EcoService Models Library (ESML)
Home
Search EMs
My
EMs 
Learn about
ESML
View ESML Data Maploading
View Runs
: Wildflower planting mix supporting bees in agricultural landscapes, CA, USA (EM-812)
Back
Collapse All
Expand AllEM Identity and Description
EM Identification (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
|
EM ID
em.detail.idHelp
?
|
New or revised model | New or revised model | EM-275 | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | EM-632 | Application of existing model | Application of existing model | EM-734 | EM-735 | EM-777 | EM-796 | EM-812 | New or revised model | Application of existing model | maximize optimal habitat |
|
EM Short Name
em.detail.shortNameHelp
?
|
Divergence in flowering date, Central French Alps | Natural attenuation by soil, The Netherlands | SWAT, Aixola watershed, Spain | ARIES open Space, Puget Sound Region, USA | Coastal protection, Europe | InVEST carbon storage and sequestration (v3.2.0) | VELMA soil temperature, Oregon, USA | Decrease in erosion (shoreline), St. Croix, USVI | Reef dive site favorability, St. Croix, USVI | Yasso07 v1.0.1, Switzerland | EnviroAtlas - Restorable wetlands | DayCent N2O flux simulation, Ireland | Waterfowl pairs, CREP wetlands, Iowa, USA | Grasshopper Sparrow density, CREP, Iowa, USA | Savannah Sparrow density, CREP, Iowa, USA | WESP: Marsh and open water, ID, USA | C sequestration in grassland restoration, England | Bees and managed prairie plants and soil, MO, USA | Wildflower mix supporting bees, MI, USA | Wildflower mix supporting bees, CA, USA | HWB poor health, Great Lakes waterfront, USA | Atlantis ecosystem biology submodel | MesoHABSIM, river habitat assessment, Poland |
|
EM Full Name
em.detail.fullNameHelp
?
|
Functional divergence in flowering date, Central French Alps | Natural attenuation capacity of the soil, The Netherlands | SWAT (Soil and Water Assessment Tool), Aixola watershed, Spain | ARIES (Artificial Intelligence for Ecosystem Services) Open Space Proximity for Homeowners, Puget Sound Region, Washington, USA | Coastal protection, Europe | InVEST v3.2.0 Carbon storage and sequestration | VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | Decrease in erosion (shoreline) by reef, St. Croix, USVI | Dive site favorability (reef), St. Croix, USVI | Yasso07 v1.0.1 forest litter decomposition, Switzerland | US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | DayCent simulation N2O flux and climate change, Ireland | Waterfowl pairs, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Grasshopper Sparrow population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Savannah Sparrow population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | WESP: Deepwater marsh and open Water waterfowl habitat, Idaho, USA | Carbon sequestration in grassland diversity restoration, England | Tallgrass prairie bee community affected by management effects on plant community and soil properties, Missouri, USA | Wildflower planting mix supporting bees in agricultural landscapes, MI, USA | Wildflower planting mix supporting bees in agricultural landscapes, CA, USA | Human well being indicator-poor health, Great Lakes waterfront, USA | Calibrating process-based marine ecosystem models: An example case using Atlantis | Application of the Mesohabitat Simulation System (MesoHABSIM) for Assessing Impact of River Maintenance and Restoration Measures |
|
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
EU Biodiversity Action 5 | * | * | ARIES | EU Biodiversity Action 5 | InVEST | US EPA | US EPA | US EPA | * | US EPA | EnviroAtlas | * | * | * | * | * | * | * | * | None | * | * | * |
|
EM Source Document ID
|
260 | 287 | 295 | 302 | 296 | 315 | 317 | 335 | 335 | 343 | 262 | 358 | 372 | 372 | 372 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
396 | 398 | * | * |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
459 | 495 |
|
Document Author
em.detail.documentAuthorHelp
?
|
Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | van Wijnen, H.J., Rutgers, M., Schouten, A.J., Mulder, C., de Zwart, D., and Breure, A.M. | Zabaleta, A., Meaurio, M., Ruiz, E., and Antigüedad, I. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Liquete, C., Zulian, G., Delgado, I., Stips, A., and Maes, J. | The Natural Capital Project | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Didion, M., B. Frey, N. Rogiers, and E. Thurig | US EPA Office of Research and Development - National Exposure Research Laboratory | 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 | 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 | 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 | Buckles, B. J., and A. N. Harmon-Threatt | * | * | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | Pethybridge, H. R., Weijerman, M., Perrymann, H., Audzijonyte, A., Porobic, J., McGregor, V., … & Fulton, E. | Suska, K. and Parasiewicz, P. |
|
Document Year
em.detail.documentYearHelp
?
|
2011 | 2012 | 2014 | 2014 | 2013 | * | 2013 | 2014 | 2014 | 2014 | 2013 | 2010 | 2010 | 2010 | 2010 | 2012 | 2011 | 2019 | * | * | None | 2019 | 2020 |
|
Document Title
em.detail.sourceIdHelp
?
|
Using plant functional traits to understand the landscape distribution of multiple ecosystem services | How to calculate the spatial distribution of ecosystem services - Natural attenuation as example from the Netherlands | Simulation climate change impact on runoff and sediment yield in a small watershed in the Basque Country, Northern Spain | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Assessment of coastal protection as an ecosystem service in Europe | Carbon storage and sequestration - InVEST (v3.2.0) | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Validating tree litter decomposition in the Yasso07 carbon model | EnviroAtlas - National | 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 | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | 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 | Bee diversity in tallgrass prairies affected by management and its effects on above‐ and below‐ground resources | * | * | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | Calibrating process-based marine ecosystem models: An example case using Atlantis | Application of the mesohabitat simulation system (mesohabsim) for assessing impact of river maintenance and restoration measures |
|
Document Status
em.detail.statusCategoryHelp
?
|
* | * | * | * | * | * | * | * | * | * | * | * | * | * | * | * | * | * | * | Peer reviewed and published | Peer reviewed but unpublished (explain in Comment) | * | * |
|
Comments on Status
em.detail.commentsOnStatusHelp
?
|
* | * | * | * | * | Website | * | * | * | * | Published on US EPA EnviroAtlas website | * | Published report | Published report | Published report | Published report | * | * | * | Published journal manuscript | Journal manuscript submitted or in review | * | * |
Software and Access (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
|
EM ID
em.detail.idHelp
?
|
New or revised model | New or revised model | EM-275 | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | EM-632 | Application of existing model | Application of existing model | EM-734 | EM-735 | EM-777 | EM-796 | EM-812 | New or revised model | Application of existing model | maximize optimal habitat |
| * | * | http://swat.tamu.edu/software/arcswat/ | http://aries.integratedmodelling.org/ | * | 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 | https://www.epa.gov/enviroatlas | * | * | * | * | * | * | * | * | Not applicable | * | https://noaa-fisheries-integrated-toolbox.github.io/Atlantis | https://mesohabsim.org/ | |
|
Contact Name
em.detail.contactNameHelp
?
|
Sandra Lavorel | H.J. van Wijnen | Ane Zabaleta | Ken Bagstad | Camino Liquete | The Natural Capital Project | Alex Abdelnour | Susan H. Yee | Susan H. Yee |
Markus Didion ?Comment:Tel.: +41 44 7392 427 |
EnviroAtlas Team | M. Abdalla | David Otis | David Otis | David Otis | Chris Murphy | Gerlinde B. De Deyn | Alexandra N. Harmon‐Threatt | * | Neal Williams | Ted Angradi | Heidi R. Pethybridge | k.suska@infish.com.pl |
|
Contact Address
|
Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | National Institute for Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, The Netherlands | Hydrogeology and Environment Group, Science and Technology Faculty, University of the Basque Country, 48940 Leioa, Basque Country (Spain) | Geosciences and Environmental Change Science Center, US Geological Survey | European Commission, 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 | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Not reported | 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 | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | 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, University of Illinois, Urbana, IL, USA | * | Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616 | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | CSIRO Oceans and Atmosphere, GPO Box 1538, Hobart, Tasmania, 7000, Australia | Inland Fisheries Institute, Oczapowskiego Street 10, 10-719 Olsztyn, Poland |
|
Contact Email
|
sandra.lavorel@ujf-grenoble.fr | harm.van.wijnen@rivm.nl | ane.zabaleta@ehu.es | kjbagstad@usgs.gov | camino.liquete@gmail.com | invest@naturalcapitalproject.org | abdelnouralex@gmail.com | yee.susan@epa.gov | yee.susan@epa.gov | markus.didion@wsl.ch | enviroatlas@epa.gov | abdallm@tcd.ie | dotis@iastate.edu | dotis@iastate.edu | dotis@iastate.edu | chris.murphy@idfg.idaho.gov | g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com | aht@illinois.edu | * | nmwilliams@ucdavis.edu | tedangradi@gmail.com | Heidi.Pethybridge@csiro.au | k.suska@infish.com.pl |
EM Description (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
|
EM ID
em.detail.idHelp
?
|
New or revised model | New or revised model | EM-275 | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | EM-632 | Application of existing model | Application of existing model | EM-734 | EM-735 | EM-777 | EM-796 | EM-812 | New or revised model | Application of existing model | maximize optimal habitat |
|
Summary Description
em.detail.summaryDescriptionHelp
?
|
ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. 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: "Maps play an important role during the entire process of spatial planning and bring ecosystem services to the attention of stakeholders' negotiation more easily. As example we show the quantification of the ecosystem service ‘natural attenuation of pollutants’, which is a service necessary to keep the soil clean for production of safe food and provision of drinking water, and to provide a healthy habitat for soil organisms to support other ecosystem services. A method was developed to plot the relative measure of the natural attenuation capacity of the soil in a map. Several properties of Dutch soils were related to property-specific reference values and subsequently combined into one proxy for the natural attenuation of pollutants." AUTHOR'S DESCRIPTION: "The natural attenuation capacity that is modeled in this study must be seen as a measure that describes the ‘biodegradation capacity’ of the soil, including biodegradation of all types of contaminants" | ABSTRACT: "We explored the potential impact of climate change on runoff and sediment yield for the Aixola watershed using the Soil and Water Assessment Tool (SWAT). The model calibration (2007–2010) and validation (2005–2006) results were rated as satisfactory. Subsequently, simulations were run for four climate change model–scenario combinations based on two general circulation models (CGCM2 and ECHAM4) under two emissions scenarios (A2 and B2) from 2011 to 2100." AUTHOR'S DESCRIPTION: "The results were grouped into three consecutive 30-yr periods (2011-2040, 2041-2070, and 2071-2100) and compared with the values simulated for the baseline period (1961-1990)." | 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: "For open space proximity, we mapped the relative value of open space, highways that impede walking access or reduce visual and soundscape quality, and housing locations, connected by a flow model simulating physical access to desirable spaces. We used reviews of the hedonic valuation literature (Bourassa et al. 2004, McConnell and Walls 2005) to inform model development, ranking the influence of different open space characteristics on property values to parameterize the source and sink models. The model includes a distance decay function that accounts for changes with distance in the value of open space. We then computed the ratio of actual to theoretical provision of open space to compare the values accruing to homeowners relative to those for the entire landscape." | ABSTRACT: "Mapping and assessment of ecosystem services is essential to provide scientific support to global and EU biodiversity policy. Coastal protection has been mostly analysed in the frame of coastal vulnerability studies or in local, habitat-specific assessments. This paper provides a conceptual and methodological approach to assess coastal protection as an ecosystem service at different spatial–temporal scales, and applies it to the entire EU coastal zone. The assessment of coastal protection incorporates 14 biophysical and socio-economic variables from both terrestrial and marine datasets. Those variables define three indicators: coastal protection capacity, coastal exposure and human demand for protection. A questionnaire filled by coastal researchers helped assign ranks to categorical parameters and weights to the individual variables. The three indicators are then framed into the ecosystem services cascade model to estimate how coastal ecosystems provide protection, in particular describing the service function, flow and benefit. The results are comparative and aim to support integrated land and marine spatial planning. The main drivers of change for the provision of coastal protection come from the widespread anthropogenic pressures in the European coastal zone, for which a short quantitative analysis is provided." | 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: "...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 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)…In lieu of surveys of diver opinion, recreational opportunities can also be estimated by actual field data of coral condition at preferred dive sites. A few studies have directly examined links between coral condition and production of recreational opportunities through field monitoring in an attempt to validate perceptions of recreational quality (Pendleton, 1994; Williams and Polunin, 2002; Leeworthy et al., 2004; Leujakand Ormond, 2007; Uyarraetal., 2009). Uyarraetal. (2009) used surveys to determine reef attributes related to diver perceptions of most and least favorite dive sites. Field data was used to narrow down the suite of potential preferred attributes to those that reflected actual site condition. We combined these attributes to form an index of dive site favorability: Dive site favorability = ΣipiRi where pi is the proportion of respondents indicating each attribute i that affected dive enjoyment positively. Ri is the mean relative magnitude of measured variables used to quantify each descriptive attribute i, including ‘fish abundance’ (pi=0.803), quantified by number of fish schools and fish species richness, and | 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." | DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map." | Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios. | ABSTRACT: "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)" | 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: "The migratory bird benefits of the 27 CREP sites were predicted for Grasshopper Sparrow (Ammodramus savannarum)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: GRSP density = e (-2.554612 + 0.0246975 * grass400 – 0.1032461 * trees400) | 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: "The migratory bird benefits of the 27 CREP sites were predicted for Savannah Sparrow (Passerculus sandwichensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SASP density = e^(-1.581362 + 0.0229603 *bbspath + 0.01024* grass3200 + 0.0255867 * hay3200) | 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: "1. Habitat management methods are crucial to maintaining habitats in the long term and ensuring vital resources are available for declining species. However, when management focuses on a single resource there is the potential to reduce or degrade other critical resources and negatively affect species of concern. Although both floral and nesting resources are critical to supporting bee populations, little consideration is given to the availability of nesting resources. Given known effects of management methods on soils, where the majority of bees nest, and floral food resources, increasing our understanding of management effects on both soils and floral resources is important to improving bee conservation efforts. 2. In 20 tallgrass prairie plots managed under 1 of 3 common methods: burning, haying and patch‐burn grazing, we assessed effects on bee communities and necessary above‐ and below‐ground resources. We also considered how management and resources affected below‐ground nesting in each prairie. 3. Management type affected both soil conditions and floral resources with patchburn grazing sites providing overall worse resources for bees compared to ungrazed sites. Soil conditions were also important for predicting most aspects of the bee community including abundance and community composition. Soil conditions also decreased floral richness and Floristic Quality Index (FQI). This suggests management affects bee communities both directly and indirectly through soil. 4. Increased nesting was observed in sites with greater floral abundance and soil conditions that correspond to increased bare ground, lower soil moisture and warmer soil temperatures suggesting management that helps increase floral abundance and improve soil conditions could be critical to increasing bee nesting. 5. Synthesis and applications. Measuring and tracking bare ground, Floristic Quality Index (FQI) and floral richness may help managers determine if their management methods are adversely affecting bees. Grazing and haying management negatively affected the bee community, vital nesting and…" floraresources and nesting rate. These managements may need to be avoided to meet bee conservation goals in prairies. Additionally, while soils have been largely overlooked, we found soil conditions to be an important predictor for bee communities and floral resources, and should be considered more explicitly in conserved areas. | * | 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: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context." | Calibration of complex, process-based ecosystem models is a timely task with modellers challenged by many parameters, multiple outputs of interest and often a scarcity of empirical data. Incorrect calibration can lead to unrealistic ecological and socio-economic predictions with the modeller’s experience and available knowledge of the modelled system largely determining the success of model calibration. Here we provide an overview of best practices when calibrating an Atlantis marine ecosystem model, a widely adopted framework that includes the parameters and processes comprised in many different ecosystem models. We highlight the importance of understanding the model structure and data sources of the modelled system. We then focus on several model outputs (biomass trajectories, age distributions, condition at age, realised diet proportions, and spatial maps) and describe diagnostic routines that can assist modellers to identify likely erroneous parameter values. We detail strategies to fine tune values of four groups of core parameters: growth, predator-prey interactions, recruitment and mortality. Additionally, we provide a pedigree routine to evaluate the uncertainty of an Atlantis ecosystem model based on data sources used. Describing best and current practices will better equip future modellers of complex, processed-based ecosystem models to provide a more reliable means of explaining and predicting the dynamics of marine ecosystems. Moreover, it promotes greater transparency between modellers and end-users, including resource managers. | Maintenance and restoration activities alter the river morphology and hydrology, and in consequence, alter fish habitats. The aim of this research was to investigate the change of habitat availability for fish guilds after carrying out maintenance works, commonly used river restoration measures and a restoration derived from fish habitat requirements. The selected study site is located at a close to natural condition section of Swider River in central Poland. The MesoHABSIM model was used to assess the area of suitable habitats in this site and predict habitat distribution at all planning scenarios. The affinity index which is a measure of similarity of two distributions showed that the likely distribution of habitats for fish resulting from simulated maintenance is 76.5% similar to that under measured conditions. The distribution of habitats caused by river restoration is also similar to that of the baseline in 73.2%. The resemblance between the restoration scenario focusing on fish habitat requirements and the reference conditions is 93.1%. It is beneficial to define the river restoration measures based on habitat availability for fish community. Modelling is a useful tool to simulate the changes and predict which guilds there is abundance of suitable habitats, and for which there are too few. It allows for more effective use of resources according to quantitative target states. |
|
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
* | * | Transport of solids for characterizing rivers in the European Water Framework Directive (WFD) | * | Supports global and EU biodiversity policy | * | * | * | * | * | * | climate change | * | * | * | * | * | Management strategies of prairie remnants for pollinator community | None identrified | None identified | * | N/A | None provided |
|
Biophysical Context
|
Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Five soil types including Löss, Fluvial clay, Peat, Sand, and Silty Loam. Five land-use types including Pasture, Arable farming, Semi-natural grassland, Heathland, and Forest. | The Aixola watershed drains into the Aixola reservoir, which has a cpacity of 2.73 x 10^6 m^3, and is used for water supply. The elevation ranges from 340 m at the outlet of the watershed to 750 m at the highest peak, with a mean elevation of 511 m a.s.l. Most slopes in the watershed are less than 30%. The region is characterized by a humid and temperate climate. The mean annual precipitation is about 1480 mm, distributed fairly evenly throughout the year.; the mean annual temperature is 12 degrees C; and the mean annual discharge is 600 mm (around 0.092 m^3 s^−1). Autochthonus vegetation is limited to small patches, and commercial foresty, mostly evergreen stands composed mainly of Pinus radiata (Monterey pine), occupies more than 80% of the watershed. The lithology is highly homogenous, with most of the bedrock (94%) consisting of impervious Upper Cretaceous Calcareous Flysch. The main types of soils are relatively deep cambisols and regosols, with depths ranging from 0.8 to 10 m and a silt-loam texture. During the 2003-2008 period, mean suspended sediment yield calculated for the watershed was 36 t km^-2. | No additional description provided | 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. | No additional description provided | No additional description provided | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Prairie pothole region of north-central Iowa | Prairie pothole region of north-central Iowa | 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. | No additional description provided | field plots near agricultural fruit and vegetable research farms | field plots near agricultural fields (mixed row crop, almond, walnuts), central valley, Ca | Waterfront districts on south Lake Michigan and south lake Erie | Marine ecosystem | Swider River, central Poland |
|
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
No scenarios presented | No scenarios presented | Four future climate change scenarios combining two IPCC SRES scenarios and two GCMs | No scenarios presented | 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). |
No scenarios presented | air temperature, precipitation, Atmospheric CO2 concentrations | No scenarios presented | No scenarios presented | No scenarios presented | Sites, function or habitat focus | Additional benefits due to biodiversity restoration practices | Alternative management strategies: burning, haying and patch‐burn grazing | * | Varied wildflower planting mixes of annuals and perennials | N/A | No scenarios presented | 4. Ask the fish scenario (maximize optimal habitat) |
EM Relationship to Other EMs or Applications
|
EM ID
em.detail.idHelp
?
|
New or revised model | New or revised model | EM-275 | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | EM-632 | Application of existing model | Application of existing model | EM-734 | EM-735 | EM-777 | EM-796 | EM-812 | New or revised model | Application of existing model | maximize optimal habitat |
|
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application | Method + Application | Method + Application (multiple runs exist) | Method + Application | 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 | Method + Application (multiple runs exist) | Method + Application (multiple runs exist) | Method + Application | Method + Application | Method + Application (multiple runs exist) | Method + Application (multiple runs exist) | Method + Application (multiple runs exist) | Method + Application (multiple runs exist) | Method + Application (multiple runs exist) | Method + Application | Method Only | Model Run Associated with a Specific EM Application |
|
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | New or revised model | Application of existing model | New or revised model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
Application of existing model ?Comment:Models developed by Quamen (2007). |
WESP Deepwater Marsh | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | maximize optimal habitat |
Related EMs (for example, other versions or derivations of this EM) described in ESML
em.detail.relatedEmHelp
?
|
EM ID
em.detail.idHelp
?
|
New or revised model | New or revised model | EM-275 | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | EM-632 | Application of existing model | Application of existing model | EM-734 | EM-735 | EM-777 | EM-796 | EM-812 | New or revised model | Application of existing model | maximize optimal habitat |
|
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
Doc-260 | Doc-269 | Doc-288 | None | Doc-303 | Doc-305 | None | Doc-309 | Doc-13 | Doc-317 | Doc-335 | None | Doc-342 | Doc-344 | None | None | None | Doc-372 | Doc-372 | Doc-390 | None | None | None | None | Doc-422 | Doc-456 | None |
|
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83 | None | None | None | None | EM-349 | EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | EM-447 | EM-448 | None | EM-466 | EM-469 | EM-480 | EM-485 | None | EM-598 | EM-705 | EM-703 | EM-702 | EM-701 | EM-700 | EM-652 | EM-651 | EM-650 | EM-648 | EM-648 | EM-649 | EM-650 | EM-651 | EM-718 | EM-729 | EM-743 | EM-756 | EM-757 | EM-759 | EM-760 | EM-761 | EM-763 | EM-764 | EM-766 | EM-767 | EM-751 | EM-768 | None | None | EM-784 | EM-784 | EM-793 | EM-886 | EM-888 | EM-890 | EM-891 | EM-893 | EM-894 | EM-895 | EM-978 | EM-983 | EM-985 | EM-990 | EM-991 | 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)
|
EM ID
em.detail.idHelp
?
|
New or revised model | New or revised model | EM-275 | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | EM-632 | Application of existing model | Application of existing model | EM-734 | EM-735 | EM-777 | EM-796 | EM-812 | New or revised model | Application of existing model | maximize optimal habitat |
|
EM Temporal Extent
em.detail.tempExtentHelp
?
|
2007-2008 | 1999-2005 | 1961-2100 | 2000-2011 | 1992-2010 | Not applicable | 1969-2008 | 2006-2007, 2010 | 2006-2007, 2010 | 1993-2013 | 2006-2013 | 1961-1990 | 2002-2007 | 2002-2007 | 1992-2007 | 2010-2013 | 1990-2007 | 2012-2016 | 2010-2011 | 2011-2012 | 2022 | Not applicable | 2014 |
|
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-stationary | time-stationary | * | time-stationary | time-stationary | * | * | time-stationary | time-stationary | * | time-stationary | * | time-stationary | time-stationary | time-stationary | * | time-stationary | time-stationary | * | time-dependent | time-stationary | * | time-stationary |
|
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
Not applicable | Not applicable | future time | Not applicable | Not applicable | future time | future time | Not applicable | Not applicable | future time | Not applicable | both | Not applicable | Not applicable | Not applicable | * | Not applicable | Not applicable | * | past time | Not applicable | Not applicable | Not applicable |
|
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
Not applicable | Not applicable | continuous | Not applicable | Not applicable | * | * | Not applicable | Not applicable | * | Not applicable | * | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | * | discrete | Not applicable | continuous | Not applicable |
|
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
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 | * | 1 | Not applicable | Not applicable | Not applicable |
|
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | * | Day | Not applicable | Not applicable | * | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | * | Year | Not applicable | Not applicable | Not applicable |
EM spatial extent (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
|
EM ID
em.detail.idHelp
?
|
New or revised model | New or revised model | EM-275 | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | EM-632 | Application of existing model | Application of existing model | EM-734 | EM-735 | EM-777 | EM-796 | EM-812 | New or revised model | Application of existing model | maximize optimal habitat |
|
Bounding Type
em.detail.boundingTypeHelp
?
|
Physiographic or Ecological | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical | Not applicable | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Geopolitical | Geopolitical | * | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Other | Geopolitical |
* ?Comment:This is a guess based on information in the document. 3 field sites were separated by up to 9km |
Point or points ?Comment:This is a guess based on information in the document. 3 field sites were separated by up to 9km |
Geopolitical | Not applicable | Watershed/Catchment/HUC |
|
Spatial Extent Name
em.detail.extentNameHelp
?
|
Central French Alps | The Netherlands | Aixola watershed | Puget Sound Region | Shoreline of the European Union-27 | Not applicable | H. J. Andrews LTER WS10 | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Switzerland | conterminous United States | Oak Park Research centre | CREP (Conservation Reserve Enhancement Program) wetland sites | CREP (Conservation Reserve Enhancement Program) wetland sites | CREP (Conservation Reserve Enhancement Program) wetland sites | Wetlands in Idaho | Colt Park meadows, Ingleborough National Nature Reserve, northern England | Counties: Barton, St. Clair, Cedar, Dade and Polk | * | Agricultural plots | Great Lakes waterfront | Not applicable | Swider River |
|
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
* | 10,000-100,000 km^2 | 1-10 km^2 | 10,000-100,000 km^2 | >1,000,000 km^2 | Not applicable | 10-100 ha | 100-1000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | >1,000,000 km^2 | 1-10 ha | 1-10 km^2 | 1-10 km^2 | 1-10 km^2 | 100,000-1,000,000 km^2 | <1 ha | 1000-10,000 km^2. | * | 10-100 km^2 | 1000-10,000 km^2. | Not applicable | 1000-10,000 km^2. |
Spatial Distribution of Computations (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
|
EM ID
em.detail.idHelp
?
|
New or revised model | New or revised model | EM-275 | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | EM-632 | Application of existing model | Application of existing model | EM-734 | EM-735 | EM-777 | EM-796 | EM-812 | New or revised model | Application of existing model | maximize optimal habitat |
|
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?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) | 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) | * | Not applicable | spatially distributed (in at least some cases) |
|
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
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 | area, for pixel or radial feature | volume, for 3-D feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | * | other (specify), for irregular (e.g., stream reach, lake basin) | 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 | * | Not applicable | * | * | length, for linear feature (e.g., stream mile) |
|
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
20 m x 20 m | 100 m x 100 m | Average size 0.2 km^2 | 200m x 200m | Irregular | application specific | 30 m x 30 m surface pixel and 2-m depth soil column | 10 m x 10 m | 10 m x 10 m | 5 sites | irregular | * | multiple, individual, irregular shaped sites | multiple, individual, irregular shaped sites | multiple, individual, irregular shaped sites | * | 3 m x 3 m | 1 ha | * | Not applicable | * | * | Irregular |
EM Structure and Computation Approach (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
|
EM ID
em.detail.idHelp
?
|
New or revised model | New or revised model | EM-275 | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | EM-632 | Application of existing model | Application of existing model | EM-734 | EM-735 | EM-777 | EM-796 | EM-812 | New or revised model | Application of existing model | maximize optimal habitat |
|
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | * | Analytic | Analytic | * | Analytic | * | Analytic | Analytic | Analytic | * | Analytic | Analytic | * | Numeric | * | Analytic | Analytic |
|
EM Determinism
em.detail.deterStochHelp
?
|
* | * | * | stochastic | * | * | * | * | * | stochastic | * | * | * | * | * | * | stochastic | * | * | deterministic | * | * | * |
|
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
* | * |
|
|
* | * | * | * | * |
|
|
* | * | * | * | * | * | * | * |
|
* | * | * |
Model Checking Procedures Used (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
|
EM ID
em.detail.idHelp
?
|
New or revised model | New or revised model | EM-275 | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | EM-632 | Application of existing model | Application of existing model | EM-734 | EM-735 | EM-777 | EM-796 | EM-812 | New or revised model | Application of existing model | maximize optimal habitat |
|
Model Calibration Reported?
em.detail.calibrationHelp
?
|
* | * | Yes | * | * | Not applicable | * | Yes | Yes | * | * | * | Unclear | Unclear | Unclear | * | Not applicable | * | * | No | * | Yes | Unclear |
|
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
Yes | * | * | * | * | Not applicable | * | * | * | * | * |
Yes ?Comment:for N2O fluxes |
* | * | * | * | Not applicable | Not applicable | * | No | * | Not applicable | Not applicable |
|
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
|
* | * | * | * | * | * | * | * | * | * |
|
* | * | * | * | * | * | * | None | * | * | * |
|
Model Operational Validation Reported?
em.detail.validationHelp
?
|
* | * | Yes | * | * | Not applicable | * | Yes | Yes | Yes | * | Yes | Unclear | Unclear | Unclear | * | * | * | * | No | * | Not applicable | Not applicable |
|
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
* | * | * | * | * | Not applicable | * | * | * | * | * | * | * | * | * | * | * | * | * | No | * | Not applicable | * |
|
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
* | * | Yes | * | * | Not applicable | * | * | * | * | * | * | * | * | * | * | * | Yes | * | No | Yes | Not applicable | * |
|
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
* | * | No | * | * | * | * | * | * | * | * | * | * | * | * | * | * | No | * | 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
?
| New or revised model | New or revised model | EM-275 | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | EM-632 | Application of existing model | Application of existing model | EM-734 | EM-735 | EM-777 | EM-796 | EM-812 | New or revised model | Application of existing model | maximize optimal habitat |
|
|
|
|
|
* |
|
* | * |
|
|
|
|
|
|
|
|
|
|
|
|
* |
|
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
?
| New or revised model | New or revised model | EM-275 | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | EM-632 | Application of existing model | Application of existing model | EM-734 | EM-735 | EM-777 | EM-796 | EM-812 | New or revised model | Application of existing model | maximize optimal habitat |
| * | * | * |
|
|
* | * |
|
|
* | * | * | * | * | * | * | * | * | * | None | * | * | * |
Centroid Lat/Long (Decimal Degree)
|
EM ID
em.detail.idHelp
?
|
New or revised model | New or revised model | EM-275 | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | EM-632 | Application of existing model | Application of existing model | EM-734 | EM-735 | EM-777 | EM-796 | EM-812 | New or revised model | Application of existing model | maximize optimal habitat |
|
Centroid Latitude
em.detail.ddLatHelp
?
|
45.05 | 52.37 | 43 | 48 | 48.2 | -9999 | 44.25 | 17.73 | 17.73 | 46.82 | 39.5 | 52.86 | 42.62 | 42.62 | 42.62 | 44.06 | 54.2 | 37.68 | 43.87 | 29.4 | 42.26 | Not applicable | 52.23 |
|
Centroid Longitude
em.detail.ddLongHelp
?
|
6.4 | 4.88 | -1 | -123 | 16.35 | -9999 | -122.33 | -64.77 | -64.77 | 8.23 | -98.35 | 6.54 | -93.84 | -93.84 | -93.84 | -114.69 | -2.35 | -93.71 | -85.64 | -82.18 | -87.84 | Not applicable | 21.01 |
|
Centroid Datum
em.detail.datumHelp
?
|
* | * | * | * | * | Not applicable | * | * | * | * | * | None provided | * | * | * | * | * | * | * | WGS84 | * | Not applicable | * |
|
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
* | Estimated | * | Estimated | Estimated | Not applicable | * | Estimated | Estimated | Estimated | Estimated | * | Estimated | Estimated | Estimated | Estimated | * | Estimated | * | Provided | Estimated | 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)
|
EM ID
em.detail.idHelp
?
|
New or revised model | New or revised model | EM-275 | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | EM-632 | Application of existing model | Application of existing model | EM-734 | EM-735 | EM-777 | EM-796 | EM-812 | New or revised model | Application of existing model | maximize optimal habitat |
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Rivers and Streams | Forests | Barren | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Not applicable | Forests | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Forests | * | * | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Grasslands | * | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas | Rivers and Streams |
|
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Subalpine terraces, grasslands, and meadows | Not applicable | Forested watershed used for commercial forestry | Terrestrial environment surrounding a large estuary | Coastal zones | 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). | Coral reefs | Coral reefs | forests | Terrestrial | farm pasture | Wetlands buffered by grassland set in agricultural land | Grassland buffering inland wetlands set in agricultural land | Grassland buffering inland wetlands set in agricultural land | created, restored and enhanced wetlands | fertilized grassland (historically hayed) | Remnant tallgrass prairie | * | Agricultural landscape | Lake Michigan & Lake Erie waterfront | Multiple | temperate river system |
|
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
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 | Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | * | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 | * | * | * |
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
?
|
EM ID
em.detail.idHelp
?
|
New or revised model | New or revised model | EM-275 | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | EM-632 | Application of existing model | Application of existing model | EM-734 | EM-735 | EM-777 | EM-796 | EM-812 | New or revised model | Application of existing model | maximize optimal habitat |
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Community | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Community | Not applicable | Not applicable | * | * | * | Not applicable | Community | * | * | Species | Not applicable | Not applicable | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
?
| New or revised model | New or revised model | EM-275 | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | EM-632 | Application of existing model | Application of existing model | EM-734 | EM-735 | EM-777 | EM-796 | EM-812 | New or revised model | Application of existing model | maximize optimal habitat |
| * | * | * | * | * | * | * | * | * | * | * | * |
|
|
|
* | * |
|
|
|
* | * |
|
EnviroAtlas URL
em.detail.enviroAtlasURLHelp
?
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
?
| New or revised model | New or revised model | EM-275 | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | EM-632 | Application of existing model | Application of existing model | EM-734 | EM-735 | EM-777 | EM-796 | EM-812 | New or revised model | Application of existing model | maximize optimal habitat |
| * |
|
|
|
|
|
* |
|
|
|
* |
|
|
|
|
|
|
|
* |
|
* |
|
|
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
?
| New or revised model | New or revised model | EM-275 | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | EM-632 | Application of existing model | Application of existing model | EM-734 | EM-735 | EM-777 | EM-796 | EM-812 | New or revised model | Application of existing model | maximize optimal habitat |
| * | * |
|
|
|
* | * |
|
|
* | * |
|
|
|
|
* | * | * | * |
|
* |
|
|
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
?
Intermediate
|
EM ID
em.detail.idHelp
?
|
New or revised model | New or revised model | EM-275 | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | EM-632 | Application of existing model | Application of existing model | EM-734 | EM-735 | EM-777 | EM-796 | EM-812 | New or revised model | Application of existing model | maximize optimal habitat |
|
Intermediate (Computed) Variables (and Units)
em.detail.intermediateVariableHelp
?
|
None | None |
|
None | None | None | None | None | None | None | None | None | None | None | None | None |
Response
em.detail.variablesResponseHelp
?