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Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-63 |
EM-148 
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EM-337 | EM-457 |
EM-660
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EM-702 |
EM-709
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EM-788
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EM-841 | EM-862 | EM-937 | EM-1010 | EM-1020 |
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EM Short Name
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EnviroAtlas - Natural biological nitrogen fixation | InVEST - Water provision, Francoli River, Spain | Rate of Fire Spread | Visitation to reef dive sites, St. Croix, USVI | RUM: Valuing fishing quality, Michigan, USA | Northern Shoveler recruits, CREP wetlands, IA, USA | Pollinators on landfill sites, United Kingdom | Wild bees over 26 yrs of restored prairie, IL, USA | Brown-headed cowbird abundance, Piedmont, USA | Recreational fishery index, USA | EPA national stormwater calculator tool | ORVal, Valuing recreational sites, W. Norwich, UK | EPIC agriculture model, Baden-Wurttemberg, Germany |
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EM Full Name
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US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) v2.4.2 - Water provision, Francoli River, Spain | Rate of Fire Spread | Visitation to dive sites (reef), St. Croix, USVI | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | Northern Shoveler duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Pollinating insects on landfill sites, East Midlands, United Kingdon | Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA | Brown-headed cowbird abundance, Piedmont ecoregion, USA | Recreational fishery index for streams and rivers, USA | Environmental Protection Agency National stormwater calculator tool | ORVal short case study: number 1 valuing recreational sites in West Norwich (version 2.0) | Carbon sequestration in soils of SW-Germany as affected by agricultural management—Calibration of the EPIC model for regional simulations |
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EM Source or Collection
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US EPA | EnviroAtlas | InVEST | None | US EPA | None | None | None | None | None | US EPA | US EPA | None | None |
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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. |
280 | 306 | 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. |
372 ?Comment:Document 373 is a secondary source for this EM. |
389 | 401 | 405 | 414 |
428 ?Comment:This is a tool available on the web for downloading to personal computers. A manual is also available for further documentation of the tool. |
476 | 482 |
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Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Marques, M., Bangash, R.F., Kumar, V., Sharp, R., and Schuhmacher, M. | Rothermel, Richard C. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin | Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree | Riffel, S., Scognamillo, D., and L. W. Burger | Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold | Rossman, L.A., Bernagros, J.T., Barr, C.M., and M.A. Simon | Day, B., & Smith | Billen, N., Röder, C., Gaiser, T. and Stahr, K., |
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Document Year
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2013 | 2013 | 1972 | 2014 | 2014 | 2010 | 2013 | 2017 | 2008 | 2021 | 2022 | None | 2009 |
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Document Title
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EnviroAtlas - National | The impact of climate change on water provision under a low flow regime: A case study of the ecosystems services in the Francoli river basin | A Mathematical model for predicting fire spread in wildland fuels | 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 | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects | 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 | Correspondence between a recreational fishery index and ecological condition for U.S.A. streams and rivers. | EPA National Stormwater Calculator Web App users guide-Version 3.4.0. | ORVal short case study: number 1 valuing recreational sites in West Norwich (version 2.0) | Carbon sequestration in soils of SW-Germany as affected by agricultural management—calibration of the EPIC model for regional simulations |
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Document Status
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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 |
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Comments on Status
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Published on US EPA EnviroAtlas website | Published journal manuscript | Published USDA Forest Service report | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report | Published report | Published journal manuscript |
Software and Access
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EM ID
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EM-63 |
EM-148
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EM-337 | EM-457 |
EM-660
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EM-702 |
EM-709
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EM-788
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EM-841 | EM-862 | EM-937 | EM-1010 | EM-1020 |
| https://www.epa.gov/enviroatlas | https://www.naturalcapitalproject.org/invest/ | http://firelab.org/project/farsite | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.epa.gov/water-research/national-stormwatercalculator | https://www.leep.exeter.ac.uk/orval/ | https://epicapex.tamu.edu/epic/ | |
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Contact Name
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EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Montse Marquès | Charles McHugh | Susan H. Yee | Richard Melstrom | David Otis | Sam Tarrant | Sean R. Griffin | Sam Riffell | Gregg Lomnicky | Lewis Rossman | Brett Day | Norbert Billen |
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Contact Address
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Not reported | Environmental Analysis and Management Group, Department d'Enginyeria Qimica, Universitat Rovira I Virgili, Tarragona, Catalonia, Spain | RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808 | 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 | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K. | 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 | 200 SW 35th St., Corvallis, OR, 97333 | Center for environmental solutions and emergency response, Cincinnati, Ohio | None provided | University of Hohenheim, Institute of Soil Science and Land Evaluation, Emil Wolff Strasse 27, D-70593 Stuttgart, Germany |
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Contact Email
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enviroatlas@epa.gov | montserrat.marques@fundacio.urv.cat | cmchugh@fs.fed.us | yee.susan@epa.gov | melstrom@okstate.edu | dotis@iastate.edu | sam.tarrant@rspb.org.uk | srgriffin108@gmail.com | sriffell@cfr.msstate.edu | lomnicky.gregg@epa.gov | n.a. | Brett.Day@exeter.ac.uk | billen@uni-hohenheim.de |
EM Description
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EM ID
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EM-63 |
EM-148
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EM-337 | EM-457 |
EM-660
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EM-702 |
EM-709
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EM-788
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EM-841 | EM-862 | EM-937 | EM-1010 | EM-1020 |
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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." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "InVEST 2.4.2 model runs as script tool in the ArcGIS 10 ArcTool-Box on a gridded map at an annual average time step, and its results can be reported in either biophysical or monetary terms, depending on the needs and the availability of information. It is most effectively used within a decision making process that starts with a series of stakeholder consultations to identify questions and services of interest to policy makers, communities, and various interest groups. These questions may concern current service delivery and how services may be affected by new programmes, policies, and conditions in the future. For questions regarding the future, stakeholders develop scenarios of management interventions or natural changes to explore the consequences of potential changes on natural resources [21]. This tool informs managers and policy makers about the impacts of alternative resource management choices on the economy, human well-being, and the environment, in an integrated way [22]. The spatial resolution of analyses is flexible, allowing users to address questions at the local, regional or global scales. | 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.** | 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)…Pendleton (1994) used field observations of dive sites to model potential impacts on local economies due to loss of dive tourism with reef degradation. A key part of the diver choice model is a fitted model of visitation to dive sites described by Visitation to dive sites = 2.897+0.0701creef -0.133D+0.0417τ where creef is percent coral cover, D is the time in hours to the dive site, which we estimate using distance from reef to shore and assuming a boat speed of 5 knots or 2.57ms-1, and τ is a dummy variable for the presence of interesting topographic features. We interpret τ as dramatic changes in bathymetry, quantified as having a standard deviation in depth among grid cells within 30 m that is greater than the75th percentile across all grid cells. Because our interpretation of topography differed from the original usage of “interesting features”, we also calculated dive site visitation assuming no contribution of topography (τ=0). Unsightly coastal development, an additional but non-significant variable in the original model, was assumed to be zero for St. Croix." | 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: "Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2*R*n where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = H*Z*B/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632). | ABSTRACT: "...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: "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: [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: EPA’s National Stormwater Calculator (SWC) is a software application tool that estimates the annual amount of rainwater and frequency of runoff from a specific site using green infrastructure as low impact development controls. The SWC is designed for use by anyone interested in reducing runoff from a property, including site developers, landscape architects, urban planners, and homeowners. This User’s guide contains information on the SWC web application. SWC Version 3.4 contains has updated historical meteorological data (from 1970 - 2006 to 1990 - 2019), updated Bureau of Labor Statistics Cost Data (from 2018 to 2020), and the 5.1.015 Stormwater Management Model (SWMM) engine (from 5.1.007). Evaporation was calculated by the Hargreaves method (EPA, 2015), based on historical or future daily temperature data." | The ORVal Tool is a web application (accessed at https://www.leep.exeter.ac.uk/orval) developed by the Land, Environment, Economics and Policy (LEEP) Institute at the University of Exeter with support from DEFRA. ORVal’s primary purpose is to help quantify the benefits that are derived from accessible outdoor recreation areas in England. Those outdoor recreation areas, or greenspaces, include an array of features such as beaches, parks, nature reserves and country paths This short case study uses ORVal to explore the visits and welfare values that are generated by existing greenspaces in West Norwich. It provides both practical details as to how tasks are performed in ORVal and explanations as to how the numbers reported by the Tool should be interpreted. | Global emissions trading allows for agricultural measures to be accounted for the carbon sequestration in soils. The Environmental Policy Integrated Climate (EPIC) model was tested for central European site conditions by means of agricultural extensification scenarios. Results of soil and management analyses of different management systems (cultivation with mouldboard plough, reduced tillage, and grassland/fallow establishment) on 13 representative sites in the German State Baden-Württemberg were used to calibrate the EPIC model. Calibration results were compared to those of the Intergovernmental Panel on Climate Change (IPCC) prognosis tool. The first calibration step included adjustments in (a) N depositions, (b) N2-fixation by bacteria during fallow, and (c) nutrient content of organic fertilisers according to regional values. The mixing efficiency of implements used for reduced tillage and four crop parameters were adapted to site conditions as a second step of the iterative calibration process, which should optimise the agreement between measured and simulated humus changes. Thus, general rules were obtained for the calibration of EPIC for different criteria and regions. EPIC simulated an average increase of +0.341 Mg humus-C ha−1 a−1 for on average 11.3 years of reduced tillage compared to land cultivated with mouldboard plough during the same time scale. Field measurements revealed an average increase of +0.343 Mg C ha−1 a−1 and the IPCC prognosis tool +0.345 Mg C ha−1 a−1. EPIC simulated an average increase of +1.253 Mg C ha−1 a−1 for on average 10.6 years of grassland/fallow establishment compared to an average increase of +1.342 Mg humus-C ha−1 a−1 measured by field measurements and +1.254 Mg C ha−1 a−1 according to the IPCC prognosis tool. The comparison of simulated and measured humus C stocks was r2 ≥ 0.825 for all treatments. However, on some sites deviations between simulated and measured results were considerable. The result for the simulation of yields was similar. In 49% of the cases the simulated yields differed from the surveyed ones by more than 20%. Some explanations could be found by qualitative cause analyses. Yet, for quantitative analyses the available information from farmers was not sufficient. Altogether EPIC is able to represent the expected changes by reduced tillage or grassland/fallow establishment acceptably under central European site conditions of south-western Germany. |
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Specific Policy or Decision Context Cited
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None Identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None reported | None identified | None given | None provided | Impact of different agricultural management strategies |
|
Biophysical Context
|
No additional description provided | Mediteranean coastal mountains | Not applicable | No additional description provided | stream and river reaches of Michigan | Prairie Pothole Region of Iowa | No additional description provided | 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 | None | Sites up to 12 acres | Urban greenspaces in the UK | Central Europe agricultural sites |
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EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
No scenarios presented | IPPC scenarios A2- severe changes in temperature and precipitation, B1 - more moderate variations in temperature and precipitation schemes from the present | No scenarios presented | No scenarios presented | targeted sport fish biomass | No scenarios presented | No scenarios presented | No scenarios presented | N/A | N/A | Climate change scenarios | NA | NA |
EM Relationship to Other EMs or Applications
|
EM ID
em.detail.idHelp
?
|
EM-63 |
EM-148
|
EM-337 | EM-457 |
EM-660
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EM-702 |
EM-709
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EM-788
|
EM-841 | EM-862 | EM-937 | EM-1010 | EM-1020 |
|
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
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Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method Only | Method + Application | Method + Application |
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New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
New or revised model | Application of existing model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | 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.detail.relatedEmHelp
?
|
EM ID
em.detail.idHelp
?
|
EM-63 |
EM-148
|
EM-337 | EM-457 |
EM-660
|
EM-702 |
EM-709
|
EM-788
|
EM-841 | EM-862 | EM-937 | EM-1010 | EM-1020 |
|
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-307 | Doc-311 | Doc-338 | Doc-205 | None | None | None | Doc-372 | Doc-373 | Doc-389 | None | Doc-405 | None | None | None | Doc-478 |
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EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
None | EM-344 | EM-368 | EM-437 | EM-111 | None | None | None | EM-705 | EM-704 | EM-703 | EM-701 | EM-700 | EM-632 | EM-697 | None | EM-831 | EM-838 | EM-839 | EM-842 | EM-843 | EM-844 | EM-845 | EM-846 | EM-847 | None | None | None | EM-1012 | EM-1021 |
EM Modeling Approach
EM Relationship to Time
|
EM ID
em.detail.idHelp
?
|
EM-63 |
EM-148
|
EM-337 | EM-457 |
EM-660
|
EM-702 |
EM-709
|
EM-788
|
EM-841 | EM-862 | EM-937 | EM-1010 | EM-1020 |
|
EM Temporal Extent
em.detail.tempExtentHelp
?
|
2006-2010 | 1971-2100 | Not applicable | 2006-2007, 2010 | 2008-2010 | 1987-2007 | 2007-2008 | 1988-2014 | 2008 | 2013-2014 | Not applicable | 2016-2018 |
4-20 years ?Comment:This paper compares agricultural plots that have used specific types of management practices over various periods ranging from 4-20 years. The beginning and end dates of those periods are not provided. |
|
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-stationary | time-stationary | Not applicable | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent |
|
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable | past time |
|
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable |
other or unclear (comment) ?Comment:This paper compares agricultural plots that have used specific types of management practices over various periods ranging from 4-20 years. The beginning and end dates of those periods are not provided. |
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EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
|
EM ID
em.detail.idHelp
?
|
EM-63 |
EM-148
|
EM-337 | EM-457 |
EM-660
|
EM-702 |
EM-709
|
EM-788
|
EM-841 | EM-862 | EM-937 | EM-1010 | EM-1020 |
|
Bounding Type
em.detail.boundingTypeHelp
?
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Geopolitical | Watershed/Catchment/HUC | Not applicable | Physiographic or ecological | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Physiographic or ecological | Geopolitical | Not applicable | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) |
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Spatial Extent Name
em.detail.extentNameHelp
?
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counterminous United States | Francoli River | Not applicable | Coastal zone surrounding St. Croix | HUCS in Michigan | CREP (Conservation Reserve Enhancement Program | East Midlands | Nachusa Grasslands | Piedmont Ecoregion | United States | Not applicable | West Norwich | Baden-Wurttemberg |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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>1,000,000 km^2 | 100-1000 km^2 | Not applicable | 100-1000 km^2 | 100,000-1,000,000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | 10-100 km^2 | 100,000-1,000,000 km^2 | >1,000,000 km^2 | Not applicable | 1-10 km^2 | 10,000-100,000 km^2 |
Spatial Distribution of Computations
|
EM ID
em.detail.idHelp
?
|
EM-63 |
EM-148
|
EM-337 | EM-457 |
EM-660
|
EM-702 |
EM-709
|
EM-788
|
EM-841 | EM-862 | EM-937 | EM-1010 | EM-1020 |
|
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) | 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 lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
|
Spatial Grain Type
em.detail.spGrainTypeHelp
?
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other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | Not applicable | 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) | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | length, for linear feature (e.g., stream mile) | Not applicable | map scale, for cartographic feature | Not applicable |
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Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
irregular | 30m x 30m | Not applicable | 10 m x 10 m | reach in HUC | multiple, individual, irregular sites | multiple unrelated locations | Area varies by site | Not applicable | stream reach (site) | Not applicable | Not reported | Not applicable |
EM Structure and Computation Approach
|
EM ID
em.detail.idHelp
?
|
EM-63 |
EM-148
|
EM-337 | EM-457 |
EM-660
|
EM-702 |
EM-709
|
EM-788
|
EM-841 | EM-862 | EM-937 | EM-1010 | EM-1020 |
|
EM Computational Approach
em.detail.emComputationalApproachHelp
?
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Analytic | Numeric | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | 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 |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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Model Checking Procedures Used
|
EM ID
em.detail.idHelp
?
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EM-63 |
EM-148
|
EM-337 | EM-457 |
EM-660
|
EM-702 |
EM-709
|
EM-788
|
EM-841 | EM-862 | EM-937 | EM-1010 | EM-1020 |
|
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | Not applicable | Yes | No | Unclear | Not applicable | No | Yes | No | Not applicable | Unclear | Yes |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | Not applicable | No | Yes | No | Not applicable | No | No | No | Not applicable | No | Yes |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
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None | None | None | None |
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None | None | None | None | None | None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
?
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No |
Yes ?Comment:Used Nash-Sutcliffe model efficiency index |
No | Yes | No | No | Not applicable | No | No | No | Not applicable | Unclear | Yes |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
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No | No | Not applicable | No | No | No | Not applicable | No | No | No | Not applicable | Unclear | Unclear |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | Not applicable | No | No | No | Not applicable | No | Yes | No | Not applicable | Unclear | Unclear |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
em.detail.relationToSpaceTerrestrialHelp
?
| EM-63 |
EM-148
|
EM-337 | EM-457 |
EM-660
|
EM-702 |
EM-709
|
EM-788
|
EM-841 | EM-862 | EM-937 | EM-1010 | EM-1020 |
|
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None | None |
|
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|
|
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
?
| EM-63 |
EM-148
|
EM-337 | EM-457 |
EM-660
|
EM-702 |
EM-709
|
EM-788
|
EM-841 | EM-862 | EM-937 | EM-1010 | EM-1020 |
| None | None | None |
|
None | None | None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
?
|
EM-63 |
EM-148
|
EM-337 | EM-457 |
EM-660
|
EM-702 |
EM-709
|
EM-788
|
EM-841 | EM-862 | EM-937 | EM-1010 | EM-1020 |
|
Centroid Latitude
em.detail.ddLatHelp
?
|
39.5 | 41.26 | -9999 | 17.73 | 45.12 | 42.62 | 52.22 | 41.89 | 36.23 | 36.21 | Not applicable | 52.62 | 48.62 |
|
Centroid Longitude
em.detail.ddLongHelp
?
|
-98.35 | 1.18 | -9999 | -64.77 | 85.18 | -93.84 | -0.91 | -89.34 | -81.9 | -113.76 | Not applicable | 1.25 | 9.03 |
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Centroid Datum
em.detail.datumHelp
?
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WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 |
|
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
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Estimated | Estimated | Not applicable | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Not applicable | Estimated | Estimated |
Environments and Scales Modeled
|
EM ID
em.detail.idHelp
?
|
EM-63 |
EM-148
|
EM-337 | EM-457 |
EM-660
|
EM-702 |
EM-709
|
EM-788
|
EM-841 | EM-862 | EM-937 | EM-1010 | EM-1020 |
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
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Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Rivers and Streams | Inland Wetlands | Agroecosystems | Grasslands | Created Greenspace | Grasslands | Agroecosystems | Grasslands | Grasslands | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Agroecosystems |
|
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Terrestrial | Coastal mountains | Not applicable | Coral reefs | stream reaches | Wetlands buffered by grassland within agroecosystems | restored landfills and grasslands | Restored prairie, prairie remnants, and cropland | grasslands | reach | Terrrestrial landcover | Urban Greenspaces | Agriculture plots |
|
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
em.detail.nameOfOrgsOrGroupsHelp
?
|
EM ID
em.detail.idHelp
?
|
EM-63 |
EM-148
|
EM-337 | EM-457 |
EM-660
|
EM-702 |
EM-709
|
EM-788
|
EM-841 | EM-862 | EM-937 | EM-1010 | EM-1020 |
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Individual or population, within a species | Individual or population, within a species | Species | Species | Guild or Assemblage | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
?
| EM-63 |
EM-148
|
EM-337 | EM-457 |
EM-660
|
EM-702 |
EM-709
|
EM-788
|
EM-841 | EM-862 | EM-937 | EM-1010 | EM-1020 |
| None Available | None Available | None Available | None Available |
|
|
|
|
|
None Available | None Available | None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
em.detail.cicesHelp
?
| EM-63 |
EM-148
|
EM-337 | EM-457 |
EM-660
|
EM-702 |
EM-709
|
EM-788
|
EM-841 | EM-862 | EM-937 | EM-1010 | EM-1020 |
|
|
None |
|
|
|
|
|
|
|
None |
|
|
<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
fegs1Help
?
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
?
| EM-63 |
EM-148
|
EM-337 | EM-457 |
EM-660
|
EM-702 |
EM-709
|
EM-788
|
EM-841 | EM-862 | EM-937 | EM-1010 | EM-1020 |
|
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None |
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None | None |
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None |
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