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-85 | EM-94 | EM-97 | EM-126 |
EM-129 ![]() |
EM-326 |
EM-380 ![]() |
EM-432 | EM-438 | EM-453 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-885 | EM-961 |
EM Short Name
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Area and hotspots of flow regulation, South Africa | Reduction in pesticide runoff risk, Europe | AnnAGNPS, Kaskaskia River watershed, IL, USA | Annual profit from agriculture, South Australia | 3-PG, South Australia | ARIES flood regulation, Puget Sound Region, USA | VELMA plant-soil, Oregon, USA | Nitrogen fixation rates, Guánica Bay, Puerto Rico | InVESTv3.0 Nutrient retention, Guánica Bay | Reef density of E. striatus, St. Croix, USVI | Value of finfish, St. Croix, USVI | InVEST fisheries, lobster, South Africa | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | RBI Spatial Analysis Method | Regional Human well being index for U.S. | Drainage water recycling, Midwest, USA |
EM Full Name
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Area and hotspots of water flow regulation, South Africa | Reduction in pesticide runoff risk, Europe | AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | Annual profit from agriculture, South Australia | 3-PG (Physiological Principles Predicting Growth), South Australia | ARIES (Artificial Intelligence for Ecosystem Services) Flood Regulation, Puget Sound Region, Washington, USA | VELMA (Visualizing Ecosystems for Land Management Assessments) plant-soil, Oregon, USA | Nitrogen fixation rates, Guánica Bay, Puerto Rico, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs)v3.0 Nutrient retention, Guánica Bay, Puerto Rico, USA | Relative density of Epinephelus striatus (on reef), St. Croix, USVI | Relative value of finfish (on reef), St. Croix, USVI | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa | DeNitrification-DeComposition simulation of N2O flux Ireland | Rapid Benefit Indicator (RBI) Spatial Analysis Toolset Method | Human well being index for geographic regions, United States | Drainage water recycling, Midwest, US |
EM Source or Collection
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None | None | US EPA | None | None | ARIES | US EPA | US EPA | US EPA | InVEST | US EPA | US EPA | InVEST | None | None | US EPA | None |
EM Source Document ID
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271 | 255 | 137 | 243 | 243 | 302 | 317 |
338 ?Comment:WE received a draft copy prior to journal publication that was agency reviewed. |
338 | 335 | 335 |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
358 | 367 | 421 | 446 |
Document Author
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Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Lautenbach, S., Maes, J., Kattwinkel, M., Seppelt, R., Strauch, M., Scholz, M., Schulz-Zunkel, C., Volk, M., Weinert, J. and Dormann, C. | Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Crossman, N. D., Bryan, B. A., and Summers, D. 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. | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Bousquin, J., Mazzotta M., and W. Berry | Smith, L.M., Harwell, L.C., Summers, J.K., Smith, H.M., Wade, C.M., Straub, K.R. and J.L. Case | Reinhart, B.D., Frankenberger, J.R., Hay, C.H., and Helmers, J.M. |
Document Year
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2008 | 2012 | 2011 | 2011 | 2011 | 2014 | 2013 | 2017 | 2017 | 2014 | 2014 | 2018 | 2010 | 2017 | 2014 | 2019 |
Document Title
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Mapping ecosystem services for planning and management | Mapping water quality-related ecosystem services: concepts and applications for nitrogen retention and pesticide risk reduction | AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | Carbon payments and low-cost conservation | Carbon payments and low-cost conservation | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | 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 | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals | 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 | Rapid Benefit Indicators (RBI) Spatial Analysis Toolset - Manual. | A U.S. Human Well-being index (HWBI) for multiple scales: linking service provisioning to human well-being endpoints (2000-2010) | Simulated water quality and irrigation benefits from drainage wter recycling at two tile-drained sites in the U.S. Midwest |
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 | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report | Published EPA report | Published journal manuscript |
EM ID
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EM-85 | EM-94 | EM-97 | EM-126 |
EM-129 ![]() |
EM-326 |
EM-380 ![]() |
EM-432 | EM-438 | EM-453 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-885 | EM-961 |
Not applicable | Not applicable | https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | Not applicable | http://www.csiro.au/products/3PGProductivity#a1 | http://aries.integratedmodelling.org/ | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable | http://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | http://www.dndc.sr.unh.edu | Not applicable | Not applicable | Not applicable | |
Contact Name
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Benis Egoh | Sven Lautenbach | Yongping Yuan | Neville D. Crossman | Anders Siggins | Ken Bagstad | Alex Abdelnour | Susan H. Yee | Susan H. Yee | Susan H. Yee | Susan H. Yee | Michelle Ward | M. Abdalla | Justin Bousquin | Lisa Smith | Benjamin Reinhart |
Contact Address
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Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany | U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia | Not reported | Geosciences and Environmental Change Science Center, US Geological Survey | Dept. of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | 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 | ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | US EPA, Office of Research and Development, National health and environmental Effects Lab, Gulf Ecology Division, Gulf Breeze, FL 32561 | 1 Sabine Island Dr, Gulf Breeze, FL 32561 | Agricultural & Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA |
Contact Email
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Not reported | sven.lautenbach@ufz.de | yuan.yongping@epa.gov | neville.crossman@csiro.au | Anders.Siggins@csiro.au | kjbagstad@usgs.gov | abdelnouralex@gmail.com | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | m.ward@uq.edu.au | abdallm@tcd.ie | bousquin.justin@epa.gov | smith.lisa@epa.gov | breinhar@purdue.edu |
EM ID
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EM-85 | EM-94 | EM-97 | EM-126 |
EM-129 ![]() |
EM-326 |
EM-380 ![]() |
EM-432 | EM-438 | EM-453 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-885 | EM-961 |
Summary Description
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AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Water flow regulation is a function of the storage and retention components of the water supply service (de Groot et al., 2002). The ability of a catchment to regulate flows is directly related to the volume of water that is retained or stored in the soil and underlying aquifers as moisture or groundwater; and the infiltration rate of water which replenishes the stored water (Kittredge, 1948; Farvolden, 1963). Groundwater contribution to surface runoff is the most direct measure of the water regulation function of a catchment. Data on the percentage contribution of groundwater to baseflows were obtained from DWAF (2005) per quaternary catchment and expressed as a percentage of total surface runoff, the range and hotspot being defined as areas with at least 10% and 30%, respectively (Colvin et al., 2007)." | AUTHOR'S DESCRIPTION: "We used a spatially explicit model to predict the potential exposure of small streams to insecticides (run-off potential – RP) as well as the resulting ecological risk (ER) for freshwater fauna on the European scale (Schriever and Liess 2007; Kattwinkel et al. 2011)...The recovery of community structure after exposure to insecticides is facilitated by the presence of undisturbed upstream stretches that can act as sources for recolonization (Niemi et al. 1990; Hatakeyama and Yokoyama 1997). In the absence of such sources for recolonization, the structure of the aquatic community at sites that are exposed to insecticides differs significantly from that of reference sites (Liess and von der Ohe 2005)...Hence, we calculated the ER depending on RP for insecticides and the amount of recolonization zones. ER gives the percentage of stream sites in each grid cell (10 × 10 km) in which the composition of the aquatic community deviated from that of good ecological status according to the WFD. In a second step, we estimated the service provided by the environment comparing the ER of a landscape lacking completely recolonization sources with that of the actual landscape configuration. Hence, the ES provided by non-arable areas (forests, pastures, natural grasslands, moors and heathlands) was calculated as the reduction of ER for sensitive species. The service can be thought of as a habitat provisioning/nursery service that leads to an improvement of ecological water quality." | AUTHORS' DESCRIPTION: "AnnAGNPS is an advanced simulation model developed by the USDA-ARS and Natural Resource Conservation Services (NRCS) to help evaluate watershed response to agricultural management practices. It is a continuous simulation, daily time step, pollutant loading model designed to simulate water, sediment and chemical movement from agricultural watersheds.p. 198" | 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 agriculture and from carbon plantings." 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). In this context, opportunity cost is usually expressed as the profit from agricultural production…We based our calculations of agricultural profit on Bryan et al. (2009), who calculated profit at full equity (i.e., economic return to land, capital, and management, exclusive of financial debt). We calculated an annual profit at full equity (PFEc) layer for each commodity (c) in the set of agricultural commodities (C), where C is wheat, field peas, beef cattle, or sheep." | 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." | 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 plant-soil model (Figure (A3)) that simulates ecosystem carbon storage and the cycling of C and N between a plant biomass layer and the active soil pools. Specifically, the plant-soil model simulates the interaction among aboveground plant biomass, soil organic carbon (SOC), soil nitrogen including dissolved nitrate (NO3), ammonium (NH4), and organic nitrogen, as well as DOC (equations (A7)–(A12)). Daily atmospheric inputs of wet and dry nitrogen deposition are accounted for in the ammonium pool of the shallow soil layer (equation (A13)). Uptake of ammonium and nitrate by plants is modeled using a Type II Michaelis-Menten function (equation (A14)). Loss of plant biomass is simulated through a density-dependent mortality. The mortality rate and the nitrogen uptake rate mimic the exponential increase in biomass mortality and the accelerated growth rate, respectively, as plants go through succession and reach equilibrium (equations (A14)–(A18)). Vertical transport of nutrients from one layer to another in a soil column is a function of water drainage (equations (A19)–(A22)). Decomposition of SOC follows first-order kinetics controlled by soil temperature and moisture content as described in the terrestrial ecosystem model (TEM) of Raich et al. [1991] (equations (A23)–(A26)). Nitrification (equations (A27)–(A30)) and denitrification (equations (A31)–(A34)) were simulated using the equations from the generalized model of N2 and N2O production of Parton et al. [1996, 2001] and Del Grosso et al. [2000]. [12] The soil column model is placed within a catchment framework to create a spatially distributed model applicable to watersheds and landscapes. Adjacent soil columns interact with each other through the downslope lateral transport of water and nutrients (Figure (A1)). Surface and subsurface lateral flow are routed using a multiple flow direction method [Freeman, 1991; Quinn et al., 1991]. As with vertical drainage of soil water, lateral subsurface downslope flow i | AUTHOR'S DESCRIPTION: " …In Guánica Bay watershed, Puerto Rico, deforestation and drainage of a large lagoon have led to sediment, contaminant, and nutrient transport into the bay, resulting in declining quality of coral reefs. A watershed management plan is currently being implemented to restore reefs through a variety of proposed actions…After the workshops, fifteen indicators of terrestrial ecosystem services in the watershed and four indicators in the coastal zone were identified to reflect the wide range of stakeholder concerns that could be impacted by management decisions. Ecosystem service production functions were applied to quantify and map ecosystem services supply in the Guánica Bay watershed, as well as an additional highly engineered upper multi-watershed area connected to the lower watershed via a series of reservoirs and tunnels,…” AUTHOR''S DESCRIPTION: "The U.S. Coral Reef Task Force (CRTF), a collaboration of federal, state and territorial agencies, initiated a program in 2009 to better incorporate land-based sources of pollution and socio-economic considerations into watershed strategies for coral reef protection (Bradley et al., 2016)...Baseline measures for relevant ecosystem services were calculated by parameterizing existing methods, largely based on land cover (Egoh et al., 2012; Martinez- Harms and Balvanera, 2012), with relevant rates of ecosystem services production for Puerto Rico, and applying them to map ecosystem services supply for the Guánica Bay Watershed...The Guánica Bay watershed is a highly engineered watershed in southwestern Puerto Rico, with a series of five reservoirs and extensive tunnel systems artificially connecting multiple mountainous sub-watersheds to the lower watershed of the Rio Loco, which itself is altered by an irrigation canal and return drainage ditch that diverts water through the Lajas Valley (PRWRA, 1948)...For each objective, a translator of ecosystem services production, i.e., ecological production function, was used to quantify baseline measurements of ecosystem services supply from land use/land cover (LULC) maps for watersheds across Puerto Rico...Two additional metrics, nitrogen fixation and rates of carbon sequestration into soil and sediment, were also calculated as potential measures of soil quality and agricultural productivity. Carbon sequestration and nitrogen fixation rates were assigned to each land cover class" | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "Nutrient retention was estimated by first calculating water yield and establishing the quantity of nitrogen or phosphorus retained by different land cover classes using a water purification model (InVEST 3.0.0; Tallis et al., 2013). Different land cover classes were assumed to have different capacities for retaining nutrients, depending on the efficiency of vegetation in removing either nitrogen or phosphorus and the rates of nitrogen or phosphorus loading." “Use of other models in conjunction with this model:Average runoff per pixel modeled here were derived from the InVEST Water Yield model" | 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 recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…Synthesis of scientific literature and expert opinion can be used to estimate the relative potential for recreational opportunities across different benthic habitat types (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to recreational opportunities as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative recreational opportunity 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: density of E. striatus" | 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:...(5) value of finfish," | AUTHOR'S DESCRIPTION: "Here we develop a method for assessing future scenarios of environmental management change that improve coastal ecosystem services and thereby, support the success of the SDGs. We illustrate application of the method using a case study of South Africa’s West Coast Rock Lobster fishery within the Table Mountain National Park (TMNP) Marine Protected Area...We calculated the retrospective and current value of the West Coast Rock Lobster fishery using published and unpublished data from various sources and combined the market worth of landed lobster from recreational fishers, small-scale fisheries (SSF), large-scale fisheries (LSF) and poachers. Then using the InVEST tool, we combined data to build scenarios that describe possible futures for the West Coast Rock Lobster fishery (see Table 1). The first scenario, entitled ‘Business as Usual’ (BAU), takes the current situation and most up-to-date data to model the future if harvest continues at the existing rate. The second scenario is entitled ‘Redirect the Poachers’ (RP), which attempts to model implementation of strict management, whereby poaching is minimised from the Marine Protected Area and other economic and nutritional sources are made available through government initiatives. The third scenario, entitled ‘Large Scale Cutbacks’ (LSC), excludes large-scale fisheries from harvesting West Coast Rock Lobster within the TMNP Marine Protected Area." | 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. 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. | AUTHOR DESCRIPTION: "The Rapid Benefits Indicators (RBI) approach consists of five steps and is outlined in Assessing the Benefits of Wetland Restoration – A Rapid Benefits Indicators Approach for Decision Makers, hereafter referred to as the “guide.” The guide presents the assessment approach, detailing each step of the indicator development process and providing an example application in the “Step in Action” pages. The spatial analysis toolset is intended to be used to analyze existing spatial information to produce metrics for many of the indicators developed in that guide. This spatial analysis toolset manual gives directions on the mechanics of the tool and its data requirements, but does not detail the reasoning behind the indicators and how to use results of the assessment; this information is found in the guide. " | Executive summary: "The HWBI is a composite assessment covering 8 domains based on 25 indicators measured using 80 different metrics. Service flow and stock assessments include 7 economic services (23 indicators, 40 metrics), 5 ecosystem services (8 indicators, 24 metrics) and 10 social services (37 indicators, 76 metrics). Data from 64 data sources were included in the HWBI and services provisioning characterizations (Fig. ES-3). For each U.S. county, state, and GSS region, data were acquired or imputed for the 2000-2010 time period resulting in over 1.5 million data points included in the full assessment linking service flows to well-being endpoints. The approaches developed for calculation of the HWBI, use of relative importance values, service stock characterization and functional modeling are transferable to smaller scales and specific population groups. Additionally, tracked over time, the HWBI may be useful in evaluating the sustainability of decisions in terms of EPA’s Total Resources Impact Outcome (TRIO) approaches." | [Enter up to 65000 characters] |
Specific Policy or Decision Context Cited
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None identified | European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | Not reported | None identified | None identified | None identified | None identified | None provided | Improving water quality | None identified | None identified | Future rock lobster fisheries management | climate change | None identified | None reported | None |
Biophysical Context
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Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | Not applicable | Upper Mississipi River basin, elevation 142-194m, | 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. | Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm. | No additional description provided | Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter. | No additional description provided | No additional description provided | No additional description provided | No additional description provided | No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | wetlands | Not applicable | None |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | No scenarios presented | Four carbon-planting systems including hardwood and mallee monoculture plantings, and mixed species ecological carbon plantings | No scenarios presented | Forest management (harvest/no harvest) | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Fisheries exploitation; fishing vulnerability (of age classes) | fertilization | N/A | geographic region | None |
EM ID
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EM-85 | EM-94 | EM-97 | EM-126 |
EM-129 ![]() |
EM-326 |
EM-380 ![]() |
EM-432 | EM-438 | EM-453 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-885 | EM-961 |
Method Only, Application of Method or Model Run
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Method + Application | 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 + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method + Application | None |
New or Pre-existing EM?
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New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | New or revised model | New or revised model | None |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-85 | EM-94 | EM-97 | EM-126 |
EM-129 ![]() |
EM-326 |
EM-380 ![]() |
EM-432 | EM-438 | EM-453 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-885 | EM-961 |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-271 |
Doc-254 | Doc-256 ?Comment:Document 254 was also used as a source document for this EM |
Doc-142 | Doc-244 | Doc-243 | Doc-246 | Doc-245 | Doc-303 | Doc-305 | Doc-13 | Doc-317 | None | Doc-309 | Doc-205 | None | None | None | None | None | Doc-418 | None |
EM ID for related EM
em.detail.relatedEmEmIdHelp
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EM-86 | EM-87 | EM-88 | None | None | None | None | None | EM-375 | EM-379 | EM-884 | EM-883 | EM-887 | None | EM-363 | EM-112 | None | None | None | EM-593 | None | None | None |
EM Modeling Approach
EM ID
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EM-85 | EM-94 | EM-97 | EM-126 |
EM-129 ![]() |
EM-326 |
EM-380 ![]() |
EM-432 | EM-438 | EM-453 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-885 | EM-961 |
EM Temporal Extent
em.detail.tempExtentHelp
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Not reported | 2000 | 1980-2006 | 2002-2008 | 2009-2050 | 1971-2006 | 1969-2008 | 1978 - 2009 | 1980 - 2013 | 2006-2007, 2010 | 2006-2007, 2010 | 1986-2115 | 1961-1990 | Not applicable | 2000-2010 | None |
EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | None |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | future time | Not applicable | other or unclear (comment) | Not applicable | Not applicable | future time | both | Not applicable | Not applicable | None |
EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete | Not applicable | discrete | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | None |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | 1 | Not applicable | 1 | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | None |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Month | Not applicable | Day | Not applicable | Year | Not applicable | Not applicable | Year | Day | Not applicable | Not applicable | None |
EM ID
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EM-85 | EM-94 | EM-97 | EM-126 |
EM-129 ![]() |
EM-326 |
EM-380 ![]() |
EM-432 | EM-438 | EM-453 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-885 | EM-961 |
Bounding Type
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Geopolitical | Geopolitical | Watershed/Catchment/HUC | Physiographic or Ecological | Physiographic or Ecological | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Geopolitical | Point or points | Not applicable | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) |
Spatial Extent Name
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South Africa | EU-27 | East Fork Kaskaskia River watershed basin | Agricultural districts of the state of South Australia | Agricultural districts of the state of South Australia | Puget Sound Region | H. J. Andrews LTER WS10 | Guanica Bay watershed | Guanica Bay Study Area | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Table Mountain National Park Marine Protected Area | Oak Park Research centre | Not applicable | Continental U.S. | Western & Eastern Corn Belt Plains |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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>1,000,000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | 10,000-100,000 km^2 | 10-100 ha | 100-1000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 1-10 ha | Not applicable | >1,000,000 km^2 | 100,000-1,000,000 km^2 |
EM ID
em.detail.idHelp
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EM-85 | EM-94 | EM-97 | EM-126 |
EM-129 ![]() |
EM-326 |
EM-380 ![]() |
EM-432 | EM-438 | EM-453 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-885 | EM-961 |
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) | 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 lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | None |
Spatial Grain Type
em.detail.spGrainTypeHelp
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other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | 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) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | None |
Spatial Grain Size
em.detail.spGrainSizeHelp
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Distributed by catchments with average size of 65,000 ha | 10 km x 10 km | 1 km^2 | 1 ha | 1 ha x 1 ha | 200m x 200m | 30 m x 30 m surface pixel and 2-m depth soil column | HUC | 30 m x 30 m | 10 m x 10 m | 10 m x 10 m | Not applicable | Not applicable | Not reported | county | None |
EM ID
em.detail.idHelp
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EM-85 | EM-94 | EM-97 | EM-126 |
EM-129 ![]() |
EM-326 |
EM-380 ![]() |
EM-432 | EM-438 | EM-453 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-885 | EM-961 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Analytic | Numeric | Analytic | Numeric | Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Numeric | Numeric | Analytic | Numeric | * |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | None |
Statistical Estimation of EM
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None |
EM ID
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EM-85 | EM-94 | EM-97 | EM-126 |
EM-129 ![]() |
EM-326 |
EM-380 ![]() |
EM-432 | EM-438 | EM-453 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-885 | EM-961 |
Model Calibration Reported?
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No | No | No | No | Yes | No | Yes | No | No | Yes | Yes | No | Yes | Not applicable | No | None |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | No | No | No | No | No | No | No | No | No | No |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
Not applicable | No | None |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None | None | None | None | None | None | None | None |
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None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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No | Yes | Yes | No | No | No | No | No | No | Yes | Yes |
Yes ?Comment:A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data. |
Yes | Not applicable | No | None |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | Yes | No | No | No | No | No | No | No | No | No | No | Not applicable | Unclear | None |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | Unclear | No | No | No | Yes | No | No | No | No | No | No | Not applicable | Yes | None |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Yes | None |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-85 | EM-94 | EM-97 | EM-126 |
EM-129 ![]() |
EM-326 |
EM-380 ![]() |
EM-432 | EM-438 | EM-453 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-885 | EM-961 |
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None | None | None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-85 | EM-94 | EM-97 | EM-126 |
EM-129 ![]() |
EM-326 |
EM-380 ![]() |
EM-432 | EM-438 | EM-453 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-885 | EM-961 |
None | None | None | None | None | None | None | None | None |
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None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-85 | EM-94 | EM-97 | EM-126 |
EM-129 ![]() |
EM-326 |
EM-380 ![]() |
EM-432 | EM-438 | EM-453 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-885 | EM-961 |
Centroid Latitude
em.detail.ddLatHelp
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-30 | 50.53 | 38.69 | -34.9 | -34.9 | 48 | 44.25 | 17.96 | 17.97 | 17.73 | 17.73 | -34.18 | 52.86 | Not applicable | 39.83 | None |
Centroid Longitude
em.detail.ddLongHelp
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25 | 7.6 | -89.1 | 138.7 | 138.7 | -123 | -122.33 | -67.02 | -66.93 | -64.77 | -64.77 | 18.35 | 6.54 | Not applicable | -98.58 | None |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | Not applicable | WGS84 | None |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Provided | Provided | Not applicable | Estimated | None |
EM ID
em.detail.idHelp
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EM-85 | EM-94 | EM-97 | EM-126 |
EM-129 ![]() |
EM-326 |
EM-380 ![]() |
EM-432 | EM-438 | EM-453 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-885 | EM-961 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Agroecosystems | Agroecosystems | Forests | Agroecosystems | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Ground Water | Forests | Inland Wetlands | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Aquatic Environment (sub-classes not fully specified) | Inland Wetlands | Near Coastal Marine and Estuarine | Open Ocean and Seas | Forests | Agroecosystems | Created Greenspace | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Agroecosystems | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Not reported | Streams and near upstream environments | Row crop agriculture in Kaskaskia river basin | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Terrestrial environment surrounding a large estuary | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | Tropical terrestrial | 13 LULC were used | Coral reefs | Coral reefs | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | farm pasture | Restored wetlands | All land of the continental US | Plains |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is coarser 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 corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-85 | EM-94 | EM-97 | EM-126 |
EM-129 ![]() |
EM-326 |
EM-380 ![]() |
EM-432 | EM-438 | EM-453 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-885 | EM-961 |
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Not applicable | Guild or Assemblage | Species | Not applicable | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Guild or Assemblage | Individual or population, within a species | Not applicable | Not applicable | Not applicable | None |
Taxonomic level and name of organisms or groups identified
EM-85 | EM-94 | EM-97 | EM-126 |
EM-129 ![]() |
EM-326 |
EM-380 ![]() |
EM-432 | EM-438 | EM-453 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-885 | EM-961 |
None Available | None Available | None Available |
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None Available | None Available | None Available | None Available |
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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-85 | EM-94 | EM-97 | EM-126 |
EM-129 ![]() |
EM-326 |
EM-380 ![]() |
EM-432 | EM-438 | EM-453 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-885 | EM-961 |
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None |
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None |
<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-85 | EM-94 | EM-97 | EM-126 |
EM-129 ![]() |
EM-326 |
EM-380 ![]() |
EM-432 | EM-438 | EM-453 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-885 | EM-961 |
None | None |
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None |
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None |
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None |
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None | None |