EcoService Models Library (ESML)
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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
em.detail.idHelp
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EM-79 |
EM-127 
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EM-339 | EM-379 |
EM-593
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EM-891 |
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EM Short Name
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Divergence in flowering date, Central French Alps | Annual profit - carbon plantings, South Australia | InVEST crop pollination, NJ and PA, USA | VELMA soil temperature, Oregon, USA | DayCent N2O flux simulation, Ireland | Home ownership, Great Lakes, USA |
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EM Full Name
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Functional divergence in flowering date, Central French Alps | Annual profit from carbon plantings, South Australia | InVEST crop pollination, New Jersey and Pennsylvania, USA | VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | DayCent simulation N2O flux and climate change, Ireland | Human well being indicator - home ownership, Great Lakes waterfront, USA |
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EM Source or Collection
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EU Biodiversity Action 5 | None | InVEST | US EPA | None | US EPA |
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EM Source Document ID
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260 | 243 | 279 | 317 | 358 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
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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. | Crossman, N. D., Bryan, B. A., and Summers, D. M. | Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick |
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Document Year
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2011 | 2011 | 2009 | 2013 | 2010 | None |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Carbon payments and low-cost conservation | Modelling pollination services across agricultural landscapes | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | 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 | Human well-being and natural capital indictors for Great Lakes waterfront revitalization |
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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 but unpublished (explain in Comment) |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Journal manuscript submitted or in review |
Software and Access
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EM ID
em.detail.idHelp
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EM-79 |
EM-127
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EM-339 | EM-379 |
EM-593
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EM-891 |
| Not applicable | Not applicable | http://www.naturalcapitalproject.org/models/crop_pollination.html | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable | Not applicable | |
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Contact Name
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Sandra Lavorel | Neville D. Crossman | Eric Lonsdorf | Alex Abdelnour | M. Abdalla | Ted Angradi |
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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 | CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | neville.crossman@csiro.au | ericlonsdorf@lpzoo.org | abdelnouralex@gmail.com | abdallm@tcd.ie | tedangradi@gmail.com |
EM Description
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EM ID
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EM-79 |
EM-127
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EM-339 | EM-379 |
EM-593
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EM-891 |
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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. 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: "Functional divergence of flowering date 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: "A price on carbon is expected to generate demand for carbon offset schemes. This demand could drive investment in tree-based monocultures that provide higher carbon yields than diverse plantings of native tree and shrub species, which sequester less carbon but provide greater variation in vegetation structure and composition. Economic instruments such as species conservation banking, the creation and trading of credits that represent biological-diversity values on private land, could close the financial gap between monocultures and more diverse plantings by providing payments to individuals who plant diverse species in locations that contribute to conservation and restoration goals. We studied a highly modified agricultural system in southern Australia that is typical of many temperate agriculture zones globally (i.e., has a high proportion of endangered species, high levels of habitat fragmentation, and presence of non-native species). We quantified the economic returns...from carbon plantings (monoculture and mixed tree and shrubs) under six carbon-price scenarios." AUTHOR'S DESCRIPTION: "The economic returns of carbon plantings are highly variable and depend primarily on carbon yield and price and opportunity costs (Newell & Stavins 2000; Richards & Stokes 2004; Torres et al. 2010)...The spatial variation in carbon yield and costs, including establishment, maintenance, transaction, and opportunity costs, means that the net economic returns of carbon plantings are also likely to vary spatially." | 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. ABSTRACT: "Background and Aims: Crop pollination by bees and other animals is an essential ecosystem service. Ensuring the maintenance of the service requires a full understanding of the contributions of landscape elements to pollinator populations and crop pollination. Here, the first quantitative model that predicts pollinator abundance on a landscape is described and tested. Methods: Using information on pollinator nesting resources, floral resources and foraging distances, the model predicts the relative abundance of pollinators within nesting habitats. From these nesting areas, it then predicts relative abundances of pollinators on the farms requiring pollination services. Model outputs are compared with data from coffee in Costa Rica, watermelon and sunflower in California and watermelon in New Jersey–Pennsylvania (NJPA). Key Results: Results from Costa Rica and California, comparing field estimates of pollinator abundance, richness or services with model estimates, are encouraging, explaining up to 80 % of variance among farms. However, the model did not predict observed pollinator abundances on NJPA, so continued model improvement and testing are necessary. The inability of the model to predict pollinator abundances in the NJPA landscape may be due to not accounting for fine-scale floral and nesting resources within the landscapes surrounding farms, rather than the logic of our model. Conclusions: The importance of fine-scale resources for pollinator service delivery was supported by sensitivity analyses indicating that the model's predictions depend largely on estimates of nesting and floral resources within crops. Despite the need for more research at the finer-scale, the approach fills an important gap by providing quantitative and mechanistic model from which to evaluate policy decisions and develop land-use plans that promote pollination conservation and service delivery." | ABSTRACT: "We used a new ecohydrological model, Visualizing Ecosystems for Land Management Assessments (VELMA), to analyze the effects of forest harvest on catchment carbon and nitrogen dynamics. We applied the model to a 10 ha headwater catchment in the western Oregon Cascade Range where two major disturbance events have occurred during the past 500 years: a stand-replacing fire circa 1525 and a clear-cut in 1975. Hydrological and biogeochemical data from this site and other Pacific Northwest forest ecosystems were used to calibrate the model. Model parameters were first calibrated to simulate the postfire buildup of ecosystem carbon and nitrogen stocks in plants and soil from 1525 to 1969, the year when stream flow and chemistry measurements were begun. Thereafter, the model was used to simulate old-growth (1969–1974) and postharvest (1975–2008) temporal changes in carbon and nitrogen dynamics…" AUTHOR'S DESCRIPTION: "The soil column model consists of three coupled submodels:...a soil temperature model [Cheng et al., 2010] that simulates daily soil layer temperatures from surface air temperature and snow depth by propagating the air temperature first through the snowpack and then through the ground using the analytical solution of the one-dimensional thermal diffusion equation" | 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: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context. " |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified | climate change | None identified |
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Biophysical Context
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Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm. | No additional description provided | Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter. | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Waterfront districts on south Lake Michigan and south lake Erie |
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EM Scenario Drivers
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No scenarios presented | Carbon prices at $10/t CO2^-e, $15/t CO2^-e, $20/t CO2^-e, $25/t CO2^-e, $30/t CO2^-e, and $40/t CO2^-e | No scenarios presented | No scenarios presented | air temperature, precipitation, Atmospheric CO2 concentrations | N/A |
EM Relationship to Other EMs or Applications
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EM ID
em.detail.idHelp
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EM-79 |
EM-127
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EM-339 | EM-379 |
EM-593
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EM-891 |
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Method Only, Application of Method or Model Run
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Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Runs are differentiated based on the the expected annual profit from two types of carbon plantings: 1) Tree-based monocultures (i.e., monoculture carbon planting) and 2) Diverse plantings of native tree and shrub species (i.e., ecological carbon planting) |
Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | 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 | Application of existing model | Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
em.detail.relatedEmHelp
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EM ID
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EM-79 |
EM-127
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EM-339 | EM-379 |
EM-593
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EM-891 |
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Document ID for related EM
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Doc-260 | Doc-269 | Doc-245 | Doc-246 | Doc-247 | Doc-243 | Doc-279 | Doc-13 | Doc-317 | None | Doc-422 |
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EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83 | EM-128 | EM-141 | EM-340 | EM-338 | EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | EM-598 | EM-886 | EM-888 | EM-889 | EM-890 | EM-893 | EM-894 | EM-895 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-79 |
EM-127
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EM-339 | EM-379 |
EM-593
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EM-891 |
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EM Temporal Extent
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2007-2008 | 2009-2050 | 2000-2002 | 1969-2008 | 1961-1990 | 2022 |
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EM Time Dependence
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time-stationary | time-dependent | time-stationary | time-dependent | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | future time | Not applicable | future time | both | Not applicable |
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EM Time Continuity
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Not applicable | discrete | Not applicable | discrete | discrete | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable | 1 | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Year | Not applicable | Day | Day | Not applicable |
EM Spatial Extent
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EM ID
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EM-79 |
EM-127
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EM-339 | EM-379 |
EM-593
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EM-891 |
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Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Other | Watershed/Catchment/HUC | Point or points | Geopolitical |
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Spatial Extent Name
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Central French Alps | Agricultural districts of the state of South Australia | Central New Jersey and east-central Pennsylvania | H. J. Andrews LTER WS10 | Oak Park Research centre | Great Lakes waterfront |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | 10-100 ha | 1-10 ha | 1000-10,000 km^2. |
Spatial Distribution of Computations
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EM ID
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EM-79 |
EM-127
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EM-339 | EM-379 |
EM-593
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EM-891 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:See below, grain includes vertical, subsurface dimension. |
spatially lumped (in all cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | volume, for 3-D feature | Not applicable | Not applicable |
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Spatial Grain Size
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20 m x 20 m | 1 ha x 1 ha | 30 m x 30 m | 30 m x 30 m surface pixel and 2-m depth soil column | Not applicable | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-79 |
EM-127
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EM-339 | EM-379 |
EM-593
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EM-891 |
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EM Computational Approach
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Analytic | Analytic | Analytic | Numeric | Numeric | Numeric |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-79 |
EM-127
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EM-339 | EM-379 |
EM-593
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EM-891 |
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Model Calibration Reported?
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No | No | Unclear | No | No | No |
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Model Goodness of Fit Reported?
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Yes | No | No | No |
Yes ?Comment:for N2O fluxes |
No |
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Goodness of Fit (metric| value | unit)
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None | None | None |
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None |
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Model Operational Validation Reported?
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No | No |
Yes ?Comment:Aggregate native bee abundance on watermelon flowers was measured at 23 sites in 2005. Species richness was measured using the specimens collected from watermelon flowers at the end of the sampling period. |
No | Yes | No |
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Model Uncertainty Analysis Reported?
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No | No | No | No | No | No |
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Model Sensitivity Analysis Reported?
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No | No | No | No | No | Yes |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | 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])
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| EM-79 |
EM-127
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EM-339 | EM-379 |
EM-593
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EM-891 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-79 |
EM-127
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EM-339 | EM-379 |
EM-593
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EM-891 |
| None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-79 |
EM-127
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EM-339 | EM-379 |
EM-593
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EM-891 |
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Centroid Latitude
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45.05 | -34.9 | 40.2 | 44.25 | 52.86 | 42.26 |
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Centroid Longitude
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6.4 | 138.7 | -74.8 | -122.33 | 6.54 | -87.84 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 |
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Centroid Coordinates Status
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Provided | Estimated | Estimated | Provided | Provided | Estimated |
Environments and Scales Modeled
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EM ID
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EM-79 |
EM-127
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EM-339 | EM-379 |
EM-593
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EM-891 |
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Forests | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Cropland and surrounding landscape | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | farm pasture | Lake Michigan & Lake Erie waterfront |
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EM Ecological Scale
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Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-79 |
EM-127
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EM-339 | EM-379 |
EM-593
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EM-891 |
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EM Organismal Scale
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Community | Guild or Assemblage | Species | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-79 |
EM-127
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EM-339 | EM-379 |
EM-593
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EM-891 |
| None Available |
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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
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| EM-79 |
EM-127
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EM-339 | EM-379 |
EM-593
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EM-891 |
| None |
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None |
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None |
<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
fegs1Help
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
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| EM-79 |
EM-127
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EM-339 | EM-379 |
EM-593
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EM-891 |
| None | None |
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None |
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None |