EcoService Models Library (ESML)
Home
Search EMs
My
EMs 
Learn about
ESML
View ESML Data Maploading
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.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
|
EM ID
em.detail.idHelp
?
|
EM-70 | EM-91 | EM-92 |
EM-129 
|
EM-326 |
EM-369
|
EM-458 | EM-459 | EM-549 | EM-647 |
EM-743
|
EM-846 | EM-939 |
|
EM Short Name
em.detail.shortNameHelp
?
|
Plant species diversity, Central French Alps | RHyME2, Upper Mississippi River basin, USA | Runoff potential of pesticides, Europe | 3-PG, South Australia | ARIES flood regulation, Puget Sound Region, USA | Envision, Puget Sound, WA, USA | Reef density of P. argus, St. Croix, USVI | Reef density of S. gigas, St. Croix, USVI | Nutrient Tracking Tool (NTT) | EcoAIM v.1.0 APG, MD | WESP: Irrigation water, ID, USA | Indigo bunting abund, Piedmont region, USA | ESTIMAP- Recreation, Europe |
|
EM Full Name
em.detail.fullNameHelp
?
|
Plant species diversity, Central French Alps | RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | Runoff potential of pesticides, Europe | 3-PG (Physiological Principles Predicting Growth), South Australia | ARIES (Artificial Intelligence for Ecosystem Services) Flood Regulation, Puget Sound Region, Washington, USA | Envision, Puget Sound, WA, USA | Relative density of Panulirus argus (on reef), St. Croix, USVI | Relative density of Strombus gigas (on reef), St. Croix, USVI | Nutrient Tracking Tool (NTT) | EcoAIM v.1.0, Aberdeen Proving Ground, MD | WESP: Irrigation return water treatment, Idaho, USA | Indigo bunting abundance, Piedmont ecoregion, USA | ESTIMAP- Recreation, Europe |
|
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
EU Biodiversity Action 5 | US EPA | None | None | ARIES | Envision | US EPA | US EPA | None | None | None | None | None |
|
EM Source Document ID
|
260 | 123 | 254 | 243 | 302 |
313 ?Comment:Doc 314 is a secondary source. It is a webpage guide intended to provide support for developing an application using ENVISION. |
335 | 335 | 352 | 374 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
405 | 432 |
|
Document Author
em.detail.documentAuthorHelp
?
|
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. | Schriever, C. A., and Liess, M. | Crossman, N. D., Bryan, B. A., and Summers, D. M. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Bolte, J. and Vache, K. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Saleh, A. and O. Gallego | Booth, P., Law, S. , Ma, J. Turnley, J., and J.W. Boyd | Murphy, C. and T. Weekley | Riffel, S., Scognamillo, D., and L. W. Burger | Zulian, G., Parrachini, M.L., Maes, J., |
|
Document Year
em.detail.documentYearHelp
?
|
2011 | 2013 | 2007 | 2011 | 2014 | 2010 | 2014 | 2014 | 2018 | 2014 | 2012 | 2008 | 2013 |
|
Document Title
em.detail.sourceIdHelp
?
|
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 | Mapping ecological risk of agricultural pesticide runoff | Carbon payments and low-cost conservation | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Envisioning Puget Sound Alternative Futures: PSNERP Final Report | 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 | Nutrient Tracking Tool (NTT) User Manual | Implementation of EcoAIM - A Multi-Objective Decision Support Tool for Ecosystem Services at Department of Defense Installations | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | ESTIMAP: Ecosystem services mapping at the European scale |
|
Document Status
em.detail.statusCategoryHelp
?
|
Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Documentation is 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
em.detail.commentsOnStatusHelp
?
|
Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | webpage | Published report | Published report | Published journal manuscript | Published report |
Software and Access
|
EM ID
em.detail.idHelp
?
|
EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 |
EM-369
|
EM-458 | EM-459 | EM-549 | EM-647 |
EM-743
|
EM-846 | EM-939 |
| Not applicable | Not applicable | Not applicable | http://www.csiro.au/products/3PGProductivity#a1 | http://aries.integratedmodelling.org/ | http://envision.bioe.orst.edu | Not applicable | Not applicable | http://ntt.tiaer.tarleton.edu/welcomes/new?locale=en | Not applicable | Not applicable | Not applicable | N.A. | |
|
Contact Name
em.detail.contactNameHelp
?
|
Sandra Lavorel | Liem Tran | Carola Alexandra Schriever | Anders Siggins | Ken Bagstad |
John Bolte ?Comment:Phone# 541-737-2041 |
Susan H. Yee | Susan H. Yee |
Ali Saleh ?Comment:Phone # 254-968-9079 |
Pieter Booth | Chris Murphy | Sam Riffell | Grazia Zulian |
|
Contact Address
|
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 | Helmholtz Centre for Environmental Research - UFZ, Department of System Ecotoxicology, Permoserstrasse 15, 04318 Leipzig, Germany | Not reported | Geosciences and Environmental Change Science Center, US Geological Survey | Oregon State University, Dept. of Biological & Ecological Engineering, 116C Gilmore Hall, Corvallis, OR 97333 | 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 | Associate Director, Texas Institute for Applied Environmental Research, P.O. Box T410, Tarleton State University Stephenville, TX 76402 | Exponent Inc., Bellevue WA | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Joint Research Centre, Via Enrico Fermi 2749, TP 272, 21027 Ispra (VA), Italy |
|
Contact Email
|
sandra.lavorel@ujf-grenoble.fr | ltran1@utk.edu | carola.schriever@ufz.de | Anders.Siggins@csiro.au | kjbagstad@usgs.gov | boltej@engr.orst.edu | yee.susan@epa.gov | yee.susan@epa.gov | saleh@tarleton.edu | pbooth@ramboll.com | chris.murphy@idfg.idaho.gov | sriffell@cfr.msstate.edu | grazia.zulian@jrc.ec.europa.e |
EM Description
|
EM ID
em.detail.idHelp
?
|
EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 |
EM-369
|
EM-458 | EM-459 | EM-549 | EM-647 |
EM-743
|
EM-846 | EM-939 |
|
Summary Description
em.detail.summaryDescriptionHelp
?
|
ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Simpson species diversity was modelled using the LU + abiotic [land use and all abiotic variables] model given that functional diversity should be a consequence of species diversity rather than the reverse (Lepsˇ et al. 2006)…Species diversity for each pixel was calculated and mapped using model estimates for effects of land use types, and for regression coefficients on abiotic variables. For each pixel these calculations were applied to mapped estimates of 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: "The approach is based on the runoff potential (RP) of stream sites, by a spatially explicit calculation based on pesticide use, precipitation, topography, land use and soil characteristics in the near-stream environment. The underlying simplified model complies with the limited availability and resolution of data at larger scales." AUTHOR'S DESCRIPTION: "The RP is based on a mathematical model that describes runoff losses of a compound with generalized properties and which was developed from a proposal by the Organisation for Economic Co-operation and Development (OECD) for estimating dissolved runoff inputs of a pesticide into surface waters (OECD, 1998)...The runoff model underlying RP calculates the dissolved amount of a generic substance that was applied in the near environment of a stream site and that is expected to reach the stream site during one rainfall event. The dissolved amount results from a single application in the near-stream environment (i.e., a two-sided 100-m stream corridor extending for 1500 m upstream of the site) and is the amount of applied substance in the designated corridor reduced due to the influence of the site-specific key environmental factors precipitation, soil characteristics, topography, and plant interception." | AUTHOR'S DESCRIPTION: "Carbon trading and its resultant market for carbon offsets are expected to drive investment in the sequestration of carbon through tree plantings (i.e., carbon plantings). Most carbon-planting investment has been in monocultures of trees that offer a rapid return on investment but have relatively little compositional and structural diversity (Bekessy & Wintle 2008; Munro et al. 2009). There are additional benefits available should carbon plantings comprise native species of diverse composition and age that are planted strategically to meet conservation and restoration objectives (hereafter ecological carbon plantings) (Bekessy &Wintle 2008; Dwyer et al. 2009; Bekessy et al. 2010). Ecological carbon plantings may increase availability of resources and refugia for native animals, but they often yield less carbon and are more expensive to establish than monocultures and therefore are less profitable…We used the tree-stand growth model 3-PG (physiological principles predicting growth) (Landsberg & Waring 1997) to simulate annual carbon sequestration under permanent carbon plantings in the part of the study area cleared for agriculture. The 3-PG model calculates total above- and below-ground biomass of a stand after accounting for soil water deficit, atmospheric vapor pressure deficits, and stand age…The 3-PG model was originally parameterized for a generic species, but species-specific parameters have since been calibrated for many commercially valuable trees (Paul et al. 2007) and most recently for mixed species used in permanent ecological restoration plantings (Polglase et al. 2008). We simulated four carbon-planting systems described in Polglase et al. (2008) for which the plants in the systems would grow in our study area. All species were native to areas of Australia with climate similar to that in the study area. We simulated the annual growth of three trees typically grown in monoculture (Eucalyptus globulus, native to Tasmania, constrained to precipitation ≥ 550 mm/year; Eucalyptus camaldulensis, native to the study area, constrained to 350–549 mm/year; Eucalyptus kochii, native to Western Australia, constrained to <350 mm/year). For the simulations of ecological carbon plantings we used a set of trees and shrubs representative of those planted for ecological restoration in temperate southern Australia (species list in England et al. 2006).We assumed the ecological carbon plantings were planted and managed in such a way as to comply with the definition of ecological restoration (Society for Ecological Restoration International Science and PolicyWorking Group 2004)." | ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "We estimated flood sinks, i.e., the capacity of the landscape to intercept, absorb, or detain floodwater, using a Bayesian model of vegetation, topography, and soil influences (Bagstad et al. 2011). This green infrastructure, the ecosystem service that we used for subsequent analysis, can combine with anthropogenic gray infrastructure, such as dams and detention basins, to provide flood regulation. Since flood regulation implies a hydrologic connection between sources, sinks, and users, we simulated its flow through a threestep process. First, we aggregated values for precipitation (sources of floodwater), flood mitigation (sinks), and users (developed land located in the 100-year floodplain) within each of the 502 12-digit Hydrologic Unit Code (HUC) watersheds within the Puget Sound region. Second, we subtracted the sink value from the source value for each subwatershed to quantify remaining floodwater and the proportion of mitigated floodwater. Third, we multiplied the proportion of mitigated floodwater for each subwatershed by the number of developed raster cells within the 100-year floodplain to yield a ranking of flood mitigation for each subwatershed...We calculated the ratio of actual to theoretical flood sinks by dividing summed flood sink values for subwatersheds providing flood mitigation to users by summed flood sink values for the entire landscape without accounting for the presence of at-risk structures." | SUMMARY: "...the Puget Sound Nearshore Ecosystem Restoration Project, completed an analysis of alternative future regional trajectories of landscape change for the Puget Sound region. This effort developed three scenarios of change: 1) Status Quo, reflecting a continuation of current trends in the region, 2) Managed Growth, reflecting the adoption of an aggressive set of land use management policies focusing on protecting and restoring ecosystem function and concentrating growth within Urban Growth Areas (UGA) and near regional growth centers, and 3) Unmanaged Growth, reflecting a relaxation of land use restrictions with limited protection of ecosystem functions. Analyses assumed a fixed population growth rate across all three scenarios, defined by the Washington Office of Financial Management county level growth estimates. Scenarios were generated using a spatially- and temporally-explicit alternative futures analysis model, Envision, previously developed by Oregon State University researchers. The model accepts as input a vector-based representation of the landscape and associated datasets describing relevant landscape characteristics, descriptors of various processes influencing landscape change, and a set of policies, or decision alternatives, which reflect scenario-specific land management alternatives. The model generates 1) a set of spatial coverages (maps) reflecting scenario outcomes of a variety of landscape variables, most notably land use/land cover, shoreline modifications, and population projections, and 2) a set of summary statistics describing landscape change variables summarized across spatial reporting units. Analyses were run on each of such sub-basins in the Puget Sound, and aggregated to providing Sound-wide results. This information is being used by PSNERP to project future impairment of ecosystem functions, goods, and services. The Puget Sound Nearshore Ecosystem project data also provide inputs to calculate aspects of future nearshore process degradation. Impairment and degradation are primary factors being used to define future conditions for the PSNERP General Investigation Study." AUTHOR'S DESCRIPTION: "In this report, we document the application of an alternative futures analysis framework that incorporates these capabilities to the analysis of alternative future trajectories in the Puget Sound region. This framework, Envision (Bolte et al, 2007; Hulse et al. 2008) is a spatially and temporally explicit, standards-based, open source toolset specifically designed to facilitate alternative futures analyses. It employs a multiagent-based modeling approach that contains a robust capability for defining alternative management strategies and scenarios, incorporating a variety of landscape change processes, and creating maps of alternative landscape trajectories, expressed though a variety of metrics defined in an application-specific way." ABOUT ENVISION (ENVISION WEBSITE): "Central to Envision, and conceived at the s | 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…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j: (1) density of the spiny lobster Panulirus argus" | 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…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(2) density of the queen conch Strombus gigas" | 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)." | [ABSTRACT: "This report describes the demonstration of the EcoAIM decision support framework and GIS-based tool. EcoAIM identifies and quantifies the ecosystem services provided by the natural resources at the Aberdeen Proving Ground (APG). A structured stakeholder process determined the mission and non-mission priorities at the site, elicited the natural resource management decision process, identified the stakeholders and their roles, and determine the ecosystem services of priority that impact missions and vice versa. The EcoAIM tool was customized to quantify in a geospatial context, five ecosystem services – vista aesthetics, landscape aesthetics, recreational opportunities, habitat provisioning for biodiversity and nutrient sequestration. The demonstration included a Baseline conditions quantification of ecosystem services and the effects of a land use change in the Enhanced Use Lease parcel in cantonment area (Scenario 1). Biodiversity results ranged widely and average scores decreased by 10% after Scenario 1. Landscape aesthetics scores increased by 10% after Scenario 1. Final scores did not change for recreation or nutrient sequestration because scores were outside the boundaries of the baseline condition. User feedback after the demonstration indicated positive reviews of EcoAIM as being useful and usable for land use decisions and particularly for use as a communication tool. " | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds." | AUTHOR Descriptions: "ESTIMAP consists of a set of separate components, each of which can be run separately. The models have been all framed in the ecosystem services cascade model [4] which connects ecosystem structure and functioning to human well-being through the flow of ecosystem services. At present, three modules are operational and described in further detail in this report: pollination, recreation and coastal protectionPeople can benefit from the opportunities provided by nature for recreational activities if they are able to reach them. The Recreation Opportunity spectrum was chosen as a method to map different degrees of service available according to their proximity to the people. Remoteness and proximity have been addressed in the second step of the analysis, in order to assess how the benefit (recreation) can be delivered to people. The proxy that has been identified couples information on both variables and has been mapped by classifying the EU into zones of proximity versus remoteness. From the ROS perspective this part takes into account remoteness and to some extent expected social experience. Distance from roads and residential areas have been used as inputs. The information on the road network is provided by the TeleAtlas database, and covers all paved roads in Europe. Gravel roads have been discarded to ease the processing. Residential areas are extracted from CORINE land cover classes “continuous urban fabric” and “discontinuous urban fabric”, therefore, all urban patches larger than 25 ha are considered in the mapping. In the current exercise there was the necessity to adapt overseas experiences to the peculiarities of the European continent, especially considering that the EU does not contain large wilderness areas like other continents " |
|
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
None identified | Not reported | European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | None identified | None identified | None identified | None identified | None identified | None identified | None reported | None identified | None reported | None |
|
Biophysical Context
|
Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | No additional description provided | Not applicable | 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 | No additional description provided | No additional description provided | No additional description provided | No additional description provided | Chesapeake bay coastal plain, elev. 60ft. | restored, enhanced and created wetlands | Conservation Reserve Program lands left to go fallow | Continential Scale |
|
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
No scenarios presented | No scenarios presented | No scenarios presented | Four carbon-planting systems including hardwood and mallee monoculture plantings, and mixed species ecological carbon plantings | No scenarios presented | Alternative future land management strategies (status quo, managed growth, unmanaged growth) | No scenarios presented | No scenarios presented | No scenarios presented | N/A | Sites, function or habitat focus | N/A | N.A. |
EM Relationship to Other EMs or Applications
|
EM ID
em.detail.idHelp
?
|
EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 |
EM-369
|
EM-458 | EM-459 | EM-549 | EM-647 |
EM-743
|
EM-846 | EM-939 |
|
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application | Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Runs are differentiated based on the the average annual biomass flux and carbon sequestration from two types of carbon plantings: 1) Tree-based monocultures of three different species (i.e., monoculture carbon planting) and 2) Diverse plantings of nine different native tree and shrub species (i.e., ecological carbon planting) |
Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method Only | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only |
|
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
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 | New or revised model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
em.detail.relatedEmHelp
?
|
EM ID
em.detail.idHelp
?
|
EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 |
EM-369
|
EM-458 | EM-459 | EM-549 | EM-647 |
EM-743
|
EM-846 | EM-939 |
|
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
Doc-260 | Doc-123 | Doc-255 | Doc-256 | Doc-257 | Doc-243 | Doc-246 | Doc-245 | Doc-303 | Doc-305 |
Doc-314 | Doc-47 ?Comment:Doc 314 is a secondary source. It is a webpage guide intended to provide support for developing an application using ENVISION. |
None | None | None | None | Doc-390 | Doc-405 | None |
|
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
EM-65 | EM-66 | EM-68 | EM-69 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | None | None | None | EM-12 | EM-333 | None | None | EM-584 | None | EM-718 | EM-734 | EM-760 | EM-761 | EM-763 | EM-764 | EM-766 | EM-767 | EM-768 | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-843 | EM-844 | EM-845 | EM-847 | EM-941 |
EM Modeling Approach
EM Relationship to Time
|
EM ID
em.detail.idHelp
?
|
EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 |
EM-369
|
EM-458 | EM-459 | EM-549 | EM-647 |
EM-743
|
EM-846 | EM-939 |
|
EM Temporal Extent
em.detail.tempExtentHelp
?
|
2007-2009 | 1987-1997 | 2000 | 2009-2050 | 1971-2006 | 2000-2060 | 2006-2007, 2010 | 2006-2007, 2010 | 35 yr | 2014 | 2010-2012 | 2008 | Not applicable |
|
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | Not applicable |
|
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
Not applicable | Not applicable | future time | future time | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable |
|
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
Not applicable | Not applicable | discrete | discrete | Not applicable | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable |
|
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
Not applicable | Not applicable | 1 | 1 | Not applicable | 1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable |
|
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable | Not applicable | Day | Month | Not applicable | Year | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
|
EM ID
em.detail.idHelp
?
|
EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 |
EM-369
|
EM-458 | EM-459 | EM-549 | EM-647 |
EM-743
|
EM-846 | EM-939 |
|
Bounding Type
em.detail.boundingTypeHelp
?
|
Physiographic or Ecological | Watershed/Catchment/HUC | Geopolitical | Physiographic or Ecological | Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Not applicable | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | No location (no locational reference given) |
|
Spatial Extent Name
em.detail.extentNameHelp
?
|
Central French Alps | Upper Mississippi River basin; St. Croix River Watershed | EU-15 | Agricultural districts of the state of South Australia | Puget Sound Region | Puget Sound watershed | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Not applicable | Aberdeen Proving Ground | Wetlands in idaho | Piedmont Ecoregion | Not applicable |
|
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
10-100 km^2 | 100,000-1,000,000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 10,000-100,000 km^2 | 10,000-100,000 km^2 | 100-1000 km^2 | 100-1000 km^2 | Not applicable | 100-1000 km^2 | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | >1,000,000 km^2 |
Spatial Distribution of Computations
|
EM ID
em.detail.idHelp
?
|
EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 |
EM-369
|
EM-458 | EM-459 | EM-549 | EM-647 |
EM-743
|
EM-846 | EM-939 |
|
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
spatially distributed (in at least some cases) ?Comment:500m x 500m is also used for some computations. The evaluation does include some riparian buffers which are linear features along streams. |
spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
|
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
area, for pixel or radial feature | NHDplus v1 | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Irregular | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | Not applicable | Not applicable | area, for pixel or radial feature |
|
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
20 m x 20 m | NHDplus v1 | 10 km x 10 km | 1 ha x 1 ha | 200m x 200m | Varies | 10 m x 10 m | 10 m x 10 m | Not applicable | 100m x 100m | Not applicable | Not applicable | Pixel size |
EM Structure and Computation Approach
|
EM ID
em.detail.idHelp
?
|
EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 |
EM-369
|
EM-458 | EM-459 | EM-549 | EM-647 |
EM-743
|
EM-846 | EM-939 |
|
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Analytic | Numeric | Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Numeric | Numeric | Numeric | Analytic | Numeric |
|
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
|
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Model Checking Procedures Used
|
EM ID
em.detail.idHelp
?
|
EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 |
EM-369
|
EM-458 | EM-459 | EM-549 | EM-647 |
EM-743
|
EM-846 | EM-939 |
|
Model Calibration Reported?
em.detail.calibrationHelp
?
|
No | Yes | No | Yes | No | Unclear | Yes | Yes | Not applicable |
No ?Comment:Nutrient sequestion submodel ( EPA's P8 model has been long used) |
No | Yes | No |
|
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
Yes | Yes | No | No | No | Not applicable | No | No | Not applicable | Not applicable | No | No | Not applicable |
|
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
|
|
None | None | None | None | None | None | None | None | None | None | None |
|
Model Operational Validation Reported?
em.detail.validationHelp
?
|
No | No | No | No | No | Not applicable | Yes | Yes | Unclear | No | No | No | Unclear |
|
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
No | No | Yes | No | No | Not applicable | No | No | Not applicable | No | No | No | No |
|
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
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. |
Yes | No | No | Not applicable | No | No | Not applicable |
Unclear ?Comment:Just cannot tell, but no mention of sensitivity was made. |
No | Yes | Yes |
|
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
em.detail.relationToSpaceTerrestrialHelp
?
| EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 |
EM-369
|
EM-458 | EM-459 | EM-549 | EM-647 |
EM-743
|
EM-846 | EM-939 |
|
|
|
|
|
|
None | None | None |
|
|
|
|
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
?
| EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 |
EM-369
|
EM-458 | EM-459 | EM-549 | EM-647 |
EM-743
|
EM-846 | EM-939 |
| None | None | None | None | None |
|
|
|
None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
|
EM ID
em.detail.idHelp
?
|
EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 |
EM-369
|
EM-458 | EM-459 | EM-549 | EM-647 |
EM-743
|
EM-846 | EM-939 |
|
Centroid Latitude
em.detail.ddLatHelp
?
|
45.05 | 42.5 | 50.01 | -34.9 | 48 | 47.58 | 17.73 | 17.73 | Not applicable | 39.46 | 44.06 | 36.23 | Not applicable |
|
Centroid Longitude
em.detail.ddLongHelp
?
|
6.4 | -90.63 | 4.67 | 138.7 | -123 | -122.32 | -64.77 | -64.77 | Not applicable | 76.12 | -114.69 | -81.9 | Not applicable |
|
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | Not applicable |
|
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated | Estimated | Not applicable |
Environments and Scales Modeled
|
EM ID
em.detail.idHelp
?
|
EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 |
EM-369
|
EM-458 | EM-459 | EM-549 | EM-647 |
EM-743
|
EM-846 | EM-939 |
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Forests | Agroecosystems | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Agroecosystems | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Forests | Created Greenspace | Grasslands | Scrubland/Shrubland | Inland Wetlands | Grasslands | Terrestrial Environment (sub-classes not fully specified) |
|
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Subalpine terraces, grasslands, and meadows | None | Arable lands in near-stream environments | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Terrestrial environment surrounding a large estuary | Pacific NW US region, coastal to montane, urban to rural | Coral reefs | Coral reefs | Agroecosystems | Coastal Plain | created, restored and enhanced wetlands | grasslands | Not applicable |
|
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Not applicable | Ecosystem | 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 | 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 |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
em.detail.nameOfOrgsOrGroupsHelp
?
|
EM ID
em.detail.idHelp
?
|
EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 |
EM-369
|
EM-458 | EM-459 | EM-549 | EM-647 |
EM-743
|
EM-846 | EM-939 |
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Community | Not applicable | Not applicable | Species | Not applicable | Not applicable | Species | Species | Not applicable | Not applicable | Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
?
| EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 |
EM-369
|
EM-458 | EM-459 | EM-549 | EM-647 |
EM-743
|
EM-846 | EM-939 |
| None Available | None Available | None Available |
|
None Available | None Available |
|
|
None Available | None Available | None Available |
|
None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
em.detail.cicesHelp
?
| EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 |
EM-369
|
EM-458 | EM-459 | EM-549 | EM-647 |
EM-743
|
EM-846 | EM-939 |
| None |
|
None |
|
|
None |
|
|
|
|
|
|
|
<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
fegs1Help
?
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
?
| EM-70 | EM-91 | EM-92 |
EM-129
|
EM-326 |
EM-369
|
EM-458 | EM-459 | EM-549 | EM-647 |
EM-743
|
EM-846 | EM-939 |
| None | None | None |
|
|
None |
|
|
|
|
None |
|
None |