an ecosystem service in Europe | Mapping soil erosion prevention using an ecosystem service modeling framework for integrated land management and policy | WaSSI Ecosystem Services Model | Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | Polyscape: A GIS mapping framework providing efficient and spatially explicit landscape-scale valuation of multple ecosystem services | ESII Tool | Diversity and distribution of floral resources influence the restoration of plant-pollinator networks on a reclaimed strip mine | Wild bee community change over a 26-year chronosequence of restored tallgrass prairie | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | An empirical model to estimate the propagation of random breaking and nonbreaking waves over vegetation fields | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
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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 | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Other or unclear (explain in Comment) | 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 |
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Comments on Status
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Published on US EPA EnviroAtlas website | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | While the user guide on which model entry is based has not been peer reviewed, several peer reviewed journal articles describing this USA HUC8 version of WaSSI have been published. | Published journal manuscript | Published journal manuscript | Published journal manuscript | Website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Journal manuscript submitted or in review | Published journal manuscript | Published journal manuscript |
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EM ID
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EM-59 |
EM-63 | EM-66 | EM-70 | EM-88 | EM-92 | EM-320 |
EM-321 |
EM-439 |
EM-480 |
EM-630 | EM-658 | EM-712 |
EM-774 |
EM-788 |
EM-844 | EM-893 |
EM-897 |
EM-990 |
| https://www.epa.gov/enviroatlas | https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://www.wassiweb.sgcp.ncsu.edu/ | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | www.policysupport.org/waterworld |
https://www.lucitools.org/ ?Comment:The LUCI (Land Utilisation and Capability Indicator) model, is a second-generation extension and software implementation of the Polyscape framework. |
https://www.esiitool.com/ | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://research.csiro.au/atlantis/home/links/ | |
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Contact Name
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EnviroAtlas Team |
EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Sandra Lavorel | Sandra Lavorel | Benis Egoh | Carola Alexandra Schriever | Camino Liquete | Carlos A. Guerra | Ge Sun | Xing Wu | Arnout van Soesbergen | Bethanna Jackson | Not reported |
Sarah Cusser ?Comment:Department of Evolution, Ecology, and Organismal Biology, Ohio State University, 318 West 12th Avenue, Columbus, OH 43202, U.S.A. |
Sean R. Griffin | Sam Riffell | Ted Angradi |
F. J. Mendez ?Comment:Tel.: +34-942-201810 |
Elizabeth Fulton |
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Contact Address
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Not reported | Not reported | 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 | Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | Helmholtz Centre for Environmental Research - UFZ, Department of System Ecotoxicology, Permoserstrasse 15, 04318 Leipzig, Germany | European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, I-21027 Ispra, VA, Italy | Instituto de Ciências Agrárias e Ambientais Mediterrânicas, Universidade de Évora, Pólo da Mitra, Apartado 94, 7002-554 Évora, Portugal | Eastern Forest Environmental Threat Assessment Center, Southern Research Station, USDA Forest Service, 920 Main Campus Dr. Venture II, Suite 300, Raleigh, NC 27606 | Chinese Academy of Sciences, Beijing 100085, China | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | School of Geography, Environment and Earth Sciences, Victoria University of Wellington, PO Box 600, Wellington, New Zealand | Not reported | Department of Evolution, Ecology, and Behavior, School of Biological Sciences, The University of Texas at Austin, 100 East 24th Street Stop A6500, Austin, TX 78712-1598, U.S.A. | Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901, U.S.A. | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | Not reported | Department of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas. 7001, Australia |
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Contact Email
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enviroatlas@epa.gov | enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | Not reported | carola.schriever@ufz.de | camino.liquete@gmail.com | cguerra@uevora.pt | gesun@fs.fed.us | xingwu@rceesac.cn | arnout.van_soesbergen@kcl.ac.uk | bethanna.jackson@vuw.ac.nz | Not reported | sarah.cusser@gmail.com | srgriffin108@gmail.com | sriffell@cfr.msstate.edu | tedangradi@gmail.com | mendezf@unican.es | beth.fulton@csiro.au |
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EM ID
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EM-59 |
EM-63 | EM-66 | EM-70 | EM-88 | EM-92 | EM-320 |
EM-321 |
EM-439 |
EM-480 |
EM-630 | EM-658 | EM-712 |
EM-774 |
EM-788 |
EM-844 | EM-893 |
EM-897 |
EM-990 |
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Summary Description
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The Air Pollutant Removal model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina. ... pollution removal ... are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA: The maps, estimate and illustrate the variation in the amount of six airborne pollutants, carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter (PM10), and particulate matter (PM2.5), removed by trees. PM10 is for particulate matter greater than 2.5 microns and less than 10 microns. DATA FACT SHEET: "The data for this map are based on the land cover derived for each EnviroAtlas community and the pollution removal models in i-Tree, a toolkit developed by the USDA Forest Service. The land cover data were created from aerial photography through remote sensing methods; tree cover was then summarized as the percentage of each census block group. The i-Tree pollution removal module uses the tree cover data by block group, the closest hourly meteorological monitoring data for the community, and the closest pollution monitoring data... hourly estimates of pollution removal by trees were combined with atmospheric data to estimate hourly percent air quality improvement due to pollution removal for each pollutant." | DATA FACT SHEET: "This EnviroAtlas national map displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM." | 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., litter biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in litter 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…Litter biomass production for each pixel was calculated and mapped using model estimates...This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on litter mass. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Simpson species diversity was modelled using the LU + abiotic [land use and all abiotic variables] model given that functional diversity should be a consequence of species diversity rather than the reverse (Lepsˇ et al. 2006)…Species diversity for each pixel was calculated and mapped using model estimates for effects of land use types, and for regression coefficients on abiotic variables. For each pixel these calculations were applied to mapped estimates of abiotic variables." | AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…In this study, only carbon storage was mapped because of a lack of data on the other functions related to the regulation of global climate such as carbon sequestration and the effects of changes in albedo. Carbon is stored above or below the ground and South African studies have found higher levels of carbon storage in thicket than in savanna, grassland and renosterveld (Mills et al., 2005). This information was used by experts to classify vegetation types (Mucina and Rutherford, 2006), according to their carbon storage potential, into three categories: low to none (e.g. desert), medium (e.g. grassland), high (e.g. thicket, forest) (Rouget et al., 2004). All vegetation types with medium and high carbon storage potential were identified as the range of carbon storage. Areas of high carbon storage potential where it is essential to retain this store were mapped as the carbon storage hotspot." | ABSTRACT: "The approach is based on the runoff potential (RP) of stream sites, by a spatially explicit calculation based on pesticide use, precipitation, topography, land use and soil characteristics in the near-stream environment. The underlying simplified model complies with the limited availability and resolution of data at larger scales." AUTHOR'S DESCRIPTION: "The RP is based on a mathematical model that describes runoff losses of a compound with generalized properties and which was developed from a proposal by the Organisation for Economic Co-operation and Development (OECD) for estimating dissolved runoff inputs of a pesticide into surface waters (OECD, 1998)...The runoff model underlying RP calculates the dissolved amount of a generic substance that was applied in the near environment of a stream site and that is expected to reach the stream site during one rainfall event. The dissolved amount results from a single application in the near-stream environment (i.e., a two-sided 100-m stream corridor extending for 1500 m upstream of the site) and is the amount of applied substance in the designated corridor reduced due to the influence of the site-specific key environmental factors precipitation, soil characteristics, topography, and plant interception." | ABSTRACT: "Mapping and assessment of ecosystem services is essential to provide scientific support to global and EU biodiversity policy. Coastal protection has been mostly analysed in the frame of coastal vulnerability studies or in local, habitat-specific assessments. This paper provides a conceptual and methodological approach to assess coastal protection as an ecosystem service at different spatial–temporal scales, and applies it to the entire EU coastal zone. The assessment of coastal protection incorporates 14 biophysical and socio-economic variables from both terrestrial and marine datasets. Those variables define three indicators: coastal protection capacity, coastal exposure and human demand for protection. A questionnaire filled by coastal researchers helped assign ranks to categorical parameters and weights to the individual variables. The three indicators are then framed into the ecosystem services cascade model to estimate how coastal ecosystems provide protection, in particular describing the service function, flow and benefit. The results are comparative and aim to support integrated land and marine spatial planning. The main drivers of change for the provision of coastal protection come from the widespread anthropogenic pressures in the European coastal zone, for which a short quantitative analysis is provided." | ABSTRACT: "We present an integrative conceptual framework to estimate the provision of soil erosion prevention (SEP) by combining the structural impact of soil erosion and the social–ecological processes that allow for its mitigation. The framework was tested and illustrated in the Portel municipality in Southern Portugal, a Mediterranean silvo-pastoral system that is prone to desertification and soil degradation. The results show a clear difference in the spatial and temporal distribution of the capacity for ecosystem service provision and the actual ecosystem service provision." AUTHOR'S DESCRIPTION: "To begin assessing the contribution of SEP we need to identify the structural impact of soil erosion, that is, the erosion that would occur when vegetation is absent and therefore no ES is provided. It determines the potential soil erosion in a given place and time and is related to rainfall erosivity (that is, the erosive potential of rainfall), soil erodibility (as a characteristic of the soil type) and local topography. Although external drivers can have an effect on these variables (for example, climate change), they are less prone to be changed directly by human action. The actual ES provision reduces the total amount of structural impact, and we define the remaining impact as the ES mitigated impact. We can then define the capacity for ES provision as a key component to determine the fraction of the structural impact that is mitigated…Following the conceptual outline, we will estimate the SEP provided by vegetation cover using an adaptation of the Universal Soil Loss Equation (USLE)." | AUTHORS DESCRIPTION: "WaSSI simulates monthly water and carbon dynamics at the Hydrologic Unit Code 8 level in the US. Three modules are integrated within the WaSSI model framework. The water balance module computes ecosystem water use, evapotranspiration and the water yield from each watershed. Water yield is sometimes referred to as runoff and can be thought of as the amount of streamflow at the outlet of each watershed due to hydrologic processes in each watershed in isolation without any flow contribution from upstream watersheds. The ecosystem productivity module simulates carbon gains and losses in each watershed or grid cell as functions of evapotranspiration. The water supply and demand module routes and accumulates the water yield through the river network according to topological relationships between adjacent watersheds, subtracts consumptive water use by humans from river flows, and compares water supply to water demand to compute the water supply stress index, or WaSSI." | ABSTRACT: "Land use changes are known to significantly affect the soil C balance by altering both C inputs and losses. Since the late 1990s, a large area of the Loess Plateau has undergone intensive land use changes during several ecological restoration projects to control soil erosion and combat land degradation, especially in the Grain for Green project. By using remote sensing techniques and the Yasso07 model, we simulated the dynamics of soil organic carbon (SOC) stocks in the Yangjuangou catchment of the Loess Plateau. The performance of the model was evaluated by comparing the simulated results with the intensive field measurements in 2006 and 2011 throughout the catchment. SOC stocks and NPP values of all land use types had generally increased during our study period. The average SOC sequestration rate in the upper 30 cm soil from 2006 to 2011 in the Yangjuangou catchment was approximately 44 g C m-2 yr-1, which was comparable to other studies in the Loess Plateau. Forest and grassland showed a more effective accumulation of SOC than the other land use types in our study area. The Yasso07 model performed reasonably well in predicting the overall dynamics of SOC stock for different land use change types at both the site and catchment scales. The assessment of the model performance indicated that the combination of Yasso07 model and remote sensing data could be used for simulating the effect of land use changes on SOC stock at catchment scale in the Loess Plateau." | 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: "This paper introduces a GIS framework (Polyscape) designed to explore spatially explicit synergies and trade-offs amongst ecosystem services to support landscape management (from individual fields through to catchments of ca 10,000 km2 scale). Algorithms are described and results presented from a case study application within an upland Welsh catchment (Pontbren). Polyscape currently includes algorithms to explore the impacts of land cover change on flood risk, habitat connectivity, erosion and associated sediment delivery to receptors, carbon sequestration and agricultural productivity. Algorithms to trade these single-criteria landscape valuations against each other are also provided, identifying where multiple service synergies exist or could be established. Changes in land management can be input to the tool and “traffic light” coded impact maps produced, allowing visualisation of the impact of different decisions. Polyscape hence offers a means for prioritising existing feature preservation and identifying opportunities for landscape change. The basic algorithms can be applied using widely available national scale digital elevation, land use and soil data. Enhanced output is possible where higher resolution data are available..." AUTHOR'S DESCRIPTION: "The framework acts as a screening tool to identify areas where scientific investigation might be valuably directed and/or where a lack of information exists, and allows flexibility and quick visualisation of the impact of different rural land management decisions on a variety of sustainability criteria. Specifically, Polyscape is designed to facilitate: 1. spatially explicit policy implementation; 2. integration of policy implementation across sectors (e.g., water, biodiversity, agriculture and forestry); 3. participation (and learning) by many different stakeholder groups. Importantly, it is designed not as a prescriptive decision making tool, but as a negotiation tool. Algorithms allow identification of ideas of where change might be beneficial – for example where installation of “structures” such as ponds or buffer strips might be considered optimal at a farm scale – but also allows users to trial their own plans and build in their own knowledge/restrictions. The framework aims to highlight areas with maximum potential for improvement, not to place value judgements on which methods (e.g., tillage change, land use change, hard engineering approaches) might be appropriate to realise such potential. Furthermore, the toolbox aims to identify areas of existing high value – e.g., particularly productive cropland, wetlands..." "Our case study site is the 12.5 km2 catchment of the Pontbren in mid-Wales." NOTE: The LUCI (Land Utilisation and Capability Indicator) model, is a second-generation extension and software implementation of the Polyscape framework, as described in EM-659. https://esml.epa.gov/detail/em/659 | AUTHORS DESCRIPTION: "The Nature Conservancy (TNC) and The Dow Chemical Company (Dow) initiated a collaborative effort to develop models that would help Dow and the wider business community identify and incorporate the value of nature into business decision making…the ESII Tool models and outputs were constructed and tested with an engineering and design perspective to facilitate actionable land use and management decisions. The ESII Tool helps non-ecologists make relative comparisons of the expected levels of ecosystem service performance across a given site, under a variety of conditions. As a planning-level tool, it can inform business decisions while enhancing the user’s relationship with nature. However, other uses that require ecological models of a higher degree of accuracy and/or precision, expert data collection, extensive sampling, and analysis of ecological relationships are beyond the intended scope of this tool." "The ESII App is your remote interface to the ESII Tool. It enables you to collect spatially-explicit ecological data, make maps, collect survey data, take photos, and record notes about your observations. With a Wi-Fi connection, the ESII App can upload and download information stored on the ESII Project Workspace, where final analyses and reports are generated. Because sites may be large and may include several different types of habitats, each Site to be assessed using the ESII Tool is divided into smaller areas called map units, and field data is collected on a map unit basis." "Once a map unit has been selected from the list of map units, the first survey question will always be “Map Unit Habitat Type” (Figure 12). The survey will progress through four categories of questions: habitat, vegetation, surface characteristics, and noise and visual screening. The questions are designed to enable you to select the most appropriate response easily and quickly." "Ecosystem Functions and Services scores are shown in units of percent performance, while each Units of Measure score will be shown in the engineering units appropriate to each attribute. At a map unit level, percent performance predicts how well a map unit would perform a given function or service as a proportion of the maximum potential you would expect from ideal attribute conditions. At a Site or Scenario level, percent performance is calculated as the area weighted average of the individual map unit’s percent performance values; it provides a normalized comparative metric between Sites or Scenarios. At both the map unit and the Site or Scenario levels, the units of measure represent absolute values (such as gallons of runoff or BTU reduction through shading) and can be either summed to show absolute performance of a Scenario, or normalized by area to show area-based rates of performance." | ABSTRACT: "Plant–pollinator mutualisms are one of the several functional relationships that must be reinstated to ensure the long-term success of habitat restoration projects. These mutualisms are unlikely to reinstate themselves until all of the resource requirements of pollinators have been met. By meeting these requirements, projects can improve their long-term success. We hypothesized that pollinator assemblage and structure and stability of plant–pollinator networks depend both on aspects of the surrounding landscape and of the restoration effort itself. We predicted that pollinator species diversity and network stability would be negatively associated with distance from remnant habitat, but that local floral diversity might rescue pollinator diversity and network stability in locations distant from the remnant. We created plots of native prairie on a reclaimed strip mine in central Ohio, U.S.A. that ranged in floral diversity and isolation from the remnant habitat. We found that the pollinator diversity declined with distance from the remnant habitat. Furthermore, reduced pollinator diversity in low floral diversity plots far from the remnant habitat was associated with loss of network stability. High floral diversity, however, compensated for losses in pollinator diversity in plots far from the remnant habitat through the attraction of generalist pollinators. Generalist pollinators increased network connectance and plant-niche overlap. Asa result, network robustness of high floral diversity plots was independent of isolation. We conclude that the aspects of the restoration effort itself, such as floral community composition, can be successfully tailored to incorporate the restoration of pollinators and improve success given a particular landscape context." | ABSTRACT: "Restoration efforts often focus on plants, but additionally require the establishment and long-term persistence of diverse groups of nontarget organisms, such as bees, for important ecosystem functions and meeting restoration goals. We investigated long-term patterns in the response of bees to habitat restoration by sampling bee communities along a 26-year chronosequence of restored tallgrass prairie in north-central Illinois, U.S.A. Specifically, we examined how bee communities changed over time since restoration in terms of (1) abundance and richness, (2) community composition, and (3) the two components of beta diversity, one-to-one species replacement, and changes in species richness. Bee abundance and raw richness increased with restoration age from the low level of the pre-restoration (agricultural) sites to the target level of the remnant prairie within the first 2–3 years after restoration, and these high levels were maintained throughout the entire restoration chronosequence. Bee community composition of the youngest restored sites differed from that of prairie remnants, but 5–7 years post-restoration the community composition of restored prairie converged with that of remnants. Landscape context, particularly nearby wooded land, was found to affect abundance, rarefied richness, and community composition. Partitioning overall beta diversity between sites into species replacement and richness effects revealed that the main driver of community change over time was the gradual accumulation of species, rather than one-to-one species replacement. At the spatial and temporal scales we studied, we conclude that prairie restoration efforts targeting plants also successfully restore bee communities." | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. " | 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." | ASTRACT: "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: "The theoretical solution for random waves is compared to the experimental results for an artificial kelp field given by Dubi (1995). The experiment was carried out in a 33-m-long, 1-m-wide and 1.6-m-high wave flume...The artificial kelp models were L. hyperborea" | Models are key tools for integrating a wide range of system information in a common framework. Attempts to model exploited marine ecosystems can increase understanding of system dynamics; identify major processes, drivers and responses; highlight major gaps in knowledge; and provide a mechanism to ‘road test’ management strategies before implementing them in reality. The Atlantis modelling framework has been used in these roles for a decade and is regularly being modified and applied to new questions (e.g. it is being coupled to climate, biophysical and economic models to help consider climate change impacts, monitoring schemes and multiple use management). This study describes some common lessons learned from its implementation, particularly in regard to when these tools are most effective and the likely form of best practices for ecosystem-based management (EBM). Most importantly, it highlighted that no single management lever is sufficient to address the many trade-offs associated with EBM and that the mix of measures needed to successfully implement EBM will differ between systems and will change through time. Although it is doubtful that any single management action will be based solely on Atlantis, this modelling approach continues to provide important insights for managers when making natural resource management decisions. |
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Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
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None identified | None Identified | None identified | None identified | None identified | European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | Supports global and EU biodiversity policy | None identified | WaSSI can be used to project the regional effects of forest land cover change, climate change, and water withdrawals on river flows, water supply stress, and ecosystem productivity (i.e., carbon sequestration).WaSSI can be used to evaluate trade-offs among management strategies that influence multiple ecosystem services | None identified | None identified | Polyscape acts as a screening tool to allow flexibility and visualisation of the impact of different rural land management decisions. | None identified | None identified | None identified | None reported | None identified | None identified | None identified |
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Biophysical Context
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No additional description provided | No additional description provided | Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | Not applicable | No additional description provided | Open savannah-like forest of cork (Quercus suber) and holm (Quercus ilex) oaks, with trees of different ages randomly dispersed in changing densities, and pastures in the under cover. The pastures are mostly natural in a mosaic with patches of shrubs, which differ in size and the distribution depends mainly on the grazing intensity. Shallow, poor soils are prone to erosion, especially in areas with high grazing pressure. | Conterminous US | Agricultural plain, hills, gulleys, forest, grassland, Central China | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | Elevation ranges between 170 m and 425 m | Not applicable | The site was surface mined for coal until the mid-1980s and soon after recontoured and seeded with a low diversity of non-native grasses and forbes. The property is grassland in a state of arrested succession, unable to support tree growth because of shallow, infertile soils. | The Nachusa Grasslands consists of over 1,900 ha of restored prairie plantings, prairie remnants, and other habitats such as wetlands and oak savanna. The area is generally mesic with an average annual precipitation of 975 mm, and most precipitation occurs during the growing season. | Conservation Reserve Program lands left to go fallow | Waterfront districts on south Lake Michigan and south lake Erie | No additional description provided | NA |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Different land management practices as represented by the comparison of different grazing intensities (i.e., livestock densities) in the whole study area and in three Civil Parishes within the study area |
No scenarios presented ?Comment:Model can be run from WaSSI website using a historic data set (1961 - 2010) or projections from various climate models representing different emissions scenarios and time periods from recent past to 2099. |
Land use change | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | Initial habitat coverage (1990), and planting additional broadleaved woodland (2001-2007) | No scenarios presented | No scenarios presented | No scenarios presented | N/A | N/A | No scenarios presented | No scenarios presented |
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EM ID
em.detail.idHelp
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EM-59 |
EM-63 | EM-66 | EM-70 | EM-88 | EM-92 | EM-320 |
EM-321 |
EM-439 |
EM-480 |
EM-630 | EM-658 | EM-712 |
EM-774 |
EM-788 |
EM-844 | EM-893 |
EM-897 |
EM-990 |
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Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
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Method + Application (multiple runs exist) View EM Runs | 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) | Method + Application | Method Only | 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 Only |
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New or Pre-existing EM?
em.detail.newOrExistHelp
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Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model |
Application of existing model ?Comment:. |
Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
em.detail.idHelp
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EM-59 |
EM-63 | EM-66 | EM-70 | EM-88 | EM-92 | EM-320 |
EM-321 |
EM-439 |
EM-480 |
EM-630 | EM-658 | EM-712 |
EM-774 |
EM-788 |
EM-844 | EM-893 |
EM-897 |
EM-990 |
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Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-345 |
Doc-346 | Doc-347 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
Doc-260 | Doc-260 | Doc-271 | Doc-255 | Doc-256 | Doc-257 | None | Doc-282 | Doc-283 | Doc-284 | Doc-285 | None | Doc-343 | Doc-342 | None | Doc-380 | None | None | None | Doc-405 | Doc-422 | Doc-424 | Doc-456 | Doc-459 | Doc-461 |
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EM ID for related EM
em.detail.relatedEmEmIdHelp
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None | None | EM-65 | 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-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-85 | EM-86 | EM-87 | None | None | None | None | EM-466 | EM-467 | EM-469 | EM-485 | None | EM-659 | EM-713 | None | None | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-843 | EM-845 | EM-846 | EM-847 | EM-886 | EM-888 | EM-889 | EM-890 | EM-891 | EM-894 | EM-895 | EM-896 | EM-904 | EM-978 | EM-981 | EM-983 | EM-985 | EM-991 |
EM Modeling Approach
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EM ID
em.detail.idHelp
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EM-59 |
EM-63 | EM-66 | EM-70 | EM-88 | EM-92 | EM-320 |
EM-321 |
EM-439 |
EM-480 |
EM-630 | EM-658 | EM-712 |
EM-774 |
EM-788 |
EM-844 | EM-893 |
EM-897 |
EM-990 |
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EM Temporal Extent
em.detail.tempExtentHelp
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2008-2010 | 2006-2010 | Not reported | 2007-2009 | Not reported | 2000 | 1992-2010 | January to December 2003 | 1961-2009 | 1969-2011 | 1950-2071 | 1990-2007 | Not applicable | 2009-2010 | 1988-2014 | 2008 | 2022 | Not appicable | Not applicable |
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EM Time Dependence
em.detail.timeDependencyHelp
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time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
em.detail.futurePastHelp
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future time | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | future time | future time | past time | both | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
em.detail.continueDiscreteHelp
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discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete | discrete | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | continuous | continuous |
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EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | 1 | 1 | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Hour | Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable | Month | Month | Year | Month | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM ID
em.detail.idHelp
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EM-59 |
EM-63 | EM-66 | EM-70 | EM-88 | EM-92 | EM-320 |
EM-321 |
EM-439 |
EM-480 |
EM-630 | EM-658 | EM-712 |
EM-774 |
EM-788 |
EM-844 | EM-893 |
EM-897 |
EM-990 |
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Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Geopolitical | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Not applicable | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Geopolitical | Other | Not applicable |
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Spatial Extent Name
em.detail.extentNameHelp
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Durham NC and vicinity | counterminous United States | Central French Alps | Central French Alps | South Africa | EU-15 | Shoreline of the European Union-27 | Portel municipality | All 8-digit hydrologic unit codes (HUC-8) in the conterminous USA | Yangjuangou catchment | Santa Basin | Pontbren catchment | Not applicable | The Wilds | Nachusa Grasslands | Piedmont Ecoregion | Great Lakes waterfront | wave flume | Not applicable |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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100-1000 km^2 | >1,000,000 km^2 | 10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 1-10 km^2 | 10,000-100,000 km^2 | 10-100 km^2 | Not applicable | 1-10 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | <1 ha | Not applicable |
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EM ID
em.detail.idHelp
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EM-59 |
EM-63 | EM-66 | EM-70 | EM-88 | EM-92 | EM-320 |
EM-321 |
EM-439 |
EM-480 |
EM-630 | EM-658 | EM-712 |
EM-774 |
EM-788 |
EM-844 | EM-893 |
EM-897 |
EM-990 |
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EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) ?Comment:Spatial grain type is census block group. |
spatially distributed (in at least some cases) ?Comment:Watersheds (12-digit HUCs). |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Spatial grain for computations is the HUC-8. A HUC-12 version is under development. Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:map units delineated by user based on project. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | Not applicable |
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Spatial Grain Type
em.detail.spGrainTypeHelp
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other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | length, for linear feature (e.g., stream mile) | Not applicable |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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irregular | irregular | 20 m x 20 m | 20 m x 20 m | Distributed across catchments with average size of 65,000 ha | 10 km x 10 km | Irregular | 250 m x 250 m | Computations are at the 8-digit HUC scale. MostHUC-8 watersheds are within a range of 800-8000 km^2 (500-5000 mi^2) in size. | 30m x 30m | 1 km2 | Not reported | map units | 10 m radius | Area varies by site | Not applicable | Not applicable | 1 m | Not applicable |
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EM ID
em.detail.idHelp
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EM-59 |
EM-63 | EM-66 | EM-70 | EM-88 | EM-92 | EM-320 |
EM-321 |
EM-439 |
EM-480 |
EM-630 | EM-658 | EM-712 |
EM-774 |
EM-788 |
EM-844 | EM-893 |
EM-897 |
EM-990 |
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EM Computational Approach
em.detail.emComputationalApproachHelp
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Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | * | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic |
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EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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None |
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EM ID
em.detail.idHelp
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EM-59 |
EM-63 | EM-66 | EM-70 | EM-88 | EM-92 | EM-320 |
EM-321 |
EM-439 |
EM-480 |
EM-630 | EM-658 | EM-712 |
EM-774 |
EM-788 |
EM-844 | EM-893 |
EM-897 |
EM-990 |
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Model Calibration Reported?
em.detail.calibrationHelp
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Unclear | No | No | No | No | No | No | No | No | No | No | No | Not applicable | Not applicable | No | Yes | No | No | Not applicable |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | Yes | Yes | No | No | No | No | No |
Yes ?Comment:p value: p<0.001 |
No | No | Not applicable | Not applicable | No | 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 | None | None | None |
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None | None | None | None | None | None | None | None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | No | Yes | No | No | No | No | No | No | No | Yes | No | Not applicable | Yes | No | No | No | Yes | Not applicable |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | No | Yes | No | No | No | No | No | No | Not applicable | Yes | No | No | No | No | Not applicable |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | No | No | Yes | No | No | No | No | No | No | Not applicable | No | No | Yes | Yes | No | 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 | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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EM-59 |
EM-63 | EM-66 | EM-70 | EM-88 | EM-92 | EM-320 |
EM-321 |
EM-439 |
EM-480 |
EM-630 | EM-658 | EM-712 |
EM-774 |
EM-788 |
EM-844 | EM-893 |
EM-897 |
EM-990 |
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None |
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None |
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|
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None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
|
EM-59 |
EM-63 | EM-66 | EM-70 | EM-88 | EM-92 | EM-320 |
EM-321 |
EM-439 |
EM-480 |
EM-630 | EM-658 | EM-712 |
EM-774 |
EM-788 |
EM-844 | EM-893 |
EM-897 |
EM-990 |
| None | None | None | None | None | None |
|
None | None | None | None | None | None | None | None | None | None |
|
None |
Centroid Lat/Long (Decimal Degree)
|
EM ID
em.detail.idHelp
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|
EM-59 |
EM-63 | EM-66 | EM-70 | EM-88 | EM-92 | EM-320 |
EM-321 |
EM-439 |
EM-480 |
EM-630 | EM-658 | EM-712 |
EM-774 |
EM-788 |
EM-844 | EM-893 |
EM-897 |
EM-990 |
|
Centroid Latitude
em.detail.ddLatHelp
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|
35.99 | 39.5 | 45.05 | 45.05 | -30 | 50.01 | 48.2 | 38.3 | 39.83 | 36.7 | -9.05 | 52.61 | Not applicable | 39.82 | 41.89 | 36.23 | 42.26 | 58.1 | Not applicable |
|
Centroid Longitude
em.detail.ddLongHelp
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|
-78.96 | -98.35 | 6.4 | 6.4 | 25 | 4.67 | 16.35 | -7.7 | -98.58 | 109.52 | -77.81 | -3.3 | Not applicable | -81.75 | -89.34 | -81.9 | -87.84 | -7.1 | Not applicable |
|
Centroid Datum
em.detail.datumHelp
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None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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|
Estimated | Estimated | Provided | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Not applicable | Provided | Provided | Estimated | Estimated | Estimated | Not applicable |
|
EM ID
em.detail.idHelp
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|
EM-59 |
EM-63 | EM-66 | EM-70 | EM-88 | EM-92 | EM-320 |
EM-321 |
EM-439 |
EM-480 |
EM-630 | EM-658 | EM-712 |
EM-774 |
EM-788 |
EM-844 | EM-893 |
EM-897 |
EM-990 |
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Created Greenspace | Atmosphere | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Scrubland/Shrubland |
Lakes and Ponds ?Comment:Watershed model represents all land areas, major streams and rivers. Since leaf area index, LAI, is an important variable, forests, created greenspaces (e.g., urban forests) and scrub/shrub subclasses are included. |
Agroecosystems | None | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Grasslands | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Grasslands | Agroecosystems | Grasslands | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Urban and vicinity | Terrestrial | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows | Not applicable | Arable lands in near-stream environments | Coastal zones | Silvo-pastoral system | Not applicable | Loess plain | tropical, coastal to montane | mainly of ‘improved’ pasture, semi-natural, unmanaged moorland, mature woodland, recent tree plantations, and small paved/roofed areas, root crops and open water | Not applicable | Grassland | Restored prairie, prairie remnants, and cropland | grasslands | Lake Michigan & Lake Erie waterfront | Near coastal marine and estuarine | Multiple |
|
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Not applicable | 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 coarser than that of the Environmental Sub-class |
Ecological scale is coarser than that of the Environmental Sub-class ?Comment:Terrestrial characteristics are aggregated at a broad (HUC-8) scale; different types of aquatic sub-classes are not differentiated. |
Ecological scale is finer than that of the Environmental Sub-class | Other or unclear (comment) | Ecological scale is finer than that of the Environmental Sub-class | Not applicable | 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 corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
|
EM ID
em.detail.idHelp
?
|
EM-59 |
EM-63 | EM-66 | EM-70 | EM-88 | EM-92 | EM-320 |
EM-321 |
EM-439 |
EM-480 |
EM-630 | EM-658 | EM-712 |
EM-774 |
EM-788 |
EM-844 | EM-893 |
EM-897 |
EM-990 |
|
EM Organismal Scale
em.detail.orgScaleHelp
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|
Not applicable | Not applicable | Community | Community | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unsure | Not applicable | Species | Species | Species | Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
|
EM-59 |
EM-63 | EM-66 | EM-70 | EM-88 | EM-92 | EM-320 |
EM-321 |
EM-439 |
EM-480 |
EM-630 | EM-658 | EM-712 |
EM-774 |
EM-788 |
EM-844 | EM-893 |
EM-897 |
EM-990 |
| None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available |
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|
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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-59 |
EM-63 | EM-66 | EM-70 | EM-88 | EM-92 | EM-320 |
EM-321 |
EM-439 |
EM-480 |
EM-630 | EM-658 | EM-712 |
EM-774 |
EM-788 |
EM-844 | EM-893 |
EM-897 |
EM-990 |
|
|
None | None |
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None |
|
|
|
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None |
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|
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|
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None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
|
EM-59 |
EM-63 | EM-66 | EM-70 | EM-88 | EM-92 | EM-320 |
EM-321 |
EM-439 |
EM-480 |
EM-630 | EM-658 | EM-712 |
EM-774 |
EM-788 |
EM-844 | EM-893 |
EM-897 |
EM-990 |
|
|
None | None | None | None |
|
|
|
None | None |
|
|
None | None |
|
None | None | None |
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