EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
EM Identity and Description
EM ID
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EM-79 | EM-91 |
EM-127 ![]() |
EM-374 | EM-379 | EM-392 | EM-418 | EM-449 |
EM-467 ![]() |
EM-549 |
EM-593 ![]() |
EM-962 | EM-981 | EM-985 | EM-1001 |
EM Short Name
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Divergence in flowering date, Central French Alps | RHyME2, Upper Mississippi River basin, USA | Annual profit - carbon plantings, South Australia | InVEST carbon storage and sequestration (v3.2.0) | VELMA soil temperature, Oregon, USA | EPA H2O, Tampa Bay Region, FL,USA | SIRHI, St. Croix, USVI | Decrease in erosion (shoreline), St. Croix, USVI | Yasso07 v1.0.1, Switzerland | Nutrient Tracking Tool (NTT) | DayCent N2O flux simulation, Ireland | RZWQM2, Quebec, Canada | Atlantis ecosystem biology submodel | Atlantis ecosystem assessment submodel | NBS benefits explorer |
EM Full Name
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Functional divergence in flowering date, Central French Alps | RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | Annual profit from carbon plantings, South Australia | InVEST v3.2.0 Carbon storage and sequestration | VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | EPA H2O, Tampa Bay Region, FL, USA | SIRHI (SImplified Reef Health Index), St. Croix, USVI | Decrease in erosion (shoreline) by reef, St. Croix, USVI | Yasso07 v1.0.1 forest litter decomposition, Switzerland | Nutrient Tracking Tool (NTT) | DayCent simulation N2O flux and climate change, Ireland | Root zone water quality model 2 mitigation of greenhouse gases, Quebec, Canada | Calibrating process-based marine ecosystem models: An example case using Atlantis | Lessons in modelling and management of marine ecosystems: the Atlantis experience | Benefit Accounting of Nature-Based Solutions for Watersheds: Guide |
EM Source or Collection
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EU Biodiversity Action 5 | US EPA | None | InVEST | US EPA | US EPA | US EPA | US EPA | None | None | None | None | None | None | None |
EM Source Document ID
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260 | 123 | 243 | 315 | 317 | 321 | 335 | 335 | 343 | 352 | 358 | 447 | 459 | 463 | 471 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C. | Crossman, N. D., Bryan, B. A., and Summers, D. M. | The Natural Capital Project | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Ranade, P., Soter, G., Russell, M., Harvey, J., and K. Murphy | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Didion, M., B. Frey, N. Rogiers, and E. Thurig | Saleh, A. and O. Gallego | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Jiang, Q., Zhiming, Q., Madramootoo, C.A., and Creze, C. | Pethybridge, H. R., Weijerman, M., Perrymann, H., Audzijonyte, A., Porobic, J., McGregor, V., … & Fulton, E. | Fulton, E.A., Link, J.S., Kaplan, I.C., Savina‐Rolland, M., Johnson, P., Ainsworth, C., Horne, P., Gorton, R., Gamble, R.J., Smith, A.D. and Smith, D.C. | Brill, G., T. Shiao, C. Kammeyer, S. Diringer, K. Vigerstol, N. Ofosu-Amaah, M. Matosich, C. Müller-Zantop, W. Larson and T. Dekker |
Document Year
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2011 | 2013 | 2011 | 2015 | 2013 | 2015 | 2014 | 2014 | 2014 | 2018 | 2010 | 2018 | 2019 | 2011 | 2022 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads | Carbon payments and low-cost conservation | Carbon storage and sequestration - InVEST (v3.2.0) | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | EPA H20 User Manual | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Validating tree litter decomposition in the Yasso07 carbon model | Nutrient Tracking Tool (NTT) User Manual | 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 | Mitigating greenhouse gas emisssions in subsurface-drained field using RZWQM2 | Calibrating process-based marine ecosystem models: An example case using Atlantis | Lessons in modelling and management of marine ecosystems: the Atlantis experience | Benefit Accounting of Nature-Based Solutions for Watersheds: Guide |
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 | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Website | Published journal manuscript | Published EPA report | Published journal manuscript | Published journal manuscript | Published journal manuscript | webpage | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report |
EM ID
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EM-79 | EM-91 |
EM-127 ![]() |
EM-374 | EM-379 | EM-392 | EM-418 | EM-449 |
EM-467 ![]() |
EM-549 |
EM-593 ![]() |
EM-962 | EM-981 | EM-985 | EM-1001 |
Not applicable | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | http://www.epa.gov/ged/tbes/EPAH2O | Not applicable | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | http://ntt.tiaer.tarleton.edu/welcomes/new?locale=en | Not applicable | Not applicable | https://noaa-fisheries-integrated-toolbox.github.io/Atlantis | https://noaa-fisheries-integrated-toolbox.github.io/Atlantis | https://nbsbenefitsexplorer.net/tool | |
Contact Name
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Sandra Lavorel | Liem Tran | Neville D. Crossman | The Natural Capital Project | Alex Abdelnour | Marc J. Russell, Ph.D. | Susan H. Yee | Susan H. Yee |
Markus Didion ?Comment:Tel.: +41 44 7392 427 |
Ali Saleh ?Comment:Phone # 254-968-9079 |
M. Abdalla | Zhiming Qi | Heidi R. Pethybridge | Elizabeth Fulton | Gregg Brill |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA | CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia | 371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | USEPA GED, One Sabine Island Dr., Gulf Breeze, FL 32561 | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Associate Director, Texas Institute for Applied Environmental Research, P.O. Box T410, Tarleton State University Stephenville, TX 76402 | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Department of Bioresource Engineering, McGill University, Sainte-Anne-de-Bellevue, QC H9X 3V9, Canada | CSIRO Oceans and Atmosphere, GPO Box 1538, Hobart, Tasmania, 7000, Australia | CSIRO Wealth from Oceans Flagship, Division of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas. 7001, Australia | Not reported |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | ltran1@utk.edu | neville.crossman@csiro.au | invest@naturalcapitalproject.org | abdelnouralex@gmail.com | russell.marc@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | markus.didion@wsl.ch | saleh@tarleton.edu | abdallm@tcd.ie | zhiming.qi@mcgill.ca | Heidi.Pethybridge@csiro.au | beth.fulton@csiro.au | Not reported |
EM ID
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EM-79 | EM-91 |
EM-127 ![]() |
EM-374 | EM-379 | EM-392 | EM-418 | EM-449 |
EM-467 ![]() |
EM-549 |
EM-593 ![]() |
EM-962 | EM-981 | EM-985 | EM-1001 |
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: "We describe a framework called Regional Hydrologic Modeling for Environmental Evaluation (RHyME2) for hydrologic modeling across scales. Rooted from hierarchy theory, RHyME2 acknowledges the rate-based hierarchical structure of hydrological systems. Operationally, hierarchical constraints are accounted for and explicitly described in models put together into RHyME2. We illustrate RHyME2with a two-module model to quantify annual nutrient loads in stream networks and watersheds at regional and subregional levels. High values of R2 (>0.95) and the Nash–Sutcliffe model efficiency coefficient (>0.85) and a systematic connection between the two modules show that the hierarchy theory-based RHyME2 framework can be used effectively for developing and connecting hydrologic models to analyze the dynamics of hydrologic systems." Two EMs will be entered in EPF-Library: 1. Regional scale module (Upper Mississippi River Basin) - this entry 2. Subregional scale module (St. Croix River Basin) | 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 an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. ABSTRACT: "Terrestrial ecosystems, which store more carbon than the atmosphere, are vital to influencing carbon dioxide-driven climate change. The InVEST model uses maps of land use and land cover types and data on wood harvest rates, harvested product degradation rates, and stocks in four carbon pools (aboveground biomass, belowground biomass, soil, dead organic matter) to estimate the amount of carbon currently stored in a landscape or the amount of carbon sequestered over time. Additional data on the market or social value of sequestered carbon and its annual rate of change, and a discount rate can be used in an optional model that estimates the value of this environmental service to society. Limitations of the model include an oversimplified carbon cycle, an assumed linear change in carbon sequestration over time, and potentially inaccurate discounting rates." AUTHOR'S DESCRIPTION: "A fifth optional pool included in the model applies to parcels that produce harvested wood products (HWPs) such as firewood or charcoal or more long-lived products such as house timbers or furniture. Tracking carbon in this pool is useful because it represents the amount of carbon kept from the atmosphere by a given product." | 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" | AUTHORS DESCRIPTION: "EPA H2O is a GIS based demonstration tool for assessing ecosystem goods and services (EGS). It was developed as a preliminary assessment tool in support of research being conducted in the Tampa Bay watershed. It provides information, data, approaches and guidance that communities can use to examine alternative land use scenarios in the context of nature’s benefits to the human community. . . EPA H2O allows users for the Tampa Bay estuary and its watershed to: • Gain a greater understanding of the significance of EGS, • Explore the spatial distribution of EGS and other ecosystem features, • Obtain map and summary statistics of EGS production's potential value, • Analyze and compare potential impacts from predicted development scenarios or user specified changes in land use patterns on EGS production's potential value EPA H2O is designed for analyzing data at neighborhood to regional scales.. . The tool is transportable to other locations if the required data are available. . . . | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of indicators have been proposed for measuring reef integrity, defined as the capacity to maintain healthy function and retention of diversity (Turner et al., 2000). The Simplified Integrated Reef Health Index (SIRHI) combines four attributes of reef condition into a single index: SIRHI = ΣiGi where Gi are the grades on a scale of 1 to 5 for four key reef attributes: percent coral cover, percent macroalgal cover, herbivorous fish biomass, and commercial fish biomass (Table2; Healthy Reefs Initiative, 2010). For a number of coral reef condition attributes, including fish richness, coral richness, and reef structural complexity, available data were point surveys from field monitoring by the US Environmental Protection Agency (see Oliver et al. (2011)) or the NOAA Caribbean Coral Reef Ecosystem Monitoring Program (see Pittman et al. (2008)). To generate continuous maps of coral condition for St. Croix, we fitted regression tree models to point survey data for St. Croix and then used models to predict reef condition in non-sampled locations (Fig. 1). In general, we followed the methods of Pittman et al. (2007) which generated predictive models for fish richness using readily available benthic habitat maps and bathymetry data. Because these models rely on readily available data (benthic habitat maps and bathymetry data), the models have the potential for high transferability to other locati | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef." | ABSTRACT: "...We examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to (i) examine the effect of the application of three different published Yasso07 parameter sets on simulated decay rate; (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests;…" AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root litter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The result of the simulation was an annual estimate of the remaining fraction of the initial mass, which could then be compared with observed data." | AUTHOR'S DESCRIPTION: "The Nutrient Tracking Tool (NTT) was designed and developed by the Texas Institute for Applied Environmental Research (TIAER), Tarleton State University with funding from USDA Office of Environmental Markets, USDA-NRCS Conservation Innovation Grants program, and various state agencies. NTT is a web-based, site-specific application that estimates nutrient and sediment losses at the field scale or at the small watershed scale. Agricultural producers and land managers can define a number of management scenarios and generate a report showing the expected nutrient loss differences between any selected scenarios for a given field or small watershed. NTT compares agricultural management systems to calculate a change in expected flow, nitrogen, phosphorus, sediment losses, and crop yield. Estimates are made using the APEX model (Williams et al. 2000). Results represent average losses from the field based on 35 years of simulated weather. NTT requires regional soils, climate and site-specific crop management information. NTT currently provides selections for all regions of U.S. and Puerto Rico territory, but it has only been validated for a limited number of states and counties. As validation becomes possible in other parts of the country, parameter files may be updated for additional counties in future versions. There are two versions of new NTT program available: The BASIC version is a user-friendly version of NTT that allows users to estimate N, P and sediment from crop and pasture lands. The Research and Education version of NTT (NTT-RE) was developed for researchers and educational institutes for teaching and training purposes. NTT-RE includes additional functions allowing the user to view and edit soil layers, view crop water and nutrient stresses, and modify and the APEX parameters file for calibration and validation purposes. The data sources and APEX simulations in both versions are identical. For more information regarding NTT, please refer to Saleh et al. (2011 and 2015)." | 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: "Greenhouse gas (GHG) emissions from agricultural soils are affected by various environmental factors and agronomic practices. The impact of inorganic nitrogen (N) fertilization rates and timing, and water table management practices on N2O and CO2 emissions were investigated to propose mitigation and adaptation efforts based on simulated results founded on field data. Drawing on 2012–2015 data measured on a subsurface-drained corn (Zea mays L.) field in Southern Quebec, the Root Zone Water Quality Model 2 (RZWQM2) was calibrated and validated for the estimation of N2O and CO2 emissions under free drainage (FD) and controlled drainage with sub-irrigation (CD-SI). Long term simulation from 1971 to 2000 suggested that the optimal N fertilization should be in the range of 125 to 175 kg N ha−1 to obtain higher NUE (nitrogen use efficiency, 7–14%) and lower N2O emission (8–22%), compared to 200 kg N ha−1 for corn-soybean rotation (CS). While remaining crop yields, splitting N application would potentially decrease total N2O emissions by 11.0%. Due to higher soil moisture and lower soil O2 under CD-SI, CO2 emissions declined by 6% while N2O emissions increased by 21% compared to FD. The CS system reduced CO2 and N2O emissions by 18.8% and 20.7%, respectively, when compared with continuous corn production. This study concludes that RZWQM2 model is capable of predicting GHG emissions, and GHG emissions from agriculture can be mitigated using agronomic management." | Calibration of complex, process-based ecosystem models is a timely task with modellers challenged by many parameters, multiple outputs of interest and often a scarcity of empirical data. Incorrect calibration can lead to unrealistic ecological and socio-economic predictions with the modeller’s experience and available knowledge of the modelled system largely determining the success of model calibration. Here we provide an overview of best practices when calibrating an Atlantis marine ecosystem model, a widely adopted framework that includes the parameters and processes comprised in many different ecosystem models. We highlight the importance of understanding the model structure and data sources of the modelled system. We then focus on several model outputs (biomass trajectories, age distributions, condition at age, realised diet proportions, and spatial maps) and describe diagnostic routines that can assist modellers to identify likely erroneous parameter values. We detail strategies to fine tune values of four groups of core parameters: growth, predator-prey interactions, recruitment and mortality. Additionally, we provide a pedigree routine to evaluate the uncertainty of an Atlantis ecosystem model based on data sources used. Describing best and current practices will better equip future modellers of complex, processed-based ecosystem models to provide a more reliable means of explaining and predicting the dynamics of marine ecosystems. Moreover, it promotes greater transparency between modellers and end-users, including resource managers. | Models are key tools for integrating a wide range of system information in a common framework. Attempts to model exploited marine ecosystems can increase understanding of system dynamics; identify major processes, drivers and responses; highlight major gaps in knowledge; and provide a mechanism to ‘road test’ management strategies before implementing them in reality. The Atlantis modelling framework has been used in these roles for a decade and is regularly being modified and applied to new questions (e.g. it is being coupled to climate, biophysical and economic models to help consider climate change impacts, monitoring schemes and multiple use management). This study describes some common lessons learned from its implementation, particularly in regard to when these tools are most effective and the likely form of best practices for ecosystem-based management (EBM). Most importantly, it highlighted that no single management lever is sufficient to address the many trade-offs associated with EBM and that the mix of measures needed to successfully implement EBM will differ between systems and will change through time. Although it is doubtful that any single management action will be based solely on Atlantis, this modelling approach continues to provide important insights for managers when making natural resource management decisions. | Watersheds around the world are in peril and risk further decline from climate change and human impacts, like pollution, degrading landscapes, and unsustainable water use. These impacts can inhibit the ability of ecosystems to regulate water flows, sequester carbon to reduce atmospheric greenhouse gas levels, maintain biodiversity and healthy waterways, promote social well-being, offer economic opportunities, and sustain agricultural productivity. Climate change is exacerbating these impacts by shifting weather and precipitation patterns, degrading habitats, and increasing the recurrence and severity of natural disasters. Urgent action is needed to address these impacts by implementing nature-based solutions (NBS). NBS protect, sustainably manage, and restore natural or modified watersheds, to address societal challenges effectively and adaptively, simultaneously providing human well-being and biodiversity benefits (IUCN, 2016). Investment in NBS offers a mechanism to restore degraded watersheds and protect intact ones, leading to improved water quality and quantity, improved carbon sequestration and increased biodiversity, among many other social and economic benefits. NBS also support climate mitigation and adaptation efforts and reduce the impacts from other shocks, such as floods, droughts, and extreme weather events. Implementing NBS can also help advance progress toward achieving the United Nations Sustainable Development Goals (SDGs), particularly SDG 2 (zero hunger), SDG 6 (water), SDG 11 (sustainable cities and communities), SDG 13 (climate action), and SDG 15 (life on land). NBS therefore support social, economic and environmental objectives, and may be particularly important in supporting vulnerable communities. |
Specific Policy or Decision Context Cited
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None identified | Not reported | None identified | None identified | None identified | None reported | None identified | None identified | None identified | None identified | climate change | None | N/A | None identified | None identified |
Biophysical Context
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Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | No additional description provided | 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. | Not applicable | 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. | Not applicable | No additional description provided | No additional description provided | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | None | Marine ecosystem | N/A | NA |
EM Scenario Drivers
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No scenarios presented | 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 | Optional future scenarios for changed LULC and wood harvest | No scenarios presented | Land Use, EGS algorithm values, | No scenarios presented | No scenarios presented |
No scenarios presented ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
No scenarios presented | air temperature, precipitation, Atmospheric CO2 concentrations | None | No scenarios presented | No scenarios presented | No scenarios presented |
EM ID
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EM-79 | EM-91 |
EM-127 ![]() |
EM-374 | EM-379 | EM-392 | EM-418 | EM-449 |
EM-467 ![]() |
EM-549 |
EM-593 ![]() |
EM-962 | EM-981 | EM-985 | EM-1001 |
Method Only, Application of Method or Model Run
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Method + Application | 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 Only | Method + Application | Method + Application | Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
Method Only | Method + Application (multiple runs exist) View EM Runs | None | Method Only | Method Only | Method Only |
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 | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | New or revised model | Application of existing model | None | 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 ID
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EM-79 | EM-91 |
EM-127 ![]() |
EM-374 | EM-379 | EM-392 | EM-418 | EM-449 |
EM-467 ![]() |
EM-549 |
EM-593 ![]() |
EM-962 | EM-981 | EM-985 | EM-1001 |
Document ID for related EM
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Doc-260 | Doc-269 | Doc-123 | Doc-245 | Doc-246 | Doc-247 | Doc-243 | Doc-309 | Doc-13 | Doc-317 | None | None | Doc-335 | Doc-342 | Doc-344 | None | None | None | Doc-456 | Doc-456 | Doc-459 | Doc-461 | None |
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 | None | EM-128 | EM-141 | EM-349 | EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | None | None | EM-447 | EM-448 | EM-466 | EM-469 | EM-480 | EM-485 | EM-584 | EM-598 | None | EM-978 | EM-983 | EM-985 | EM-990 | EM-991 | EM-978 | EM-981 | EM-983 | EM-990 | EM-991 | None |
EM Modeling Approach
EM ID
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EM-79 | EM-91 |
EM-127 ![]() |
EM-374 | EM-379 | EM-392 | EM-418 | EM-449 |
EM-467 ![]() |
EM-549 |
EM-593 ![]() |
EM-962 | EM-981 | EM-985 | EM-1001 |
EM Temporal Extent
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2007-2008 | 1987-1997 | 2009-2050 | Not applicable | 1969-2008 | Not applicable | 2006-2007, 2010 | 2006-2007, 2010 | 1993-2013 | 35 yr | 1961-1990 | 2012-2015 | Not applicable | Not applicable | Not applicable |
EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-dependent | time-dependent | time-dependent | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | future time | future time | future time | Not applicable | Not applicable | Not applicable | future time | Not applicable | both | past time | Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | discrete | discrete | discrete | Not applicable | Not applicable | Not applicable | discrete | discrete | discrete | discrete | continuous | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | 1 | 1 | 1 | Not applicable | Not applicable | Not applicable | 1 | 1 | 1 | 1 | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Year | Year | Day | Not applicable | Not applicable | Not applicable | Year | Day | Day | Year | Not applicable | Not applicable | Not applicable |
EM ID
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EM-79 | EM-91 |
EM-127 ![]() |
EM-374 | EM-379 | EM-392 | EM-418 | EM-449 |
EM-467 ![]() |
EM-549 |
EM-593 ![]() |
EM-962 | EM-981 | EM-985 | EM-1001 |
Bounding Type
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Physiographic or Ecological | Watershed/Catchment/HUC | Physiographic or Ecological | Not applicable | Watershed/Catchment/HUC |
Geopolitical ?Comment:Extent was Tampa Bay area in example, but boundary can be geopolitical or watershed derived. |
Physiographic or ecological | Physiographic or ecological | Geopolitical | Not applicable | Point or points | Point or points | Not applicable | Not applicable | Not applicable |
Spatial Extent Name
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Central French Alps | Upper Mississippi River basin; St. Croix River Watershed | Agricultural districts of the state of South Australia | Not applicable | H. J. Andrews LTER WS10 | Tampa Bay region | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Switzerland | Not applicable | Oak Park Research centre | Corn field | Not applicable | Not applicable | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | Not applicable | 10-100 ha | 1000-10,000 km^2. | 100-1000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | Not applicable | 1-10 ha | 1-10 ha | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-79 | EM-91 |
EM-127 ![]() |
EM-374 | EM-379 | EM-392 | EM-418 | EM-449 |
EM-467 ![]() |
EM-549 |
EM-593 ![]() |
EM-962 | EM-981 | EM-985 | EM-1001 |
EM Spatial Distribution
em.detail.distributeLumpHelp
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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) |
spatially distributed (in at least some cases) ?Comment:See below, grain includes vertical, subsurface dimension. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | Not applicable | Not applicable | spatially lumped (in all cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | NHDplus v1 | area, for pixel or radial feature | area, for pixel or radial feature | volume, for 3-D feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
Spatial Grain Size
em.detail.spGrainSizeHelp
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20 m x 20 m | NHDplus v1 | 1 ha x 1 ha | application specific | 30 m x 30 m surface pixel and 2-m depth soil column | 30m x 30m | 10 m x 10 m | 10 m x 10 m | 5 sites | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
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EM-79 | EM-91 |
EM-127 ![]() |
EM-374 | EM-379 | EM-392 | EM-418 | EM-449 |
EM-467 ![]() |
EM-549 |
EM-593 ![]() |
EM-962 | EM-981 | EM-985 | EM-1001 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Numeric | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Numeric | * | Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | None | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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None |
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EM ID
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EM-79 | EM-91 |
EM-127 ![]() |
EM-374 | EM-379 | EM-392 | EM-418 | EM-449 |
EM-467 ![]() |
EM-549 |
EM-593 ![]() |
EM-962 | EM-981 | EM-985 | EM-1001 |
Model Calibration Reported?
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No | Yes | No | Not applicable | No | No | Yes | Yes | No | Not applicable | No | None | Yes | Not applicable | Not applicable |
Model Goodness of Fit Reported?
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Yes | Yes | No | Not applicable | No | No | No | No | No | Not applicable |
Yes ?Comment:for N2O fluxes |
None | Not applicable | Not applicable | Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None | None | None | None |
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None | None | None | None |
Model Operational Validation Reported?
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No | No | No | Not applicable | No | No | Yes | Yes | Yes | Unclear | Yes | None | Not applicable | Not applicable | Unclear |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | Not applicable | No | No | No | No | No | Not applicable | No | None | Not applicable | Not applicable | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No |
No ?Comment:Some model coefficients serve, by their magnitude, to indicate the proportional impact on the final result of variation in the parameters they modify. |
No | Not applicable | No | No | No | No | No | Not applicable | No | None | Not applicable | Not applicable | Not applicable |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | None | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-79 | EM-91 |
EM-127 ![]() |
EM-374 | EM-379 | EM-392 | EM-418 | EM-449 |
EM-467 ![]() |
EM-549 |
EM-593 ![]() |
EM-962 | EM-981 | EM-985 | EM-1001 |
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None |
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None | None |
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None |
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None | None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-79 | EM-91 |
EM-127 ![]() |
EM-374 | EM-379 | EM-392 | EM-418 | EM-449 |
EM-467 ![]() |
EM-549 |
EM-593 ![]() |
EM-962 | EM-981 | EM-985 | EM-1001 |
None | None | None | None | None | None |
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None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-79 | EM-91 |
EM-127 ![]() |
EM-374 | EM-379 | EM-392 | EM-418 | EM-449 |
EM-467 ![]() |
EM-549 |
EM-593 ![]() |
EM-962 | EM-981 | EM-985 | EM-1001 |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | 42.5 | -34.9 | -9999 | 44.25 | 28.05 | 17.73 | 17.73 | 46.82 | Not applicable | 52.86 | 45.32 | Not applicable | Not applicable | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
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6.4 | -90.63 | 138.7 | -9999 | -122.33 | -82.52 | -64.77 | -64.77 | 8.23 | Not applicable | 6.54 | 74.17 | Not applicable | Not applicable | Not applicable |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | None provided | None provided | Not applicable | Not applicable | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Estimated | Estimated | Not applicable | Provided | Estimated | Estimated | Estimated | Estimated | Not applicable | Provided | Provided | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-79 | EM-91 |
EM-127 ![]() |
EM-374 | EM-379 | EM-392 | EM-418 | EM-449 |
EM-467 ![]() |
EM-549 |
EM-593 ![]() |
EM-962 | EM-981 | EM-985 | EM-1001 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Agroecosystems | Not applicable | Forests | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Agroecosystems | None | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland |
Specific Environment Type
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Subalpine terraces, grasslands, and meadows | None | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Terrestrial environments, but not specified for methods | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | All terestrial landcover and waterbodies | Coral reefs | Coral reefs | forests | Agroecosystems | farm pasture | None | Multiple | Multiple | None |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is coarser than that of the Environmental Sub-class | Ecosystem | Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | None | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-79 | EM-91 |
EM-127 ![]() |
EM-374 | EM-379 | EM-392 | EM-418 | EM-449 |
EM-467 ![]() |
EM-549 |
EM-593 ![]() |
EM-962 | EM-981 | EM-985 | EM-1001 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Community | Not applicable | Not applicable | None | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-79 | EM-91 |
EM-127 ![]() |
EM-374 | EM-379 | EM-392 | EM-418 | EM-449 |
EM-467 ![]() |
EM-549 |
EM-593 ![]() |
EM-962 | EM-981 | EM-985 | EM-1001 |
None Available | None Available |
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None Available | None Available | None Available |
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None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-79 | EM-91 |
EM-127 ![]() |
EM-374 | EM-379 | EM-392 | EM-418 | EM-449 |
EM-467 ![]() |
EM-549 |
EM-593 ![]() |
EM-962 | EM-981 | EM-985 | EM-1001 |
None |
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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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-79 | EM-91 |
EM-127 ![]() |
EM-374 | EM-379 | EM-392 | EM-418 | EM-449 |
EM-467 ![]() |
EM-549 |
EM-593 ![]() |
EM-962 | EM-981 | EM-985 | EM-1001 |
None | None | None | None | None |
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None |
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None |
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None |
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None | None |