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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-65 | EM-69 | EM-306 | EM-327 | EM-424 | EM-434 |
EM-593 
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EM-667
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EM-841 | EM-842 | EM-876 | EM-878 |
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EM Short Name
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EnviroAtlas - Natural biological nitrogen fixation | Green biomass production, Central French Alps | Soil carbon content, Central French Alps | Urban Temperature, Baltimore, MD, USA | ARIES sediment regulation, Puget Sound Region, USA | Denitrification rates, Guánica Bay, Puerto Rico | Land capability classification | DayCent N2O flux simulation, Ireland | Alewife derived nutrients, Connecticut, USA | Brown-headed cowbird abundance, Piedmont, USA | Horned lark abundance, Piedmont region, USA | Neighborhood greenness and health, FL, USA | Health, safety and greening urban space, PA, USA |
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EM Full Name
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US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Green biomass production, Central French Alps | Soil carbon content, Central French Alps | Urban Air Temperature Change, Baltimore, MD, USA | ARIES (Artificial Intelligence for Ecosystem Services) Sediment Regulation for Reservoirs, Puget Sound Region, Washington, USA | Denitrification rates, Guánica Bay, Puerto Rico, USA | Land capability classification | DayCent simulation N2O flux and climate change, Ireland | Alewife derived nutrients in stream food web, Connecticut, USA | Brown-headed cowbird abundance, Piedmont ecoregion, USA | Horned lark abundance, Piedmont ecoregion, USA | Neighborhood greenness and chronic health conditions in Medicare beneficiaries, Miami-Dade County, Florida, USA | Health, safety and greening urban vacant space, Pennsylvania, USA |
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EM Source or Collection
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US EPA | EnviroAtlas | EU Biodiversity Action 5 | EU Biodiversity Action 5 | i-Tree | USDA Forest Service | ARIES | US EPA | None | None | None | None | None | 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. |
260 | 260 | 217 | 302 | 338 | 340 | 358 | 384 | 405 | 405 | 417 | 419 |
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Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Heisler, G. M., Ellis, A., Nowak, D. and Yesilonis, I. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | United States Department of Agriculture - Natural Resources Conservation Service | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Walters, A. W., R. T. Barnes, and D. M. Post | Riffel, S., Scognamillo, D., and L. W. Burger | Riffel, S., Scognamillo, D., and L. W. Burger | Brown, S. C., J. Lombard, K. Wang, M. M. Byrne, M. Toro, E. Plater-Zyberk, D. J. Feaster, J. Kardys, M. I. Nardi, G. Perez-Gomez, H. M. Pantin, and J. Szapocznik | Branas, C. C., R. A. Cheney, J. M. MacDonald, V. W. Tam, T. D. Jackson, and T. R. Ten Havey |
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Document Year
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2013 | 2011 | 2011 | 2016 | 2014 | 2017 | 2013 | 2010 | 2009 | 2008 | 2008 | 2016 | 2011 |
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Document Title
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EnviroAtlas - National | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Modeling and imaging land-cover influences on air-temperature in and near Baltimore, MD | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | National Soil Survey Handbook - Part 622 - Interpretative Groups | Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture | Anadromous alewives (Alosa pseudoharengus) contribute marine-derived nutrients to coastal stream food webs | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Neighborhood greenness and chronic health conditions in Medicare beneficiaries | A difference-in-differences analysis of health, safety, and greening vacant urban space |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | 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 journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-63 | EM-65 | EM-69 | EM-306 | EM-327 | EM-424 | EM-434 |
EM-593
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EM-667
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EM-841 | EM-842 | EM-876 | EM-878 |
| https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | http://aries.integratedmodelling.org/ | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | |
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Contact Name
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EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Sandra Lavorel | Sandra Lavorel | Gordon M. Heisler | Ken Bagstad | Susan H. Yee | United States Department of Agriculture | M. Abdalla | Annika W. Walters | Sam Riffell | Sam Riffell | Scott C. Brown | Charles C. Branas |
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Contact Address
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Not reported | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | 5 Moon Library, c/o SUNY-ESF, Syracuse, NY 13210 | Geosciences and Environmental Change Science Center, US Geological Survey | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Not reported | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Dept. of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511, USA | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Department of Public Health Sciences, University of Miami Miller School of Medicine, 1120 NW 14th Street, Clinical Research Building (CRB), Room 1065, Miami FL 33136 | Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Blockley Hall, Room 936, 423 Guardian Drive, Philadelphia, PA 19104-6021 |
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Contact Email
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enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | gheisler@fs.fed.us | kjbagstad@usgs.gov | yee.susan@epa.gov | http://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/contactus/ | abdallm@tcd.ie | annika.walters@yale.edu | sriffell@cfr.msstate.edu | sriffell@cfr.msstate.edu | sbrown@med.miami.edu | cbranas@upenn.edu |
EM Description
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EM ID
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EM-63 | EM-65 | EM-69 | EM-306 | EM-327 | EM-424 | EM-434 |
EM-593
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EM-667
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EM-841 | EM-842 | EM-876 | EM-878 |
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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." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in soil carbon was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy…Soil carbon for each pixel was calculated and mapped using model estimates...This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on soil carbon. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use." | An empirical model for predicting below-canopy air temperature differences is developed for evaluating urban structural and vegetation influences on air temperature in and near Baltimore, MD. AUTHOR'S DESCRIPTION: "The study . . . Developed an equation for predicting air temperature at the 1.5m height as temperature difference, T, between a reference weather station and other stations in a variety of land uses. Predictor variables were derived from differences in land cover and topography along with forcing atmospheric conditions. The model method was empirical multiple linear regression analysis.. . Independent variables included remotely sensed tree cover, impervious cover, water cover, descriptors of topography, an index of thermal stability, vapor pressure deficit, and antecedent precipitation." | ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "We mapped sediment regulation as the location of sediment sinks (depositional areas in floodplains), which can absorb sediment transported by hydrologic flows from upstream sources (erosionprone areas) prior to reaching users. In this case the benefit of avoided sedimentation is provided to 29 major reservoirs. Avoided sedimentation helps maintain the ability of reservoirs to provide benefits including hydroelectric power generation, flood control, recreation, and water supply to beneficiaries through the region. Avoided reservoir sedimentation likely helps to protect each of these benefits in different ways, i.e., increased turbidity or the loss of reservoir storage capacity may have a greater impact on some provision of some benefit types than others. For our purposes we ended the modeling and mapping exercise at the reservoirs. Reservoir sedimentation reduces their storage capacity, typically decreasing their ability to provide these benefits without costly dredging. We thus used a probabilistic Bayesian model of soil erosion incorporating vegetation, soils, and rainfall influences and calibrated using regional data from coarser scale and/or RUSLE derived erosion models (Bagstad et al. 2011). We probabilistically modeled sediment deposition in floodplains using data for floodplain vegetation, floodplain width, and stream gradient, which can influence rates of deposition. We calculated the ratio of actual to theoretical sediment regulation using the aggregated sink values upstream of reservoirs in the Puget Sound region, divided by aggregated theoretical sink values for the entire landscape." | AUTHOR'S DESCRIPTION: "Improving water quality was an objective of stakeholders in order to improve human health and reduce impacts to coral reef habitats. Four ecosystem services contributing to water quality were identified: denitrification...Denitrification rates were assigned to each land cover class, applying the mean of rates for natural sub-tropical ecosystems obtained from the literature…" | AUTHOR'S DESCRIPTION: "Definition. Land capability classification is a system of grouping soils primarily on the basis of their capability to produce common cultivated crops and pasture plants without deteriorating over a long period of time." "Class I (1) soils have slight limitations that restrict their use. Class II (2) soils have moderate limitations that reduce the choice of plants or require moderate conservation practices. Class III (3) soils have severe limitations that reduce the choice of plants or require special conservation practices, or both. Class IV (4) soils have very severe limitations that restrict the choice of plants or require very careful management, or both. Class V (5) soils have little or no hazard of erosion but have other limitations, impractical to remove, that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VI (6) soils have severe limitations that make them generally unsuited to cultivation and that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VII (7) soils have very severe limitations that make them unsuited to cultivation and that restrict their use mainly to rangeland, forestland, or wildlife habitat. Class VIII (8) soils and miscellaneous areas have limitations that preclude their use for commercial plant production and limit their use mainly to recreation, wildlife habitat, water supply, or esthetic purposes." [More information can be found at: http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_054226#ex2] | Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios. | ABSTRACT: "Diadromous fish are an important link between marine and freshwater food webs. Pacific salmon (Oncorhynchus spp.) strongly impact nutrient dynamics in inland waters and anadromous alewife (Alosa pseudoharengus) may play a similar ecological role along the Atlantic coast. The annual spawning migration of anadromous alewife contributes, on average, 1050 g of nitrogen and 120 g of phosphorus to Bride Brook, Connecticut, USA, through excretion and mortality each year... There was no significant effect of this nutrient influx on water chemistry, leaf decomposition, or periphyton accrual. Dam removal and fish ladder construction will allow anadromous alewife to regain access to historical freshwater spawning habitats, potentially impacting food web dynamics and nutrient cycling in coastal freshwater systems." | 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:"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: "Introduction: Prior studies suggest that exposure to the natural environment may impact health. The present study examines the association between objective measures of block-level greenness (vegetative presence) and chronic medical conditions, including cardiometabolic conditions, in a large population-based sample of Medicare beneficiaries in Miami-Dade County, Florida. Methods: The sample included 249,405 Medicare beneficiaries aged >=65 years whose location (ZIP+4) within Miami-Dade County, Florida, did not change, from 2010 to 2011. Data were obtained in 2013 and multilevel analyses conducted in 2014 to examine relationships between greenness, measured by mean Normalized Difference Vegetation Index from satellite imagery at the Census block level, and chronic health conditions in 2011, adjusting for neighborhood median household income, individual age, gender, race, and ethnicity. Results: Higher greenness was significantly associated with better health, adjusting for covariates: An increase in mean block-level Normalized Difference Vegetation Index from 1 SD less to 1 SD more than the mean was associated with 49 fewer chronic conditions per 1,000 individuals, which is approximately similar to a reduction in age of the overall study population by 3 years. This same level of increase in mean Normalized Difference Vegetation Index was associated with a reduced risk of diabetes by 14%, hypertension by 13%, and hyperlipidemia by 10%. Planned post-hoc analyses revealed stronger and more consistently positive relationships between greenness and health in lower- than higher-income neighborhoods. Conclusions: Greenness or vegetative presence may be effective in promoting health in older populations, particularly in poor neighborhoods, possibly due to increased time outdoors, physical activity, or stress mitigation." | ABSTRACT: "Greening of vacant urban land may affect health and safety. The authors conducted a decade-long difference-indifferences analysis of the impact of a vacant lot greening program in Philadelphia, Pennsylvania, on health and safety outcomes. ‘‘Before’’ and ‘‘after’’ outcome differences among treated vacant lots were compared with matched groups of control vacant lots that were eligible but did not receive treatment. Control lots from 2 eligibility pools were randomly selected and matched to treated lots at a 3:1 ratio by city section. Random-effects regression models were fitted, along with alternative models and robustness checks. Across 4 sections of Philadelphia, 4,436 vacant lots totaling over 7.8 million square feet (about 725,000 m^2) were greened from 1999 to 2008. Regression adjusted estimates showed that vacant lot greening was associated with consistent reductions in gun assaults across all 4 sections of the city (P < 0.001) and consistent reductions in vandalism in 1 section of the city (P < 0.001). Regression-adjusted estimates also showed that vacant lot greening was associated with residents’ reporting less stress and more exercise in select sections of the city (P < 0.01). Once greened, vacant lots may reduce certain crimes and promote some aspects of health. Limitations of the current study are discussed. Community-based trials are warranted to further test these findings." REVIEWER'S COMMENTS: Regression models were fitted separately for point-based, tract-based, and block group-based outcomes, and for the four sections of Philadelphia separately and combined. This entry presents just the point-based outcomes for the whole of Philadelphia. |
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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 provided | climate change | None identified | None reported | None reported | None identified | None identified |
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Biophysical Context
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No additional description provided | Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | One airport site, one urban site, one site in deciduous leaf litter, and four sites in short grass ground cover. Measured sky view percentages ranged from 6% at the woods site, to 96% at the rural open site. | No additional description provided | No additional description provided | No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Alewife spawning runs typically occur Mid March - May. | Conservation Reserve Program lands left to go fallow | Conservation Reserve Program lands left to go fallow | No additional description provided | No additional description provided |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | air temperature, precipitation, Atmospheric CO2 concentrations | No scenarios presented | N/A | N/A | No scenarios presented | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-63 | EM-65 | EM-69 | EM-306 | EM-327 | EM-424 | EM-434 |
EM-593
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EM-667
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EM-841 | EM-842 | EM-876 | EM-878 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + 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 | 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 |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-63 | EM-65 | EM-69 | EM-306 | EM-327 | EM-424 | EM-434 |
EM-593
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EM-667
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EM-841 | EM-842 | EM-876 | EM-878 |
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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-260 | Doc-260 | Doc-269 | Doc-220 | Doc-219 | Doc-218 | Doc-303 | Doc-305 | None | None | None | Doc-383 | Doc-405 | Doc-405 | None | None |
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EM ID for related EM
em.detail.relatedEmEmIdHelp
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None | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-65 | EM-66 | EM-68 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | None | None | None | EM-598 | EM-661 | EM-665 | EM-666 | EM-672 | EM-674 | EM-673 | EM-831 | EM-838 | EM-839 | EM-842 | EM-843 | EM-844 | EM-845 | EM-846 | EM-847 | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-843 | EM-844 | EM-845 | EM-846 | EM-847 | None | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
em.detail.idHelp
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EM-63 | EM-65 | EM-69 | EM-306 | EM-327 | EM-424 | EM-434 |
EM-593
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EM-667
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EM-841 | EM-842 | EM-876 | EM-878 |
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EM Temporal Extent
em.detail.tempExtentHelp
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2006-2010 | 2007-2009 | 2007-2009 | May 5-Sept 30 2006 | 1971-2005 |
1989 - 2011 ?Comment:6/21/16 BH - Rates were assigned from literature, ranging from 1989 - 2006, and the denitrification rate for urban lawns comes from 2011 literature. |
Not applicable | 1961-1990 | 1979-2009 | 2008 | 2008 | 2010-2011 | 1998-2008 |
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EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | Not applicable | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
em.detail.futurePastHelp
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Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | both | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Not applicable | Hour | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
em.detail.idHelp
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EM-63 | EM-65 | EM-69 | EM-306 | EM-327 | EM-424 | EM-434 |
EM-593
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EM-667
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EM-841 | EM-842 | EM-876 | EM-878 |
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Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Physiographic or ecological | Watershed/Catchment/HUC | Not applicable | Point or points | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Geopolitical | Geopolitical |
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Spatial Extent Name
em.detail.extentNameHelp
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counterminous United States | Central French Alps | Central French Alps | Baltimore, MD | Puget Sound Region | Guanica Bay watershed | Not applicable | Oak Park Research centre | Bride Brook | Piedmont Ecoregion | Piedmont Ecoregion | Miami-Dade County | Philadelphia |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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>1,000,000 km^2 | 10-100 km^2 | 10-100 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | Not applicable | 1-10 ha | 1-10 ha | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 |
Spatial Distribution of Computations
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EM ID
em.detail.idHelp
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EM-63 | EM-65 | EM-69 | EM-306 | EM-327 | EM-424 | EM-434 |
EM-593
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EM-667
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EM-841 | EM-842 | EM-876 | EM-878 |
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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) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | Not applicable | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Point-based measures are continuous and boundary-free, assign each lot to its own unique neighborhood, and avoid aggregation effects while directly accounting for spillover and the variability of neighboring areas. |
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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 | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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irregular | 20 m x 20 m | 20 m x 20 m | 10m x 10m | 200m x 200m | 30 m x 30 m | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Census block | Point based |
EM Structure and Computation Approach
|
EM ID
em.detail.idHelp
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EM-63 | EM-65 | EM-69 | EM-306 | EM-327 | EM-424 | EM-434 |
EM-593
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EM-667
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EM-841 | EM-842 | EM-876 | EM-878 |
|
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Not applicable | Numeric | 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
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EM ID
em.detail.idHelp
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EM-63 | EM-65 | EM-69 | EM-306 | EM-327 | EM-424 | EM-434 |
EM-593
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EM-667
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EM-841 | EM-842 | EM-876 | EM-878 |
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Model Calibration Reported?
em.detail.calibrationHelp
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No | No | No | Yes | Yes | No | Not applicable | No |
Yes ?Comment:The fish counter (for alewife numbers) was calibrated. |
Yes | Yes | Not applicable | No |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | Yes | Yes | Yes | No | No | Not applicable |
Yes ?Comment:for N2O fluxes |
No | No | No | No |
No ?Comment:Each outcome was fitted separatly, with R2 provided. See Variable Value comment for each Response. |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None |
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None | None | None |
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None | None | None | None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | Yes | Yes | No | No | No | No | Yes | No | No | No | No | No |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | No | No | Not applicable | No | No | No | No | No | No |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | No | No | No | Not applicable | No | No | Yes | Yes | No | No |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | 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
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| EM-63 | EM-65 | EM-69 | EM-306 | EM-327 | EM-424 | EM-434 |
EM-593
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EM-667
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EM-841 | EM-842 | EM-876 | EM-878 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
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| EM-63 | EM-65 | EM-69 | EM-306 | EM-327 | EM-424 | EM-434 |
EM-593
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EM-667
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EM-841 | EM-842 | EM-876 | EM-878 |
| None | None | None | None | None |
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None | None |
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None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-63 | EM-65 | EM-69 | EM-306 | EM-327 | EM-424 | EM-434 |
EM-593
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EM-667
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EM-841 | EM-842 | EM-876 | EM-878 |
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Centroid Latitude
em.detail.ddLatHelp
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39.5 | 45.05 | 45.05 | 39.28 | 48 | 17.96 | Not applicable | 52.86 | 41.32 | 36.23 | 36.23 | 25.64 | 39.95 |
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Centroid Longitude
em.detail.ddLongHelp
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-98.35 | 6.4 | 6.4 | -76.62 | -123 | -67.02 | Not applicable | 6.54 | -72.24 | -81.9 | -81.9 | -80.5 | -75.17 |
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Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Provided | Provided | Estimated | Estimated | Estimated | Not applicable | Provided | Provided | Estimated | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
em.detail.idHelp
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EM-63 | EM-65 | EM-69 | EM-306 | EM-327 | EM-424 | EM-434 |
EM-593
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EM-667
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EM-841 | EM-842 | EM-876 | EM-878 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Atmosphere | Rivers and Streams | Lakes and Ponds | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Rivers and Streams | Grasslands | Grasslands | Created Greenspace | Created Greenspace |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Terrestrial | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Urban landscape and surrounding area | Terrestrial environment surrounding a large estuary | Thirteen land use land cover classes were used | None identified | farm pasture | Coastal stream | grasslands | grasslands | urban neighborhood greenspace | Urban and urban green space |
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EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Not applicable | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale 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 coarser than that of 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
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EM ID
em.detail.idHelp
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EM-63 | EM-65 | EM-69 | EM-306 | EM-327 | EM-424 | EM-434 |
EM-593
|
EM-667
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EM-841 | EM-842 | EM-876 | EM-878 |
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EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Community | Community | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Individual or population, within a species | Species | Species | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
?
| EM-63 | EM-65 | EM-69 | EM-306 | EM-327 | EM-424 | EM-434 |
EM-593
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EM-667
|
EM-841 | EM-842 | EM-876 | EM-878 |
| None Available | None Available | None Available | None Available | None Available | None Available | 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.detail.cicesHelp
?
| EM-63 | EM-65 | EM-69 | EM-306 | EM-327 | EM-424 | EM-434 |
EM-593
|
EM-667
|
EM-841 | EM-842 | EM-876 | EM-878 |
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None | None |
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None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
fegs1Help
?
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
?
| EM-63 | EM-65 | EM-69 | EM-306 | EM-327 | EM-424 | EM-434 |
EM-593
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EM-667
|
EM-841 | EM-842 | EM-876 | EM-878 |
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None |
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None | None |
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