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: Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa (EM-541)
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EM Identification (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | Application of existing model | New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-541 | Application of existing model | New or revised model | New or revised model | Continuation of trends | |
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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 | KINEROS2, River Ravna watershed, Bulgaria | ARIES flood regulation, Puget Sound Region, USA | Nitrogen fixation rates, Guánica Bay, Puerto Rico | 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 | CommunityViz, Albany county, Wyoming |
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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 | KINEROS (Kinematic runoff and erosion model) v2, River Ravna watershed,Bulgaria | ARIES (Artificial Intelligence for Ecosystem Services) Flood Regulation, Puget Sound Region, Washington, USA | Nitrogen fixation rates, 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 | Wyoming Community Viz TM Partnership Phase I Pilot: Aquifer Protection and Community Viz TM in Albany County, Wyoming. |
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EM Source or Collection
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None | None | US EPA | None | None | EU Biodiversity Action 5 | ARIES | US EPA | US EPA | US EPA | InVEST | None | * | US EPA | None | * |
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EM Source Document ID
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271 | 255 | 137 | 243 | 243 |
248 ?Comment:Document 277 is also a source document for this EM |
302 |
338 ?Comment:WE received a draft copy prior to journal publication that was agency reviewed. |
335 | 335 |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
358 | 367 | 421 | 446 |
479 ?Comment:Published as a report by the University of Wyoming, but no record of peer review. |
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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. | Nedkov, S., Burkhard, B. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | 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. | Lieske, S. N., Mullen, S., Knapp, M., & Hamerlinck, J. D. |
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Document Year
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2008 | 2012 | 2011 | 2011 | 2011 | 2012 | 2014 | 2017 | 2014 | 2014 | 2018 | 2010 | 2017 | 2014 | 2019 | 2003 |
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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 | Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | 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 | Wyoming Community Viz TM Partnership Phase I Pilot: Aquifer Protection and Community Viz TM in Albany County, Wyoming |
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Document Status
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* | * | * | * | * | * | * | * | * | * | Peer reviewed and published | * | * | * | * | Not peer reviewed but is published (explain in Comment) |
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Comments on Status
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* | * | * | * | * | * | * | * | * | * | Published journal manuscript | * | Published EPA report | Published EPA report | * | Published report |
Software and Access (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | Application of existing model | New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-541 | Application of existing model | New or revised model | New or revised model | Continuation of trends | |
| 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://www.tucson.ars.ag.gov/agwa/ | http://aries.integratedmodelling.org/ | Not applicable | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | http://www.dndc.sr.unh.edu | Not applicable | Not applicable | Not applicable | https://communityviz.com/ | |
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Contact Name
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Benis Egoh | Sven Lautenbach | Yongping Yuan | Neville D. Crossman | Anders Siggins | David C. Goodrich | Ken Bagstad | Susan H. Yee | Susan H. Yee | Susan H. Yee | Michelle Ward | M. Abdalla | Justin Bousquin | Lisa Smith | Benjamin Reinhart | Scott Lieske |
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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 | USDA - ARS Southwest Watershed Research Center, 2000 E. Allen Rd., Tucson, AZ 85719 | Geosciences and Environmental Change Science Center, US Geological Survey | 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 | Department of Agricultural & Applied Economics University of Wyoming, Laramie WY 82071 |
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Contact Email
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Not reported | sven.lautenbach@ufz.de | yuan.yongping@epa.gov | neville.crossman@csiro.au | Anders.Siggins@csiro.au | agwa@tucson.ars.ag.gov | kjbagstad@usgs.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 | lieske@uwyo.edu |
EM Description (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | Application of existing model | New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-541 | Application of existing model | New or revised model | New or revised model | Continuation of trends | |
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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: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. In this study, the capacities of different ecosystems to regulate floods were assessed through investigations of water retention functions of the vegetation and soil cover. The use of the catchment based hydrologic model KINEROS and the GIS AGWA tool provided data about peak rivers’ flows and the capability of different land cover types to “capture” and regulate some parts of the water." AUTHOR'S DESCRIPTION: "KINEROS is a distributed, physically based, event model describing the processes of interception, dynamic infiltration, surface runoff and erosion from watersheds characterized by predominantly overland flow. The watershed is conceptualized as a cascade and the channels, over which the flow is routed in a top–down approach, are using a finite difference solution of the one-dimensional kinematic wave equations (Semmens et al., 2005). Rainfall excess, which leads to runoff, is defined as the difference between precipitation amount and interception and infiltration depth. The rate at which infiltration occurs is not constant but depends on the rainfall rate and the accumulated infiltration amount, or the available moisture condition of the soil. The AGWA tool is a multipurpose hydrologic analysis system addressed to: (1) provide a simple, direct and repeatable method for hydrologic modeling; (2) use basic, attainable GIS data; (3) be compatible with other geospatial basin-based environmental analysis software; and (4) be useful for scenario development and alternative future simulation work at multiple scales (Miller et al., 2002). AGWA provides the functionality to conduct the processes of modeling and assessment for…KINEROS." | 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." | 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" | 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] | The Wyoming Community VizTM Partnership was established in 2001 to promote the use of geographic information system-based planning support systems and related decision support technologies in community land-use planning and economic development activities in the State of Wyoming. Partnership members include several state agencies, local governments and several nongovernment organizations. Partnership coordination is provided by the Wyoming Rural Development Council. Research and technical support is coordinated by the Wyoming Geographic Information Science Center’s Spatial Decision Support System Research Program at the University of Wyoming. In June 2002, the Partnership initiated a three-phase plan to promote Community VizTM based planning support systems in Wyoming. Phase I of the Partnership plan was a “proof of concept” pilot project set in Albany County in southeastern Wyoming. The goal of the project was to demonstrate the application of Community VizTM to a Wyoming-specific issue (in this case, aquifer protection) and to determine potential challenges for broader adoption in terms of data requirements, computing infrastructure and technological expertise. The results of the Phase I pilot project are detailed in this report. Efforts are currently underway to secure funding for Phase II of the plan, which expands the use of Community VizTM into four additional Wyoming communities. Specific Phase II objectives are to expand the type and number of issues addressed by Community VizTM and increase the use of Community VizTM in the planning process. As a part of Phase II the Partnership will create a technical assistance network aimed at assisting communities with the technical challenges in applying the software to their planning issues. The third phase will expand the program to more communities in the state, maintain the technical assistance network, and monitor the impact of Community VizTM on the planning process. |
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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 | None identified | None identified | Future rock lobster fisheries management | climate change | None identified | None reported | None | None provided |
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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. | Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak. | * | * | * | * | No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | wetlands | Not applicable | None | Groundwater recharge area, City of Laramie |
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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 | 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 | Continuation of trends |
EM Relationship to Other EMs or Applications
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EM ID
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New or revised model | Application of existing model | New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-541 | Application of existing model | New or revised model | New or revised model | Continuation of trends | |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application |
Method + Application (multiple runs exist) ?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 | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) | Method + Application | Method Only | Method + Application | None | Model Run Associated with a Specific EM Application |
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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 | Application of existing 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 | New or revised model | New or revised model | None | Continuation of trends |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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New or revised model | Application of existing model | New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-541 | Application of existing model | New or revised model | New or revised model | Continuation of trends | |
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Document ID for related EM
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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-277 | Doc-294 | Doc-249 | Doc-250 ?Comment:Document 277 is also a source document for this EM |
Doc-303 | Doc-305 | None | None | None | None | None | None | Doc-418 | None | Doc-473 |
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EM ID for related EM
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EM-86 | EM-87 | EM-88 | None | None | None | None | EM-132 | EM-133 | None | None | None | None | None | EM-593 | None | None | None | None |
EM Modeling Approach
EM Relationship to Time (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | Application of existing model | New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-541 | Application of existing model | New or revised model | New or revised model | Continuation of trends | |
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EM Temporal Extent
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Not reported | 2000 | 1980-2006 | 2002-2008 | 2009-2050 | Not reported | 1971-2006 | 1978 - 2009 | 2006-2007, 2010 | 2006-2007, 2010 | 1986-2115 | 1961-1990 | Not applicable | 2000-2010 | None | 2050 |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | * | * | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | * | time-stationary | time-stationary | None | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | * | * | Not applicable | Not applicable | Not applicable | Not applicable | future time | both | Not applicable | Not applicable | None | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | * | * | Not applicable | Not applicable | Not applicable | Not applicable | discrete | * | Not applicable | Not applicable | None | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | * | Not reported | Not applicable | Not applicable | Not applicable | Not applicable | 1 | * | Not applicable | Not applicable | None | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Month | Not reported | Not applicable | Not applicable | Not applicable | Not applicable | Year | Day | Not applicable | Not applicable | None | Not applicable |
EM spatial extent (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | Application of existing model | New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-541 | Application of existing model | New or revised model | New or revised model | Continuation of trends | |
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Bounding Type
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* | * | Watershed/Catchment/HUC | Physiographic or Ecological | Physiographic or Ecological | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Geopolitical | Point or points | Not applicable | * | Multiple unrelated locations (e.g., meta-analysis) | Watershed/Catchment/HUC |
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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 | River Ravna watershed | Puget Sound Region | Guanica Bay watershed | 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 | Laramie City's aquifer protection area |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | >1,000,000 km^2 | * | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | 10-100 km^2 | 10,000-100,000 km^2 | * | * | * | 100-1000 km^2 | 1-10 ha | Not applicable | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 10-100 km^2 |
Spatial Distribution of Computations (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | Application of existing model | New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-541 | Application of existing model | New or revised model | New or revised model | Continuation of trends | |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | 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) | spatially distributed (in at least some cases) | None | * |
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Spatial Grain Type
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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 | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | * | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | None | * |
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Spatial Grain Size
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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 | 25 m x 25 m | 200m x 200m | HUC | 10 m x 10 m | 10 m x 10 m | Not applicable | * | Not reported | county | None | * |
EM Structure and Computation Approach (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | Application of existing model | New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-541 | Application of existing model | New or revised model | New or revised model | Continuation of trends | |
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EM Computational Approach
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Analytic | Analytic | * | Analytic | * | * | Analytic | Analytic | Analytic | Analytic | Numeric | * | Analytic | * | * | * |
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EM Determinism
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* | * | * | * | * | * | * | * | * | * | deterministic | * | * | * | None | * |
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Statistical Estimation of EM
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* | * | * |
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* | * | * |
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Model Checking Procedures Used (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | Application of existing model | New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-541 | Application of existing model | New or revised model | New or revised model | Continuation of trends | |
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Model Calibration Reported?
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* | * | * | * | Yes | Yes | * | * | Yes | Yes | No | Yes | Not applicable | * | None | Unclear |
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Model Goodness of Fit Reported?
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* | * | * | * | * | * | * | * | * | * | No |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
Not applicable | * | None | * |
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Goodness of Fit (metric| value | unit)
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* | * | * | * | * | * | * | * | * | * | None |
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* | * | * | * |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | * | * | No | No | No | No | No | * | * |
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. |
* | Not applicable | No | None | Unclear |
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Model Uncertainty Analysis Reported?
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* | * | Yes | * | * | * | * | * | * | * | No | * | Not applicable | Unclear | None | Unclear |
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Model Sensitivity Analysis Reported?
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* | * | Unclear | * | * | * | * | * | * | * | No | * | Not applicable | Yes | None | Unclear |
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Model Sensitivity Analysis Include Interactions?
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* | * | * | * | * | * | * | * | * | * | Not applicable | * | * | Yes | None | * |
EM Locations, Environments, Ecology
Location of EM Application (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
em.detail.relationToSpaceTerrestrialHelp
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| New or revised model | Application of existing model | New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-541 | Application of existing model | New or revised model | New or revised model | Continuation of trends | |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| New or revised model | Application of existing model | New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-541 | Application of existing model | New or revised model | New or revised model | Continuation of trends | |
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Centroid Lat/Long (Decimal Degree)
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EM ID
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New or revised model | Application of existing model | New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-541 | Application of existing model | New or revised model | New or revised model | Continuation of trends | |
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Centroid Latitude
em.detail.ddLatHelp
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-30 | 50.53 | 38.69 | -34.9 | -34.9 | 42.8 | 48 | 17.96 | 17.73 | 17.73 | -34.18 | 52.86 | Not applicable | 39.83 | None | 41.31 |
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Centroid Longitude
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25 | 7.6 | -89.1 | 138.7 | 138.7 | 24 | -123 | -67.02 | -64.77 | -64.77 | 18.35 | 6.54 | Not applicable | -98.58 | None | -105.46 |
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Centroid Datum
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* | * | * | * | * | * | * | * | * | * | WGS84 | None provided | Not applicable | * | None | * |
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Centroid Coordinates Status
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Estimated | Estimated | * | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | * | Not applicable | Estimated | None | Estimated |
Environments and Scales Modeled (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | Application of existing model | New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-541 | Application of existing model | New or revised model | New or revised model | Continuation of trends | |
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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 | Terrestrial Environment (sub-classes not fully specified) | Forests | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | * | * | Near Coastal Marine and Estuarine | Agroecosystems | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Ground Water | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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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 | Primarily forested watershed | Terrestrial environment surrounding a large estuary | Tropical terrestrial | 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 | watershed |
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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 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 | 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 | * |
Organisms modeled (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
Scale of differentiation of organisms modeled
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EM ID
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New or revised model | Application of existing model | New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-541 | Application of existing model | New or revised model | New or revised model | Continuation of trends | |
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EM Organismal Scale
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Not applicable | Not applicable | Not applicable | Guild or Assemblage | Species | 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 | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| New or revised model | Application of existing model | New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-541 | Application of existing model | New or revised model | New or revised model | Continuation of trends | |
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EnviroAtlas URL
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EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
* Note that run information is shown only where run data differ from the "parent" entry shown at left
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
em.detail.cicesHelp
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| New or revised model | Application of existing model | New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-541 | Application of existing model | New or revised model | New or revised model | Continuation of trends | |
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| New or revised model | Application of existing model | New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-541 | Application of existing model | New or revised model | New or revised model | Continuation of trends | |
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EM Variable Names (and Units)
* Note that for runs, variable name is displayed only where data for that variable differed by run AND those differences were reported in the source document. Where differences occurred but were not reported, the variable is not listed. Click on variable name to view details.
Predictor
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Intermediate
Response
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