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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 | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | EM-467 | EM-593 | Application of existing model | EM-632 | EM-718 | EM-735 | EM-812 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-984 | Continuation of trends |
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EM Short Name
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Divergence in flowering date, Central French Alps | Cultural ES and plant traits, Central French Alps | 3-PG, South Australia | ROS (Recreation Opportunity Spectrum), Europe | InVEST carbon storage and sequestration (v3.2.0) | VELMA soil temperature, Oregon, USA | Sed. denitrification, St. Louis River, MN/WI, USA | Decrease in erosion (shoreline), St. Croix, USVI | Yasso07 v1.0.1, Switzerland | DayCent N2O flux simulation, Ireland | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | Waterfowl pairs, CREP wetlands, Iowa, USA | WESP: Riparian & stream habitat, ID, USA | C sequestration in grassland restoration, England | Wildflower mix supporting bees, CA, USA | ARIES: Crop pollination in Rwanda and Burundi | i-Tree species selector v. 4.0 | EPA national stormwater calculator tool | Visitation to natural areas, New England, USA | Air quality regulation, Lisbon | Salmonid toxicity to heavy metals, USA | CommunityViz, Albany county, Wyoming |
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EM Full Name
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Functional divergence in flowering date, Central French Alps | Cultural ecosystem service estimated from plant functional traits, Central French Alps | 3-PG (Physiological Principles Predicting Growth), South Australia | ROS (Recreation Opportunity Spectrum), Europe | InVEST v3.2.0 Carbon storage and sequestration | VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | Sediment denitrification, St. Louis River estuary, Lake Superior, MN & WI, USA | Decrease in erosion (shoreline) by reef, St. Croix, USVI | Yasso07 v1.0.1 forest litter decomposition, Switzerland | DayCent simulation N2O flux and climate change, Ireland | DeNitrification-DeComposition simulation of N2O flux Ireland | Waterfowl pairs, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | WESP: Riparian and stream habitat focus projects, ID, USA | Carbon sequestration in grassland diversity restoration, England | Wildflower planting mix supporting bees in agricultural landscapes, CA, USA | ARIES; Crop pollination in Rwanda and Burundi | i-Tree species selector v. 4.0 | Environmental Protection Agency National stormwater calculator tool | Estimating natural area use with cell phone data, Narragansett Beach, New England, USA | Modeling the provision of air-quality regulation ecosystem service provided by urban green spaces using lichens as ecological indicators | Chinook salmon and steelhead toxicity to heavy metals, USA | 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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EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | EU Biodiversity Action 5 | InVEST | * | * | * | None | None | None | None | None | None | None | ARIES | i-Tree | * | * | * | US EPA | * |
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EM Source Document ID
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260 | 260 | 243 | 293 | 315 | 317 | 333 | 335 | 343 | 358 | 358 | 372 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
396 | 400 | 411 |
426 ?Comment:Doc# 427 is an additional source for this EM. |
428 ?Comment:This is a tool available on the web for downloading to personal computers. A manual is also available for further documentation of the tool. |
436 | 454 | 462 |
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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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Crossman, N. D., Bryan, B. A., and Summers, D. M. | Paracchini, M.L., Zulian, G., Kopperoinen, L., Maes, J., Schägner, J.P., Termansen, M., Zandersen, M., Perez-Soba, M., Scholefield, P.A., and Bidoglio, G. | The Natural Capital Project | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Brent J. Bellinger, Terri M. Jicha, LaRae P. Lehto, Lindsey R. Seifert-Monson, David W. Bolgrien, Matthew A. Starry, Theodore R. Angradi, Mark S. Pearson, Colleen Elonen, and Brian H. Hill | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Didion, M., B. Frey, N. Rogiers, and E. Thurig | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Murphy, C. and T. Weekley | De Deyn, G. B., R. S. Shiel, N. J. Ostle, N. P. McNamara, S. Oakley, I. Young, C. Freeman, N. Fenner, H. Quirk, and R. D. Bardgett | Williams, N.M., Ward, K.L., Pope, N., Isaacs, R., Wilson, J., May, E.A., Ellis, J., Daniels, J., Pence, A., Ullmann, K., and J. Peters | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. | i-Tree | Rossman, L.A., Bernagros, J.T., Barr, C.M., and M.A. Simon | Merrill, N.H., Atkinson, S.F., Mulvaney, K.K., Mazzotta, K.K., and J. Bousquin | Matos, P., Vieira, J., Rocha, B., Branquinho, C., & Pinho, P. | Chapman, G. | Lieske, S. N., Mullen, S., Knapp, M., & Hamerlinck, J. D. |
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Document Year
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2011 | 2011 | 2011 | 2014 | 2015 | 2013 | 2014 | 2014 | 2014 | 2010 | 2010 | 2010 | 2012 | 2011 | 2015 | 2018 | None | 2022 | 2020 | 2019 | 1978 | 2003 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Carbon payments and low-cost conservation | Mapping cultural ecosystem services: A framework to assess the potential for outdoor recreation across the EU | Carbon storage and sequestration - InVEST (v3.2.0) | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | Sediment nitrification and denitrification in a Lake Superior estuary | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Validating tree litter decomposition in the Yasso07 carbon model | 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 | 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 | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Additional carbon sequestration benefits of grassland diversity restoration | Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States | Towards globally customizable ecosystem service models | i-Tree Species Selector User's Manual v. 4.0 | EPA National Stormwater Calculator Web App users guide-Version 3.4.0. | Using data derived from cellular phone locations to estimate visitation to natural areas: An application to water recreation in New England, USA | Modeling the provision of air-quality regulation ecosystem service provided by urban green spaces using lichens as ecological indicators | Toxicities of Cadmium, Copper, and Zinc to Four Juvenile Toxicities of Cadmium, Copper, and Zinc to Four Juvenile Stages of Chinook Salmon and Steelhead | 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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* | * | * | * | Website | * | * | * | * | * | * | Published report | Published report | * | * | * | Webpage | Published EPA report | * | * | Published journal manuscript | 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 | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | EM-467 | EM-593 | Application of existing model | EM-632 | EM-718 | EM-735 | EM-812 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-984 | Continuation of trends |
| * | * | http://www.csiro.au/products/3PGProductivity#a1 | * | https://www.naturalcapitalproject.org/invest/ | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | * | * | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | * | http://www.dndc.sr.unh.edu | * | * | * | * | https://github.com/integratedmodelling/im.aries.global | https://species.itreetools.org/ | https://www.epa.gov/water-research/national-stormwatercalculator | https://github.com/USEPA/Recreation_Benefits.git | * | Not applicable | https://communityviz.com/ | |
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Contact Name
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Sandra Lavorel | Sandra Lavorel | Anders Siggins | Maria Luisa Paracchini | The Natural Capital Project | Alex Abdelnour | Brent J. Bellinger | Susan H. Yee |
Markus Didion ?Comment:Tel.: +41 44 7392 427 |
M. Abdalla | M. Abdalla | David Otis | Chris Murphy | Gerlinde B. De Deyn | Neal Williams | Javier Martinez |
Not reported ?Comment:send comments through any of the means listed on the i-Tree support page: http://www.itreetools.org/support/. |
Lewis Rossman | Nathaniel Merrill | Pedro Pinho | Gary Chapman | Scott Lieske |
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Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Not reported | Joint Research Centre, Institute for Environment and Sustainability, Via E.Fermi, 2749, I-21027 Ispra (VA), Italy | 371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Dept. of Terrestrial Ecology, Netherlands Institute of Ecology, P O Box 40, 6666 ZG Heteren, The Netherlands | Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616 | BC3-Basque Centre for Climate Chan ge, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain | Not reported | Center for environmental solutions and emergency response, Cincinnati, Ohio | Atlantic Coastal Environmental Sciences Division, U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Narragansett, Rhode Island, United States of America, | N/A | Corvallis Environmental Research Laboratory, Western Fish Toxicology Station U.S. Environmental Protection Agency, Corvallis, Oregon 97330 | Department of Agricultural & Applied Economics University of Wyoming, Laramie WY 82071 |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | Anders.Siggins@csiro.au | luisa.paracchini@jrc.ec.europa.eu | invest@naturalcapitalproject.org | abdelnouralex@gmail.com | bellinger.brent@epa.ogv | yee.susan@epa.gov | markus.didion@wsl.ch | abdallm@tcd.ie | abdallm@tcd.ie | dotis@iastate.edu | chris.murphy@idfg.idaho.gov | g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com | nmwilliams@ucdavis.edu | javier.martinez@bc3research.org | info@itreetools.org | n.a. | merrill.nathaniel@epa.gov | ppinho@fc.ul.pt | N/A | 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 | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | EM-467 | EM-593 | Application of existing model | EM-632 | EM-718 | EM-735 | EM-812 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-984 | Continuation of trends |
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Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Functional divergence of flowering date was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The Cultural ecosystem service map was a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to cultural ecosystem services were based on stakeholders’ perceptions, given positive or negative contributions." | 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: "Research on ecosystem services mapping and valuing has increased significantly in recent years. However, compared to provisioning and regulating services, cultural ecosystem services have not yet beenfully integrated into operational frameworks. One reason for this is that transdisciplinarity is required toaddress the issue, since by definition cultural services (encompassing physical, intellectual, spiritual inter-actions with biota) need to be analysed from multiple perspectives (i.e. ecological, social, behavioural).A second reason is the lack of data for large-scale assessments, as detailed surveys are a main sourceof information. Among cultural ecosystem services, assessment of outdoor recreation can be based ona large pool of literature developed mostly in social and medical science, and landscape and ecologystudies. This paper presents a methodology to include recreation in the conceptual framework for EUwide ecosystem assessments (Maes et al., 2013), which couples existing approaches for recreation man-agement at country level with behavioural data derived from surveys and population distribution data.The proposed framework is based on three components: the ecosystem function (recreation potential),the adaptation of the Recreation Opportunity Spectrum framework to characterise the ecosystem serviceand the distribution of potential demand in the EU." | Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. ABSTRACT: "Terrestrial ecosystems, which store more carbon than the atmosphere, are vital to influencing carbon dioxide-driven climate change. The InVEST model uses maps of land use and land cover types and data on wood harvest rates, harvested product degradation rates, and stocks in four carbon pools (aboveground biomass, belowground biomass, soil, dead organic matter) to estimate the amount of carbon currently stored in a landscape or the amount of carbon sequestered over time. Additional data on the market or social value of sequestered carbon and its annual rate of change, and a discount rate can be used in an optional model that estimates the value of this environmental service to society. Limitations of the model include an oversimplified carbon cycle, an assumed linear change in carbon sequestration over time, and potentially inaccurate discounting rates." AUTHOR'S DESCRIPTION: "A fifth optional pool included in the model applies to parcels that produce harvested wood products (HWPs) such as firewood or charcoal or more long-lived products such as house timbers or furniture. Tracking carbon in this pool is useful because it represents the amount of carbon kept from the atmosphere by a given product." | ABSTRACT: "We used a new ecohydrological model, Visualizing Ecosystems for Land Management Assessments (VELMA), to analyze the effects of forest harvest on catchment carbon and nitrogen dynamics. We applied the model to a 10 ha headwater catchment in the western Oregon Cascade Range where two major disturbance events have occurred during the past 500 years: a stand-replacing fire circa 1525 and a clear-cut in 1975. Hydrological and biogeochemical data from this site and other Pacific Northwest forest ecosystems were used to calibrate the model. Model parameters were first calibrated to simulate the postfire buildup of ecosystem carbon and nitrogen stocks in plants and soil from 1525 to 1969, the year when stream flow and chemistry measurements were begun. Thereafter, the model was used to simulate old-growth (1969–1974) and postharvest (1975–2008) temporal changes in carbon and nitrogen dynamics…" AUTHOR'S DESCRIPTION: "The soil column model consists of three coupled submodels:...a soil temperature model [Cheng et al., 2010] that simulates daily soil layer temperatures from surface air temperature and snow depth by propagating the air temperature first through the snowpack and then through the ground using the analytical solution of the one-dimensional thermal diffusion equation" |
ABSTRACT: "Inorganic nitrogen (N) transformations and removal in aquatic sediments are microbially mediated, and rates influence N-transport. In this study we related physicochemical properties of a large Great Lakes embayment, the St. Louis River Estuary (SLRE) of western Lake Superior, to sediment N-transformation rates. We tested for associations among rates and N-inputs, vegetation biomass, and temperature.We measured rates of nitrification (NIT), unamended base denitrification (DeNIT), and potential denitrification [denitrifying enzyme activity (DEA)] in 2011 and 2012 across spatial and depth zones. In vegetated habitats, NIT and DeNIT rateswere highest in deep (ca. 2 m) water (249 and 2111 mg N m−2 d−1, respectively) and in the upper and lower reaches of the SLRE (N126 and 274 mg N m−2 d−1, respectively). Rates of DEA were similar among zones. In 2012, NIT, DeNIT, and DEA rateswere highest in July, May, and June, respectively. System-wide, we observed highest NIT (223 and 287 mgNm−2 d−1) and DeNIT (77 and 64 mgNm−2 d−1) rates in the harbor and from deep water, respectively. Amendment with NO3 − enhanced DeNIT rates more than carbon amendment; however, DeNIT and NIT rates were inversely related, suggesting the two processes are decoupled in sediments. Average proportion of N2O released during DEA (23–54%) was greater than from DeNIT (0–41%). Nitrogen cycling rates were spatially and temporally variable, but we modeled how alterations to water depth and N-inputs may impact DeNIT rates. A large flood occurred in 2012 which temporarily altered water chemistry and sediment nitrogen cycling." ?Comment:BH: I pasted the entire abstract because there is not specific mention of the combined sediment nitrification 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...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef." | ABSTRACT: "...We examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to (i) examine the effect of the application of three different published Yasso07 parameter sets on simulated decay rate; (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests;…" AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root litter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The result of the simulation was an annual estimate of the remaining fraction of the initial mass, which could then be compared with observed data." | Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios. | 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. | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "Number of duck pairs per site was estimated for 6 species of ducks: Mallard (Anas platyrhynchos), Blue-winged Teal (Anas discors), Northern Shoveler (Anas clypeata), Gadwall (Anas strepera), Northern Pintail (Anas acuta), and Wood Duck (Aix sponsa), using models developed by Cowardin et al. (1995). Pair abundance was based on wetland class (i.e., temporary, seasonal, semi-permanent, lake, or river), wetland size, and a set of species specific regression coefficients. All CREP wetlands were considered semi-permanent for this analysis; therefore only coefficients associated with the semipermanent wetland pair model were used in calculations. The general equation used to estimate the pairs per wetland was: Pairs = e (a + bx + α) * p where, e = mathematical constant ≈ 2.718, a = species specific regression coefficient a, b = species specific regression coefficient b, x = the natural log of wetland size, α = species specific alpha value, and p = proportion of the basin containing water (assumed to be 0.90 for this analysis)" | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | ABSTRACT: "A major aim of European agri-environment policy is the management of grassland for botanical diversity conservation and restoration, together with the delivery of ecosystem services including soil carbon (C) sequestration. To test whether management for biodiversity restoration has additional benefits for soil C sequestration, we investigated C and nitrogen (N) accumulation rates in soil and C and N pools in vegetation in a long-term field experiment (16 years) in which fertilizer application and plant seeding were manipulated. In addition, the abundance of the legume Trifolium pratense was manipulated for the last 2 years. To unravel the mechanisms underlying changes in soil C and N pools, we also tested for effects of diversity restoration management on soil structure, ecosystem respiration and soil enzyme activities…" AUTHOR'S DESCRIPTION: "Measurements were made on 36 plots of 3 x 3 m comprising two management treatments (and their controls) in a long-term multifactorial grassland restoration experiment which have successfully increased plant species diversity, namely the cessation of NPK fertilizer application and the addition of seed mixtures…" | Abstract: " Global trends in pollinator-dependent crops have raised awareness of the need to support managed and wild bee populations to ensure sustainable crop production. Provision of sufficient forage resources is a key element for promoting bee populations within human impacted landscapes, particularly those in agricultural lands where demand for pollination service is high and land use and management practices have reduced available flowering resources. Recent government incentives in North America and Europe support the planting of wildflowers to benefit pollinators; surprisingly, in North America there has been almost no rigorous testing of the performance of wildflower mixes, or their ability to support wild bee abundance and diversity. We tested different wildflower mixes in a spatially replicated, multiyear study in three regions of North America where production of pollinatordependent crops is high: Florida, Michigan, and California. In each region, we quantified flowering among wildflower mixes composed of annual and perennial species, and with high and low relative diversity. We measured the abundance and species richness of wild bees, honey bees, and syrphid flies at each mix over two seasons. In each region, some but not all wildflower mixes provided significantly greater floral display area than unmanaged weedy control plots. Mixes also attracted greater abundance and richness of wild bees, although the identity of best mixes varied among regions. By partitioning floral display size from mix identity we show the importance of display size for attracting abundant and diverse wild bees. Season-long monitoring also revealed that designing mixes to provide continuous bloom throughout the growing season is critical to supporting the greatest pollinator species richness. Contrary to expectation, perennials bloomed in their first season, and complementarity in attraction of pollinators among annuals and perennials suggests that inclusion of functionally diverse species may provide the greatest benefit. Wildflower mixes may be particularly important for providing resources for some taxa, such as bumble bees, which are known to be in decline in several regions of North America. No mix consistently attained the full diversity that was planted. Further study is needed on how to achieve the desired floral display and diversity from seed mixes. " Additional information in supplemental Appendices online: http://dx.doi.org/10.1890/14-1748.1.sm | [Abstract:Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The Artificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multicriteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed.] | ABSTRACT: "The Species Selector is a free-standing i-Tree utility that ranks tree species based on their environmental benefits at maturity. As such, it complements existing tree selection programs that rank species based on esthetics or other features. Species are selected based on three types of information. First, hardiness is considered. The hardiness zone is determined based on state and city, and all species that are not sufficiently hardy are eliminated from consideration. Second, mature height is considered. Users are asked to specify minimum and maximum heights, and species outside of that range are eliminated. Finally, eight environmental factors are considered in the rankings created by the Species Selector: • Air pollution removal • Air temperature reduction • Ultraviolet radiation reduction • Carbon storage • Pollen allergenicity • Building energy conservation • Wind reduction • Stream flow reduction (stormwater management). Users are asked to rank the importance of each of these factors on a scale of 0 to 10. The combination of hardiness, mature height, and desired functionality produces a ranked list of appropriate species from an initial database of about 1,600 species. The large species database covers a broad range of native, naturalized and exotic trees, some of which are commonly planted in urban areas. Since only city hardiness zone, tree height and user functional preferences are used to produce the list, there may well be many species on the list that are unsuitable to the local context for a variety of reasons. A species may have particular structural, drainage, sun, pest, or soil pH limitations that should exclude it from use. Furthermore, since many native and exotic species are included, items may appear that are simply not available in the local trade. For these reasons, the list should be considered a beginning rather than an end. The list will need to be whittled down to meet local needs and limitations. Relevant cultural needs should be taken into account as well. The result will be a list of recommended species suited for local use that maximizes environmental services." | "Abstract: EPA’s National Stormwater Calculator (SWC) is a software application tool that estimates the annual amount of rainwater and frequency of runoff from a specific site using green infrastructure as low impact development controls. The SWC is designed for use by anyone interested in reducing runoff from a property, including site developers, landscape architects, urban planners, and homeowners. This User’s guide contains information on the SWC web application. SWC Version 3.4 contains has updated historical meteorological data (from 1970 - 2006 to 1990 - 2019), updated Bureau of Labor Statistics Cost Data (from 2018 to 2020), and the 5.1.015 Stormwater Management Model (SWMM) engine (from 5.1.007). Evaporation was calculated by the Hargreaves method (EPA, 2015), based on historical or future daily temperature data." | ABSTRACT: "We introduce and validate the use of commercially available human mobility datasets based on cell phone locations to estimate visitation to natural areas. By combining this data with on-the-ground observations of visitation to water recreation areas in New England, we fit a model to estimate daily visitation for four months to more than 500 sites. The results show the potential for this new big data source of human mobility to overcome limitations in traditional methods of estimating visitation and to provide consistent information at policy-relevant scales. However, the data providers’ opaque and rapidly developing methods for processing locational information required a calibration and validation against data collected by traditional means to confidently reproduce the desired estimates of visitation. We found that with this calibration, the high-resolution information in both space and time provided by cell phone location-derived data creates opportunities for developing next-generation models of human interactions with the natural environment. " | The UN Sustainable Development Goals states that urban air pollution must be tackled to create more inclusive, safe, resilient and sustainable cities. Urban green infrastructures can mitigate air pollution, but a crucial step to use this knowledge into urban management is to quantify how much air-quality regulation can green spaces provide and to understand how the provision of this ecosystem service is affected by other environmental factors. Considering the insufficient number of air quality monitoring stations in cities to monitor the wide range of natural and anthropic sources of pollution with high spatial resolution, ecological indicators of air quality are an alternative cost-effective tool. The aim of this work was to model the supply of air-quality regulation based on urban green spaces characteristics and other environmental factors. For that, we sampled lichen diversity in the centroids of 42 urban green spaces in Lisbon, Portugal. Species richness was the best biodiversity metric responding to air pollution, considering its simplicity and its significative response to the air pollutants concentration data measured in the existent air quality monitoring stations. Using that metric, we then created a model to estimate the supply of air quality regulation provided by green spaces in all green spaces of Lisbon based on the response to the following environmental drivers: the urban green spaces size and its vegetation density. We also used the unexplained variance of this model to map the background air pollution. Overall, we suggest that management should target the smallest urban green spaces by increasing green space size or tree density. The use of ecological indicators, very flexible in space, allow the understanding and the modeling of the provision of air-quality regulation by urban green spaces, and how urban green spaces can be managed to improve air quality and thus improve human well-being and cities resilience. | ABSTRACT: "Continuous-flow toxicity tests were conducted to determine the relative tolerances of newly hatched alevins, swim-up alevins, parr, and smolts of chinook salmon (Oncorhynchus tshawytscha) and steelhead (Salmo gairdneri) to cadmium, copper, and zinc. Newly hatched alevins were much more tolerant to cadmium and, to a lesser extent, to zinc than were later juvenile forms. However, the later progression from swim-up alevin, through parr, to smolt was accompanied by a slight increase in metal tolerance. The 96-h LC50 values for all four life stages ranged from 1.0 to >27ug Cd/liter, 17 to 38ug Cu/liter, and 93 to 815ug Zn/liter. Steelhead were consistently more sensitive to these metals than were chinook salmon. When a sensitive life stage for acute toxicity tests with metals is sought, the more resistant newly hatched alevins should be avoided. Although tolerance may increase with age, all later juvenile life stages are more sensitive and should give similar results. | 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 | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | climate change | climate change | None identified | None identified | None identified | None identified | None identified | None identified | None given | None identified | None identified | NA | None provided |
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Biophysical Context
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Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm. | No additional description provided | Not applicable | Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter. | Estuarine system | No additional description provided | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Prairie pothole region of north-central Iowa | restored, enhanced and created wetlands | Lolium perenne-Cynosorus cristatus grassland; The soil is a shallow brown-earth (average depth 28 cm) over limestone of moderate-high residual fertility. | field plots near agricultural fields (mixed row crop, almond, walnuts), central valley, Ca | Entire countries of Rwanda and Burundi considered | No additional description provided | Sites up to 12 acres | Natural area water bodies | Green spaces in Lisbon, Portugal | Microcosms | Groundwater recharge area, City of Laramie |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | Four carbon-planting systems including hardwood and mallee monoculture plantings, and mixed species ecological carbon plantings | No scenarios presented | Optional future scenarios for changed LULC and wood harvest | No scenarios presented | No scenarios presented | No scenarios presented |
No scenarios presented ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
air temperature, precipitation, Atmospheric CO2 concentrations | fertilization | No scenarios presented | Sites, function or habitat focus | Additional benefits due to biodiversity restoration practices | Varied wildflower planting mixes of annuals and perennials | N/A | No scenarios presented | Climate change scenarios | N/A | No scenarios presented | Life stage | Continuation of trends |
EM Relationship to Other EMs or Applications
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EM ID
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New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | EM-467 | EM-593 | Application of existing model | EM-632 | EM-718 | EM-735 | EM-812 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-984 | 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 (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 Only | Method + Application | Method + Application | Method + Application |
Method + Application (multiple runs exist) ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
Method + Application (multiple runs exist) | Method + Application | Method + Application (multiple runs exist) | Method + Application (multiple runs exist) | Method + Application (multiple runs exist) | Method + Application (multiple runs exist) | Method + Application | Method Only | Method Only | Method + Application | Method + Application | Method + Application (multiple runs exist) | 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 | New or revised model | Application of existing model | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | 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 | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | EM-467 | EM-593 | Application of existing model | EM-632 | EM-718 | EM-735 | EM-812 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-984 | Continuation of trends |
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Document ID for related EM
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Doc-260 | Doc-269 | None | Doc-243 | Doc-246 | Doc-245 | Doc-290 | Doc-291 | Doc-289 | Doc-309 | Doc-13 | Doc-317 | None | Doc-335 | Doc-342 | Doc-344 | None | None | None | Doc-390 | None | Doc-400 |
?Comment:Supplemental Information to this article can be found online at https://doi.org/10.1016/j.scitotenv.2018.09.371. |
Doc-427 | None | None | None | None | Doc-473 |
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EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-82 | EM-83 | None | None | EM-349 | EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | None | EM-447 | EM-448 | EM-466 | EM-469 | EM-480 | EM-485 | EM-598 | EM-593 | EM-705 | EM-703 | EM-702 | EM-701 | EM-700 | EM-706 | EM-729 | EM-730 | EM-734 | EM-743 | EM-749 | EM-750 | EM-756 | EM-757 | EM-758 | EM-759 | EM-760 | EM-761 | EM-763 | EM-764 | EM-766 | EM-767 | EM-732 | EM-737 | EM-738 | EM-739 | EM-741 | EM-742 | EM-751 | EM-768 | None | EM-784 | EM-793 | EM-859 | None | None | 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 | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | EM-467 | EM-593 | Application of existing model | EM-632 | EM-718 | EM-735 | EM-812 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-984 | Continuation of trends |
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EM Temporal Extent
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2007-2008 | Not reported | 2009-2050 | Not reported | Not applicable | 1969-2008 | 2011 - 2012 | 2006-2007, 2010 | 1993-2013 | 1961-1990 | 1961-1990 | 2002-2007 | 2010-2011 | 1990-2007 | 2011-2012 | 2010 | Not applicable | Not applicable | 2017 | 2015-2018 | 1978 | 2050 |
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EM Time Dependence
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* | * | time-dependent | * | time-dependent | time-dependent | * | * | time-dependent | time-dependent | time-dependent | * | time-dependent | * | time-dependent | * | Not applicable | * | time-dependent | * | time-stationary | * |
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EM Time Reference (Future/Past)
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* | * | future time | * | future time | future time | * | * | future time | both | both | * | past time | * | past time | * | * | * | past time | * | Not applicable | * |
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EM Time Continuity
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* | * | discrete | * | discrete | discrete | * | * | discrete | discrete | discrete | * | * | * | discrete | * | * | * | discrete | * | Not applicable | * |
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EM Temporal Grain Size Value
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* | * | 1 | * | 1 | 1 | * | * | 1 | 1 | 1 | * | * | * | 1 | * | * | * | 1 | * | Not applicable | * |
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EM Temporal Grain Size Unit
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* | * | Month | * | Year | Day | * | * | Year | Day | Day | * | * | * | Year | * | * | * | Day | * | 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 | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | EM-467 | EM-593 | Application of existing model | EM-632 | EM-718 | EM-735 | EM-812 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-984 | Continuation of trends |
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Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | * | Not applicable | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | * | Point or points | Point or points | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Other |
Point or points ?Comment:This is a guess based on information in the document. 3 field sites were separated by up to 9km |
* | Not applicable | Not applicable | Point or points | Physiographic or ecological | Geopolitical | Watershed/Catchment/HUC |
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Spatial Extent Name
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Central French Alps | Central French Alps | Agricultural districts of the state of South Australia | European Union countries | Not applicable | H. J. Andrews LTER WS10 | St. Louis River estuary | Coastal zone surrounding St. Croix | Switzerland | Oak Park Research centre | Oak Park Research centre | CREP (Conservation Reserve Enhancement Program) wetland sites | Wetlands in idaho | Colt Park meadows, Ingleborough National Nature Reserve, northern England | Agricultural plots | Rwanda and Burndi | Not applicable | Not applicable | Cape Cod | Urban green spaces in Lisbon | Northwest | Laramie City's aquifer protection area |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | * | >1,000,000 km^2 | Not applicable | 10-100 ha | 10-100 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | 1-10 ha | 1-10 ha | 1-10 km^2 | * | <1 ha | 10-100 km^2 | 10,000-100,000 km^2 | Not applicable | Not applicable | 1000-10,000 km^2. | 100-1000 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 | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | EM-467 | EM-593 | Application of existing model | EM-632 | EM-718 | EM-735 | EM-812 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-984 | 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) ?Comment:See below, grain includes vertical, subsurface dimension. |
* | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | * | * | spatially distributed (in at least some cases) | * | spatially distributed (in at least some cases) | * | spatially distributed (in at least some cases) | Not applicable | * | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | * |
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Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | volume, for 3-D 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 | * | * | other (specify), for irregular (e.g., stream reach, lake basin) | map scale, for cartographic feature | Not applicable | * |
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Spatial Grain Size
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20 m x 20 m | 20 m x 20 m | 1 ha x 1 ha | 100 m x 100 m | application specific | 30 m x 30 m surface pixel and 2-m depth soil column | * | 10 m x 10 m | 5 sites | * | * | multiple, individual, irregular shaped sites | * | 3 m x 3 m | * | 1km | * | * | water feature edge (beach) | N/A | Not applicable | * |
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 | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | EM-467 | EM-593 | Application of existing model | EM-632 | EM-718 | EM-735 | EM-812 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-984 | Continuation of trends |
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EM Computational Approach
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Analytic | Analytic | * | Analytic | Analytic | * | Analytic | Analytic | * | * | * | Analytic | * | Analytic | * | Analytic | Analytic | Analytic | * | Analytic | Numeric | * |
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EM Determinism
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* | * | * | * | * | * | * | * | stochastic | * | * | * | * | stochastic | * | * | * | * | * | * | deterministic | * |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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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
em.detail.idHelp
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New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | EM-467 | EM-593 | Application of existing model | EM-632 | EM-718 | EM-735 | EM-812 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-984 | Continuation of trends |
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Model Calibration Reported?
em.detail.calibrationHelp
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* | * | Yes | * | Not applicable | * | * | Yes | * | * | Yes | Unclear | * | Not applicable | * | Unclear | Not applicable | Not applicable | Yes | Yes | No | Unclear |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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Yes | * | * | * | Not applicable | * | * | * | * |
Yes ?Comment:for N2O fluxes |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
* | * | Not applicable | * | * | Not applicable | Not applicable |
Yes ?Comment:Random forest model performance statistics |
Yes | No | * |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | * |
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Model Operational Validation Reported?
em.detail.validationHelp
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* | * | * | * | Not applicable | * | * | Yes | Yes | Yes | Yes | Unclear | * | * | * | * | Not applicable | Not applicable | Yes | * | No | Unclear |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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* | * | * | * | Not applicable | * | * | * | * | * | * | * | * | * | * | * | Not applicable | Not applicable | Unclear | * | No | Unclear |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | No | Not applicable | No | No | No | No | No | No | No | No | No | No | No | Not applicable | Not applicable | * | Unclear | Yes | Unclear |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Yes | Not applicable |
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 | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | EM-467 | EM-593 | Application of existing model | EM-632 | EM-718 | EM-735 | EM-812 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-984 | Continuation of trends |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
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| New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | EM-467 | EM-593 | Application of existing model | EM-632 | EM-718 | EM-735 | EM-812 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-984 | Continuation of trends |
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* | * | * | * | * | * | * | * | * | * | * | * | None | * |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | EM-467 | EM-593 | Application of existing model | EM-632 | EM-718 | EM-735 | EM-812 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-984 | Continuation of trends |
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Centroid Latitude
em.detail.ddLatHelp
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45.05 | 45.05 | -34.9 | 48.2 | -9999 | 44.25 | 46.75 | 17.73 | 46.82 | 52.86 | 52.86 | 42.62 | 44.06 | 54.2 | 29.4 | -2.59 | Not applicable | Not applicable | 41.72 | 38.75 | 44.53 | 41.31 |
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Centroid Longitude
em.detail.ddLongHelp
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6.4 | 6.4 | 138.7 | 16.35 | -9999 | -122.33 | -92.08 | -64.77 | 8.23 | 6.54 | 6.54 | -93.84 | -114.69 | -2.35 | -82.18 | 29.97 | Not applicable | Not applicable | -70.29 | 9.8 | 123.25 | -105.46 |
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Centroid Datum
em.detail.datumHelp
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* | * | * | * | Not applicable | * | * | * | * | None provided | None provided | * | * | * | * | * | Not applicable | Not applicable | * | None provided | WGS84 | * |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Provided | * | * | Not applicable | Provided | * | * | * | Provided | Provided | * | * | Provided | Provided | * | Not applicable | Not applicable | * | * | 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
em.detail.idHelp
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New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | EM-467 | EM-593 | Application of existing model | EM-632 | EM-718 | EM-735 | EM-812 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-984 | Continuation of trends |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Forests | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) | Not applicable | Forests | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Agroecosystems | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Lakes and Ponds | Near Coastal Marine and Estuarine | Created Greenspace | Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Not applicable | Terrestrial environments, but not specified for methods | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | Freshwater estuary | Coral reefs | forests | farm pasture | farm pasture | Wetlands buffered by grassland set in agricultural land | created, restored and enhanced wetlands | fertilized grassland (historically hayed) | Agricultural landscape | varied | Urban greenspace | Terrrestrial landcover | beaches | Green spaces in Lisbon, Portugal | Modeling stream exposure | 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 | * | * | Not applicable | * | Ecological scale corresponds to the Environmental Sub-class | * | Ecological scale corresponds to the Environmental Sub-class | * | * | Ecological scale corresponds to the Environmental Sub-class | * | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to 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 corresponds to 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
em.detail.nameOfOrgsOrGroupsHelp
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EM ID
em.detail.idHelp
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New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | EM-467 | EM-593 | Application of existing model | EM-632 | EM-718 | EM-735 | EM-812 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-984 | Continuation of trends |
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EM Organismal Scale
em.detail.orgScaleHelp
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Community | Community | * | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Community | Not applicable | Not applicable | * | Not applicable | Community | * | Guild or Assemblage | * | Not applicable | Not applicable | Guild or Assemblage | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | EM-467 | EM-593 | Application of existing model | EM-632 | EM-718 | EM-735 | EM-812 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-984 | Continuation of trends |
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EnviroAtlas URL
em.detail.enviroAtlasURLHelp
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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 | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | EM-467 | EM-593 | Application of existing model | EM-632 | EM-718 | EM-735 | EM-812 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-984 | Continuation of trends |
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
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| New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | EM-467 | EM-593 | Application of existing model | EM-632 | EM-718 | EM-735 | EM-812 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-984 | Continuation of trends |
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None |
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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
em.detail.variablesPredictorHelp
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Intermediate
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EM ID
em.detail.idHelp
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New or revised model | New or revised model | EM-129 | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | EM-467 | EM-593 | Application of existing model | EM-632 | EM-718 | EM-735 | EM-812 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-984 | Continuation of trends |
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Intermediate (Computed) Variables (and Units)
em.detail.intermediateVariableHelp
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None | None | None |
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None | None | None | None | None | None | None | None | None | None | None | None | * |
Response
em.detail.variablesResponseHelp
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