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-63 | EM-88 |
EM-333 ![]() |
EM-337 | EM-428 | EM-451 |
EM-660 ![]() |
EM-709 ![]() |
EM-728 ![]() |
EM-788 ![]() |
EM-849 | EM-862 | EM-904 | EM-960 | EM-972 |
EM Short Name
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EnviroAtlas - Natural biological nitrogen fixation | Area and hotspots of carbon storage, South Africa | Evoland v3.5 (unbounded growth), Eugene, OR, USA | Rate of Fire Spread | Retained rainwater, Guánica Bay, Puerto Rico | Ease of reef access, St. Croix, USVI | RUM: Valuing fishing quality, Michigan, USA | Pollinators on landfill sites, United Kingdom | Seed mix and mowing in prairie reconstruction, USA | Wild bees over 26 yrs of restored prairie, IL, USA | InVEST Coastal Vulnerability | Recreational fishery index, USA | Drag coefficient Laminaria hyperborea | HAWQS model method | NC HUC-12 conservation prioritization tool |
EM Full Name
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US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Area and hotspots of carbon storage, South Africa | Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA | Rate of Fire Spread | Retained rainwater, Guánica Bay, Puerto Rico, USA | Ease of access (to reef), St. Croix, USVI | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | Pollinating insects on landfill sites, East Midlands, United Kingdon | Seed mix design and first year management in prairie reconstruction, IA, USA | Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA | InVEST Coastal Vulnerability | Recreational fishery index for streams and rivers, USA | Drag coefficient Laminaria hyperborea | Hydrologic and water quality system (HAWQS) model v.1.1 user's guide methodology | NC HUC-12 conservation prioritization tool v. 1.0, North Carolina, USA |
EM Source or Collection
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US EPA | EnviroAtlas | None | Envision | None | US EPA | US EPA | None | None | None | None | InVEST | US EPA | None | US EPA | None |
EM Source Document ID
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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. |
271 |
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
306 | 338 | 335 |
382 ?Comment:Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010. |
389 | 395 | 401 | 408 | 414 | 424 | 445 |
443 ?Comment:Doc 444 is an additional source for this EM |
Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Rothermel, Richard C. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin | Meissen, J. C., A. J. Glidden, M. E. Sherrard, K. J. Elgersma, and L. L. Jackson | Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree | The Natural Capital Project.org | Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold | Mendez, F. J. and I. J. Losada | United States Environmental Protection Agency | Warnell, K., I. Golden, and C. Canfield |
Document Year
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2013 | 2008 | 2008 | 1972 | 2017 | 2014 | 2014 | 2013 | 2019 | 2017 | None | 2021 | 2004 | 2019 | 2023 |
Document Title
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EnviroAtlas - National | Mapping ecosystem services for planning and management | Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | A Mathematical model for predicting fire spread in wildland fuels | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Valuing recreational fishing quality at rivers and streams | Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects | Seed mix design and first year management influence multifunctionality and cost-effectiveness in prairie reconstruction | Wild bee community change over a 26-year chronosequence of restored tallgrass prairie | InVEST Coastal Vulnerability | Correspondence between a recreational fishery index and ecological condition for U.S.A. streams and rivers. | An empirical model to estimate the propagation of random breaking and nonbreaking waves over vegetation fields | HAWQS 1.0 (Hydrologic and Water Quality System) modeling framework | Conservation planning tools for NC's people & nature |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Documented, not peer reviewed | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published USDA Forest Service report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Website users guide | Published journal manuscript | Published journal manuscript | Published EPA report | Webpage |
EM ID
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EM-63 | EM-88 |
EM-333 ![]() |
EM-337 | EM-428 | EM-451 |
EM-660 ![]() |
EM-709 ![]() |
EM-728 ![]() |
EM-788 ![]() |
EM-849 | EM-862 | EM-904 | EM-960 | EM-972 |
https://www.epa.gov/enviroatlas | Not applicable | http://evoland.bioe.orst.edu/ | http://firelab.org/project/farsite | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://naturalcapitalproject.stanford.edu/software/invest | Not applicable | Not applicable | https://dataverse.tdl.org/dataset.xhtml?persistentId=doi:10.18738/T8/GDOPBA | https://prioritizationcobenefitstool.users.earthengine.app/view/nc-huc-12-conservation-prioritizer | |
Contact Name
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EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Benis Egoh | Michael R. Guzy | Charles McHugh | Susan H. Yee | Susan H. Yee | Richard Melstrom | Sam Tarrant | Justin Meissen | Sean R. Griffin | Not applicable | Gregg Lomnicky | F. J. Mendez | Raghavan Srinivasan | Katie Warnell |
Contact Address
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Not reported | Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | Oregon State University, Dept. of Biological and Ecological Engineering | RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808 | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K. | Tallgrass Prairie Center, 2412 West 27th Street, Cedar Falls, IA 50614-0294, USA | Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901, U.S.A. | Not applicable | 200 SW 35th St., Corvallis, OR, 97333 | Not reported | Spatial Sciences Laboratory, Dept. of ecology and conservatin Biology, Texas A&M university | Not reported |
Contact Email
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enviroatlas@epa.gov | Not reported | Not reported | cmchugh@fs.fed.us | yee.susan@epa.gov | yee.susan@epa.gov | melstrom@okstate.edu | sam.tarrant@rspb.org.uk | justin.meissen@uni.edu | srgriffin108@gmail.com | Not applicable | lomnicky.gregg@epa.gov | mendezf@unican.es | r-srinivasan@tamu.edu | katie.warnell@duke.edu |
EM ID
em.detail.idHelp
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EM-63 | EM-88 |
EM-333 ![]() |
EM-337 | EM-428 | EM-451 |
EM-660 ![]() |
EM-709 ![]() |
EM-728 ![]() |
EM-788 ![]() |
EM-849 | EM-862 | EM-904 | EM-960 | EM-972 |
Summary Description
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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." | 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." | **Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | ABSTRACT: "The development of a mathematical model for predicting rate of fire spread and intensity applicable to a wide range of wildland fuels is presented from the conceptual stage through evaluation and demonstration of results to hypothetical fuel models. The model was developed for and is now being used as a basis for appraising fire spread and intensity in the National Fire Danger Rating System. The initial work was done using fuel arrays composed of uniform size particles. Three fuel sizes were tested over a wide range of bulk densities. These were 0.026-inch-square cut excelsior, 114-inch sticks, and 112-inch sticks. The problem of mixed fuel sizes was then resolved by weighting the various particle sizes that compose actual fuel arrays by either surface area or loading, depending upon the feature of the fire being predicted. The model is complete in the sense that no prior knowledge of a fuel's burning characteristics is required. All that is necessary are inputs describing the physical and chemical makeup of the fuel and the environmental conditions in which it is expected to burn. Inputs include fuel loading, fuel depth, fuel particle surface-area-to-volume ratio, fuel particle heat content, fuel particle moisture and mineral content, and the moisture content at which extinction can be expected. Environmental inputs are mean wind velocity and slope of terrain. For heterogeneous mixtures, the fuel properties are entered for each particle size. The model as originally conceived was for dead fuels in a uniform stratum contiguous to the ground, such as litter or grass. It has been found to be useful, however, for fuels ranging from pine needle litter to heavy logging slash and for California brush fields." **FARSITE4 will no longer be supported or available for download or further supported. FlamMap6 now includes FARSITE.** | AUTHOR'S DESCRIPTION: "In total, 19 ecosystem services metrics were identified as relevant to stakeholder objectives in the Guánica Bay watershed identified during the 2013 Public Values Forum (Table 2)...Ecological production functions were applied to translate LULC measures of ecosystem condition to supply of ecosystem services…The volume of retained rainwater per unit area (in^3/in^2) includes both the maximum soil moisture retention and the initial abstraction of water before runoff due to infiltration, evaporation, or interception by vegetation…" | 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: (1) ease of access for education" | ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. " | ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level…In the first year of study, plants in flower and flower visitors were surveyed using the same transects as for the floral resources surveys. The transect was left undisturbed for 20 minutes following the initial plant survey to allow the flower visitors to return. Each transect was surveyed at a rate of approximately 3m/minute for 30 minutes. All insects observed to touch the sexual parts of flowers were either captured using a butterfly net and transferred into individually labeled specimen jars, or directly captured into the jars. After the survey was completed, those insects that could be identified in the field were recorded and released. The flower-visitor surveys were conducted in the morning, within 1 hour of midday, and in the afternoon to sample those insects active at different times. Insects that could not be identified in the field were collected as voucher specimens for later identification. Identifications were verified using reference collections and by taxon specialists. Relatively low capture rates in the first year led to methods being altered in the second year when surveying followed a spiral pattern from a randomly determined point on the sites, at a standard pace of 10 m/minute for 30 minutes, following Nielsen and Bascompte (2007) and Kalikhman (2007). Given a 2-m wide transect, an area of approximately 600m2 was sampled in each | 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: "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." | Faced with an intensification of human activities and a changing climate, coastal communities need to better understand how modifications of the biological and physical environment (i.e. direct and indirect removal of natural habitats for coastal development) can affect their exposure to storm-induced erosion and flooding (inundation). The InVEST Coastal Vulnerability model produces a qualitative estimate of such exposure in terms of a vulnerability index, which differentiates areas with relatively high or low exposure to erosion and inundation during storms. By coupling these results with global population information, the model can show areas along a given coastline where humans are most vulnerable to storm waves and surge. The model does not take into account coastal processes that are unique to a region, nor does it predict long- or short-term changes in shoreline position or configuration. Model inputs, which serve as proxies for various complex shoreline processes that influence exposure to erosion and inundation, include: a polyline with attributes about local coastal geomorphology along the shoreline, polygons representing the location of natural habitats (e.g., seagrass, kelp, wetlands, etc.), rates of (observed) net sea-level change, a depth contour that can be used as an indicator for surge level (the default contour is the edge of the continental shelf), a digital elevation model (DEM) representing the topography of the coastal area, a point shapefile containing values of observed storm wind speed and wave power, and a raster representing population distribution. Outputs can be used to better understand the relative contributions of these different model variables to coastal exposure and highlight the protective services offered by natural habitats to coastal populations. This information can help coastal managers, planners, landowners and other stakeholders identify regions of greater risk to coastal hazards, which can in turn better inform development strategies and permitting. The results provide a qualitative representation of coastal hazard risks rather than quantifying shoreline retreat or inundation limits. | ABSTRACT: [Sport fishing is an important recreational and economic activity, especially in Australia, Europe and North America, and the condition of sport fish populations is a key ecological indicator of water body condition for millions of anglers and the public. Despite its importance as an ecological indicator representing the status of sport fish populations, an index for measuring this ecosystem service has not been quantified by analyzing actual fish taxa, size and abundance data across the U.S.A. Therefore, we used game fish data collected from 1,561 stream and river sites located throughout the conterminous U.S.A. combined with specific fish species and size dollar weights to calculate site-specific recreational fishery index (RFI) scores. We then regressed those scores against 38 potential site-specific environmental predictor variables, as well as site-specific fish assemblage condition (multimetric index; MMI) scores based on entire fish assemblages, to determine the factors most associated with the RFI scores. We found weak correlations between RFI and MMI scores and weak to moderate correlations with environmental variables, which varied in importance with each of 9 ecoregions. We conclude that the RFI is a useful indicator of a stream ecosystem service, which should be of greater interest to the U.S.A. public and traditional fishery management agencies than are MMIs, which tend to be more useful for ecologists, environmentalists and environmental quality agencies.] | 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." | Author overview: " The Hydrologic and Water Quality System (HAWQS) is a web-based interactive water quantity and water quality modeling system that employs the internationally-recognized public domain model Soil and Water Assessment Tool (SWAT) as its core modeling engine. HAWQS provides users with: 1) interactive web interfaces and maps and pre-loaded input data; 2) Output data includes tables, charts, graphs, and raw data; 3) A user guide; and 4) Online development, execution, and storage for users modeling projects. HAWQS enables use of SWAT to simulate the effects of management practices based on an extensive array of crops, soils, natural vegetation types, land uses, and climate change scenarios for hydrology and the following water quality parameters: Sediment pathogens, nutrients, biological oxygen demand, dissolved oxygen, pesticides, and water temperature. HAWQS users can select from three watershed scales, or hydrologic unit codes (HUCs)—small (HUC 12), medium (HUC 10), and large (HUC 8)—to run simulations. HAWQS allows for further aggregation and scalability of annual, monthly, and daily estimates of water quality across large geographic areas up to and including the continental United States. The United States Environmental Protection Agency (USEPA) Office of Water (OW) supports and provides project management and funding for HAWQS. The Texas A&M University Spatial Sciences Laboratory and EPA subject matter experts provide ongoing technical support including system design, modeling, and software development. The United States Department of Agriculture (USDA) and Texas A&M University jointly developed SWAT and have actively supported the model for more than 25 years. The system was developed to meet the needs of the USEPA Office of Water. It can also be employed by other Federal Agencies, State and local governments, academics, and contractors. " | ABSTRACT: "Conservation organizations and land trusts in North Carolina are increasingly focused on how their work can contribute to both human and ecosystem resilience and adaptation to climate change, as well as directly mitigate climate change through carbon storage and sequestration. Recent state executive and legislative actions also underscore the importance of natural systems for climate adaptation and mitigation, and may provide additional funding for conservation and restoration for those purposes in the near term. To make it more efficient for conservation organizations working in North Carolina to consider a broad suite of conservation benefits in their work, the Conservation Trust for North Carolina and the Nicholas Institute for Energy, Environment & Sustainability at Duke University have developed two online tools for identifying priority areas for conservation action and estimating benefit metrics for specific properties. The conservation prioritization tool finds the sub-watersheds in North Carolina with the greatest potential to provide a set of user-selected conservation benefits. It allows users to identify priority areas for future conservation work within the entire state or a defined region. This high-level tool allows for quick and easy exploration without the need for spatial analysis expertise." |
Specific Policy or Decision Context Cited
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None Identified | None identified | Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None identified | Meeting water demands for agriculture and domestic purposes. | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | Allows users to prioritize HUCs within their area of interest based on their conservation goals. |
Biophysical Context
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No additional description provided | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | No additional description provided | Not applicable | No additional descriptions provided | No additional description provided | stream and river reaches of Michigan | No additional description provided | 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. | 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. | Not applicable | None | No additional description provided | N/A | No additional description provided |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves. | No scenarios presented | No scenarios presented | No scenarios presented | targeted sport fish biomass | No scenarios presented | Seed mix design | No scenarios presented | Options for future sea level change and population change | N/A | No scenarios presented | N/A | No scenarios presented |
EM ID
em.detail.idHelp
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EM-63 | EM-88 |
EM-333 ![]() |
EM-337 | EM-428 | EM-451 |
EM-660 ![]() |
EM-709 ![]() |
EM-728 ![]() |
EM-788 ![]() |
EM-849 | EM-862 | EM-904 | EM-960 | EM-972 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application | Method + Application | Method Only | Method Only |
New or Pre-existing EM?
em.detail.newOrExistHelp
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New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
em.detail.idHelp
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EM-63 | EM-88 |
EM-333 ![]() |
EM-337 | EM-428 | EM-451 |
EM-660 ![]() |
EM-709 ![]() |
EM-728 ![]() |
EM-788 ![]() |
EM-849 | EM-862 | EM-904 | EM-960 | EM-972 |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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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-271 |
Doc-183 | Doc-47 | Doc-313 | Doc-314 ?Comment:Doc 183 is a secondary source for the Evoland model. |
None | None | None | None | Doc-389 | Doc-394 | None | Doc-410 | None | Doc-424 | None |
Doc-444 ?Comment:The secondary source, document 444, is the website for running the tool. |
EM ID for related EM
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None | EM-85 | EM-86 | EM-87 | EM-12 | EM-369 | None | None | None | None | EM-697 | EM-719 | None | EM-851 | None | EM-896 | EM-897 | None | None |
EM Modeling Approach
EM ID
em.detail.idHelp
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EM-63 | EM-88 |
EM-333 ![]() |
EM-337 | EM-428 | EM-451 |
EM-660 ![]() |
EM-709 ![]() |
EM-728 ![]() |
EM-788 ![]() |
EM-849 | EM-862 | EM-904 | EM-960 | EM-972 |
EM Temporal Extent
em.detail.tempExtentHelp
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2006-2010 | Not reported | 1990-2050 | Not applicable | 2006 - 2012 | 2006-2007, 2010 | 2008-2010 | 2007-2008 | 2015-2018 | 1988-2014 | Not applicable | 2013-2014 | Not applicable | Not applicable | Not applicable |
EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary | time-stationary | time-dependent | Not applicable | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | Not applicable | time-dependent | time-stationary |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
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Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | future time | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | Not applicable |
discrete ?Comment:Time can be in day, month or year increments |
Not applicable |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | 2 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | Not applicable | 1 | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Year | Not applicable | Year | Not applicable |
EM ID
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EM-63 | EM-88 |
EM-333 ![]() |
EM-337 | EM-428 | EM-451 |
EM-660 ![]() |
EM-709 ![]() |
EM-728 ![]() |
EM-788 ![]() |
EM-849 | EM-862 | EM-904 | EM-960 | EM-972 |
Bounding Type
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Geopolitical | Geopolitical | Geopolitical | Not applicable | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Other | Physiographic or ecological | Not applicable | Geopolitical | Not applicable | Not applicable | Not applicable |
Spatial Extent Name
em.detail.extentNameHelp
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counterminous United States | South Africa | Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Not applicable | Guanica Bay watershed | Coastal zone surrounding St. Croix | HUCS in Michigan | East Midlands | Iowa State University Northeast Research and Demonstration Farm near Nashua, Iowa | Nachusa Grasslands | Not applicable | United States | Not applicable | Not applicable | Not applicable |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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>1,000,000 km^2 | >1,000,000 km^2 | 10-100 km^2 | Not applicable | 1000-10,000 km^2. | 100-1000 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | <1 ha | 10-100 km^2 | Not applicable | >1,000,000 km^2 | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-63 | EM-88 |
EM-333 ![]() |
EM-337 | EM-428 | EM-451 |
EM-660 ![]() |
EM-709 ![]() |
EM-728 ![]() |
EM-788 ![]() |
EM-849 | EM-862 | EM-904 | EM-960 | EM-972 |
EM Spatial Distribution
em.detail.distributeLumpHelp
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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) | Not applicable | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
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 | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | 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 | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | Not applicable | Not applicable | map scale, for cartographic feature |
Spatial Grain Size
em.detail.spGrainSizeHelp
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irregular | Distributed across catchments with average size of 65,000 ha | varies | Not applicable | 30 m x 30 m | 10 m x 10 m | reach in HUC | multiple unrelated locations | 6.1 m x 8.53 m | Area varies by site | user defined | stream reach (site) | Not applicable | Not applicable | HUC 12 |
EM ID
em.detail.idHelp
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EM-63 | EM-88 |
EM-333 ![]() |
EM-337 | EM-428 | EM-451 |
EM-660 ![]() |
EM-709 ![]() |
EM-728 ![]() |
EM-788 ![]() |
EM-849 | EM-862 | EM-904 | EM-960 | EM-972 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Other or unclear (comment) |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-63 | EM-88 |
EM-333 ![]() |
EM-337 | EM-428 | EM-451 |
EM-660 ![]() |
EM-709 ![]() |
EM-728 ![]() |
EM-788 ![]() |
EM-849 | EM-862 | EM-904 | EM-960 | EM-972 |
Model Calibration Reported?
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No | No | Unclear | Not applicable | No | Yes | No | Not applicable | Not applicable | No | Not applicable | No | Yes | No | Not applicable |
Model Goodness of Fit Reported?
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No | No | No | Not applicable | No | No | Yes | Not applicable | No | No | Not applicable | No | Not applicable | No | Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None | None |
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None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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No | No | No | No | No | Yes | No | Not applicable | No | No | Not applicable | No | Unclear | No | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | Not applicable | No | No | No | Not applicable | No | No | Not applicable | No | No | No | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | Not applicable | No | No | No | Not applicable | No | No | Not applicable | No | No | No | Not applicable |
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 |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-63 | EM-88 |
EM-333 ![]() |
EM-337 | EM-428 | EM-451 |
EM-660 ![]() |
EM-709 ![]() |
EM-728 ![]() |
EM-788 ![]() |
EM-849 | EM-862 | EM-904 | EM-960 | EM-972 |
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None |
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None |
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None |
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None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-63 | EM-88 |
EM-333 ![]() |
EM-337 | EM-428 | EM-451 |
EM-660 ![]() |
EM-709 ![]() |
EM-728 ![]() |
EM-788 ![]() |
EM-849 | EM-862 | EM-904 | EM-960 | EM-972 |
None | None | None | None | None |
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None | None | None | None | None | None |
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None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-63 | EM-88 |
EM-333 ![]() |
EM-337 | EM-428 | EM-451 |
EM-660 ![]() |
EM-709 ![]() |
EM-728 ![]() |
EM-788 ![]() |
EM-849 | EM-862 | EM-904 | EM-960 | EM-972 |
Centroid Latitude
em.detail.ddLatHelp
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39.5 | -30 | 44.11 | -9999 | 17.96 | 17.73 | 45.12 | 52.22 | 42.93 | 41.89 | Not applicable | 36.21 | Not applicable | Not applicable | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
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-98.35 | 25 | -123.09 | -9999 | -67.02 | -64.77 | 85.18 | -0.91 | -92.57 | -89.34 | Not applicable | -113.76 | Not applicable | Not applicable | Not applicable |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | Not applicable | Not applicable | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated | Estimated | Estimated | Provided | Provided | Not applicable | Estimated | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-63 | EM-88 |
EM-333 ![]() |
EM-337 | EM-428 | EM-451 |
EM-660 ![]() |
EM-709 ![]() |
EM-728 ![]() |
EM-788 ![]() |
EM-849 | EM-862 | EM-904 | EM-960 | EM-972 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Rivers and Streams | Created Greenspace | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Near Coastal Marine and Estuarine | Agroecosystems | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Terrestrial | Not applicable | Agricultural-urban interface at river junction | Not applicable | 13 LULC were used | Coral reefs | stream reaches | restored landfills and grasslands | prairie/grassland reconstruction at demonstration farm site | Restored prairie, prairie remnants, and cropland | Coastal environments | reach | Near Coastal Marine and Estuarine | HUCs | Terrestrial and freshwater aquatic |
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 | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 coarser than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-63 | EM-88 |
EM-333 ![]() |
EM-337 | EM-428 | EM-451 |
EM-660 ![]() |
EM-709 ![]() |
EM-728 ![]() |
EM-788 ![]() |
EM-849 | EM-862 | EM-904 | EM-960 | EM-972 |
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Individual or population, within a species | Community | Species | Not applicable | Guild or Assemblage | Species | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-63 | EM-88 |
EM-333 ![]() |
EM-337 | EM-428 | EM-451 |
EM-660 ![]() |
EM-709 ![]() |
EM-728 ![]() |
EM-788 ![]() |
EM-849 | EM-862 | EM-904 | EM-960 | EM-972 |
None Available | None Available |
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None Available | None Available | None Available |
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None Available |
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None Available | None Available |
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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-63 | EM-88 |
EM-333 ![]() |
EM-337 | EM-428 | EM-451 |
EM-660 ![]() |
EM-709 ![]() |
EM-728 ![]() |
EM-788 ![]() |
EM-849 | EM-862 | EM-904 | EM-960 | EM-972 |
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None |
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None |
<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-63 | EM-88 |
EM-333 ![]() |
EM-337 | EM-428 | EM-451 |
EM-660 ![]() |
EM-709 ![]() |
EM-728 ![]() |
EM-788 ![]() |
EM-849 | EM-862 | EM-904 | EM-960 | EM-972 |
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None |
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None | None | None |
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None | None | None | None |
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None | None | None |