EcoService Models Library (ESML)
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: Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA (EM-788)
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EM Identity and Description
EM Identification (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | New or revised model | EM-660 | EM-709 | EM-788 | Continuation of trends |
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EM Short Name
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Community flowering date, Central French Alps | Rate of Fire Spread | RUM: Valuing fishing quality, Michigan, USA | Pollinators on landfill sites, United Kingdom | Wild bees over 26 yrs of restored prairie, IL, USA | CommunityViz, Albany county, Wyoming |
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EM Full Name
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Community weighted mean flowering date, Central French Alps | Rate of Fire Spread | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, 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 | Wyoming Community Viz TM Partnership Phase I Pilot: Aquifer Protection and Community Viz TM in Albany County, Wyoming. |
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EM Source or Collection
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EU Biodiversity Action 5 | * | * | * | None | * |
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EM Source Document ID
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260 | 306 |
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 | 401 |
479 ?Comment:Published as a report by the University of Wyoming, but no record of peer review. |
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Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Rothermel, Richard C. | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | 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 | Lieske, S. N., Mullen, S., Knapp, M., & Hamerlinck, J. D. |
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Document Year
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2011 | 1972 | 2014 | 2013 | 2017 | 2003 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | A Mathematical model for predicting fire spread in wildland fuels | 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 | Wild bee community change over a 26-year chronosequence of restored tallgrass prairie | Wyoming Community Viz TM Partnership Phase I Pilot: Aquifer Protection and Community Viz TM in Albany County, Wyoming |
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Document Status
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* | Documented, not peer reviewed | * | * | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) |
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Comments on Status
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* | Published USDA Forest Service report | * | * | Published journal manuscript | Published report |
Software and Access (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | New or revised model | EM-660 | EM-709 | EM-788 | Continuation of trends |
| * | http://firelab.org/project/farsite | * | * | Not applicable | https://communityviz.com/ | |
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Contact Name
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Sandra Lavorel | Charles McHugh | Richard Melstrom | Sam Tarrant | Sean R. Griffin | Scott Lieske |
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Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808 | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | 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 Agricultural & Applied Economics University of Wyoming, Laramie WY 82071 |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | cmchugh@fs.fed.us | melstrom@okstate.edu | sam.tarrant@rspb.org.uk | srgriffin108@gmail.com | lieske@uwyo.edu |
EM Description (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | New or revised model | EM-660 | EM-709 | EM-788 | Continuation of trends |
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Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | ABSTRACT: "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: " 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: "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." | The Wyoming Community VizTM Partnership was established in 2001 to promote the use of geographic information system-based planning support systems and related decision support technologies in community land-use planning and economic development activities in the State of Wyoming. Partnership members include several state agencies, local governments and several nongovernment organizations. Partnership coordination is provided by the Wyoming Rural Development Council. Research and technical support is coordinated by the Wyoming Geographic Information Science Center’s Spatial Decision Support System Research Program at the University of Wyoming. In June 2002, the Partnership initiated a three-phase plan to promote Community VizTM based planning support systems in Wyoming. Phase I of the Partnership plan was a “proof of concept” pilot project set in Albany County in southeastern Wyoming. The goal of the project was to demonstrate the application of Community VizTM to a Wyoming-specific issue (in this case, aquifer protection) and to determine potential challenges for broader adoption in terms of data requirements, computing infrastructure and technological expertise. The results of the Phase I pilot project are detailed in this report. Efforts are currently underway to secure funding for Phase II of the plan, which expands the use of Community VizTM into four additional Wyoming communities. Specific Phase II objectives are to expand the type and number of issues addressed by Community VizTM and increase the use of Community VizTM in the planning process. As a part of Phase II the Partnership will create a technical assistance network aimed at assisting communities with the technical challenges in applying the software to their planning issues. The third phase will expand the program to more communities in the state, maintain the technical assistance network, and monitor the impact of Community VizTM on the planning process. |
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Specific Policy or Decision Context Cited
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* | * | * | * | None identified | None provided |
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Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Not applicable | stream and river reaches of Michigan | 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. | Groundwater recharge area, City of Laramie |
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EM Scenario Drivers
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* | * | targeted sport fish biomass | * | No scenarios presented | Continuation of trends |
EM Relationship to Other EMs or Applications
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EM ID
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New or revised model | New or revised model | EM-660 | EM-709 | EM-788 | Continuation of trends |
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Method Only, Application of Method or Model Run
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Method + Application | Method Only | Method + Application (multiple runs exist) | Method + Application (multiple runs exist) | Method + Application (multiple runs exist) | Model Run Associated with a Specific EM Application |
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New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Continuation of trends |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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New or revised model | New or revised model | EM-660 | EM-709 | EM-788 | Continuation of trends |
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Document ID for related EM
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Doc-260 | Doc-269 | None | None | Doc-389 | None | Doc-473 |
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EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | None | EM-697 | None | None |
EM Modeling Approach
EM Relationship to Time (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | New or revised model | EM-660 | EM-709 | EM-788 | Continuation of trends |
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EM Temporal Extent
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2007-2008 | Not applicable | 2008-2010 | 2007-2008 | 1988-2014 | 2050 |
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EM Time Dependence
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* | Not applicable | * | * | time-stationary | * |
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EM Time Reference (Future/Past)
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* | * | * | * | Not applicable | * |
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EM Time Continuity
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* | * | * | * | Not applicable | * |
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EM Temporal Grain Size Value
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* | * | * | * | Not applicable | * |
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EM Temporal Grain Size Unit
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* | * | * | * | Not applicable | * |
EM spatial extent (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | New or revised model | EM-660 | EM-709 | EM-788 | Continuation of trends |
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Bounding Type
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* | Not applicable | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Watershed/Catchment/HUC |
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Spatial Extent Name
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Central French Alps | Not applicable | HUCS in Michigan | East Midlands | Nachusa Grasslands | Laramie City's aquifer protection area |
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Spatial Extent Area (Magnitude)
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* | Not applicable | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | 10-100 km^2 | * |
Spatial Distribution of Computations (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | New or revised model | EM-660 | EM-709 | EM-788 | Continuation of trends |
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EM Spatial Distribution
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* | Not applicable | * | * | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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area, for pixel or radial feature | Not applicable | * | * | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable |
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Spatial Grain Size
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20 m x 20 m | Not applicable | reach in HUC | multiple unrelated locations | Area varies by site | Not applicable |
EM Structure and Computation Approach (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | New or revised model | EM-660 | EM-709 | EM-788 | Continuation of trends |
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EM Computational Approach
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* | * | Numeric | * | Analytic | Numeric |
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EM Determinism
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* | * | * | * | deterministic | * |
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Statistical Estimation of EM
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* |
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* | * |
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Model Checking Procedures Used (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | New or revised model | EM-660 | EM-709 | EM-788 | Continuation of trends |
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Model Calibration Reported?
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* | Not applicable | * | Not applicable | No | Unclear |
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Model Goodness of Fit Reported?
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Yes | Not applicable | Yes | Not applicable | No | * |
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Goodness of Fit (metric| value | unit)
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* |
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* | None | * |
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Model Operational Validation Reported?
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* | * | * | Not applicable | No | Unclear |
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Model Uncertainty Analysis Reported?
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* | Not applicable | * | Not applicable | No | Unclear |
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Model Sensitivity Analysis Reported?
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* | Not applicable | * | Not applicable | No | Unclear |
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Model Sensitivity Analysis Include Interactions?
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* | * | * | * | Not applicable | * |
EM Locations, Environments, Ecology
Location of EM Application (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| New or revised model | New or revised model | EM-660 | EM-709 | EM-788 | Continuation of trends |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| New or revised model | New or revised model | EM-660 | EM-709 | EM-788 | Continuation of trends |
| * | * | * | * | None | * |
Centroid Lat/Long (Decimal Degree)
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EM ID
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New or revised model | New or revised model | EM-660 | EM-709 | EM-788 | Continuation of trends |
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Centroid Latitude
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45.05 | -9999 | 45.12 | 52.22 | 41.89 | 41.31 |
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Centroid Longitude
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6.4 | -9999 | 85.18 | -0.91 | -89.34 | -105.46 |
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Centroid Datum
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* | Not applicable | * | * | WGS84 | * |
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Centroid Coordinates Status
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* | Not applicable | Estimated | Estimated | Provided | Estimated |
Environments and Scales Modeled (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | New or revised model | EM-660 | EM-709 | EM-788 | Continuation of trends |
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EM Environmental Sub-Class
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* | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Created Greenspace | Grasslands | Agroecosystems | Grasslands | Ground Water | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows. | Not applicable | stream reaches | restored landfills and grasslands | Restored prairie, prairie remnants, and cropland | watershed |
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EM Ecological Scale
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Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | * | Ecological scale corresponds to the Environmental Sub-class | * |
Organisms modeled (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
Scale of differentiation of organisms modeled
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EM ID
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New or revised model | New or revised model | EM-660 | EM-709 | EM-788 | Continuation of trends |
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EM Organismal Scale
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Community | Not applicable | Not applicable | Individual or population, within a species | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
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| New or revised model | New or revised model | EM-660 | EM-709 | EM-788 | Continuation of trends |
| * | * |
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EnviroAtlas URL
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| New or revised model | New or revised model | EM-660 | EM-709 | EM-788 | Continuation of trends |
| None Available | Average Annual Precipitation | The National Hydrography Dataset (NHD), The Watershed Boundary Dataset (WBD), Enabling Conditions, Employment Rate | GAP Ecological Systems | GAP Ecological Systems | Dasymetric Allocation of Population, Total Annual Reduced Nitrogen Deposition, Employment Rate |
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
* Note that run information is shown only where run data differ from the "parent" entry shown at left
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
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| New or revised model | New or revised model | EM-660 | EM-709 | EM-788 | Continuation of trends |
| * | * |
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| New or revised model | New or revised model | EM-660 | EM-709 | EM-788 | Continuation of trends |
| * | * |
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* | None |
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EM Variable Names (and Units)
* Note that for runs, variable name is displayed only where data for that variable differed by run AND those differences were reported in the source document. Where differences occurred but were not reported, the variable is not listed. Click on variable name to view details.
Predictor
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Intermediate
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EM ID
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New or revised model | New or revised model | EM-660 | EM-709 | EM-788 | Continuation of trends |
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Intermediate (Computed) Variables (and Units)
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None |
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None | None | * |
Response
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