EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
EM Identity and Description
EM ID
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EM-68 | EM-71 | EM-79 | EM-93 |
EM-125 ![]() |
EM-127 ![]() |
EM-133 |
EM-186 ![]() |
EM-208 ![]() |
EM-275 ![]() |
EM-326 |
EM-333 ![]() |
EM-424 | EM-464 |
EM-485 ![]() |
EM-549 | EM-592 | EM-657 | EM-712 |
EM-718 ![]() |
EM-743 ![]() |
EM-774 ![]() |
EM Short Name
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Fodder crude protein content, Central French Alps | Community flowering date, Central French Alps | Divergence in flowering date, Central French Alps | Stream nitrogen removal, Mississippi R. basin, USA | Land-use change and recreation, Europe | Annual profit - carbon plantings, South Australia | Flood regulation supply-demand, Etropole, Bulgaria | FORCLIM v2.9, Western OR, USA | FORCLIM v2.9, Santiam watershed, OR, USA | SWAT, Aixola watershed, Spain | ARIES flood regulation, Puget Sound Region, USA | Evoland v3.5 (unbounded growth), Eugene, OR, USA | Denitrification rates, Guánica Bay, Puerto Rico | Mangrove connectivity, St. Croix, USVI | Yasso07 v1.0.1, Switzerland, site level | Nutrient Tracking Tool (NTT) | APEX v1501 | REQI (River Ecosystem Quality Index), Italy | ESII Tool method | WESP: Riparian & stream habitat, ID, USA | WESP: Irrigation water, ID, USA | Plant-pollinator networks at reclaimed mine, USA |
EM Full Name
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Fodder crude protein content, Central French Alps | Community weighted mean flowering date, Central French Alps | Functional divergence in flowering date, Central French Alps | Stream nitrogen removal, Upper Mississippi, Ohio and Missouri River sub-basins, USA | Land-use change effects on recreation, Europe | Annual profit from carbon plantings, South Australia | Flood regulation supply vs. demand, Municipality of Etropole, Bulgaria | FORCLIM (FORests in a changing CLIMate) v2.9, Western OR, USA | FORCLIM (FORests in a changing CLIMate) v2.9, Santiam watershed, OR, USA | SWAT (Soil and Water Assessment Tool), Aixola watershed, Spain | ARIES (Artificial Intelligence for Ecosystem Services) Flood Regulation, Puget Sound Region, Washington, USA | Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA | Denitrification rates, Guánica Bay, Puerto Rico, USA | Mangrove connectivity (of reef), St. Croix, USVI | Yasso07 v1.0.1 forest litter decomposition, Switzerland, site level | Nutrient Tracking Tool (NTT) | APEX (Agricultural Policy/Environmental eXtender Model) v1501 | REQI (River Ecosystem Quality Index), Marecchia River, Italy | ESII (Ecosystem Services Identification & Inventory) Tool method | WESP: Riparian and stream habitat focus projects, ID, USA | WESP: Irrigation return water treatment, Idaho, USA | Restoration of plant-pollinator networks at reclaimed strip mine, Ohio, USA |
EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | US EPA | EU Biodiversity Action 5 | None | EU Biodiversity Action 5 | US EPA | US EPA | None | ARIES | Envision | US EPA | US EPA | None | None | None | None | None | None | None | None |
EM Source Document ID
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260 | 260 | 260 | 52 | 228 | 243 | 248 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
295 | 302 |
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
338 | 335 | 343 | 352 | 357 | 378 |
391 ?Comment:Website for online user support |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
397 |
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. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Hill, B. and Bolgrien, D. | Haines-Young, R., Potschin, M. and Kienast, F. | Crossman, N. D., Bryan, B. A., and Summers, D. M. | Nedkov, S., Burkhard, B. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | 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. | Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Didion, M., B. Frey, N. Rogiers, and E. Thurig | Saleh, A. and O. Gallego | Steglich, E. M., J. Jeong and J. R. Williams | Santolini, R, E. Morri, G. Pasini, G. Giovagnoli, C. Morolli, and G. Salmoiraghi | EcoMetrix Solutions Group (ESG) | Murphy, C. and T. Weekley | Murphy, C. and T. Weekley | Cusser, S. and K. Goodell |
Document Year
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2011 | 2011 | 2011 | 2011 | 2012 | 2011 | 2012 | 2007 | 2007 | 2014 | 2014 | 2008 | 2017 | 2014 | 2014 | 2018 | 2016 | 2014 | 2016 | 2012 | 2012 | 2013 |
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 | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Nitrogen removal by streams and rivers of the Upper Mississippi River basin | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Carbon payments and low-cost conservation | Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | 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 | Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Validating tree litter decomposition in the Yasso07 carbon model | Nutrient Tracking Tool (NTT) User Manual | Agricultural Policy/Environmental eXtender Model User's Manual Version 1501 | Assessing the quality of riparian areas: the case of River Ecosystem Quality Index applied to the Marecchia river (Italy) | ESII Tool | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Diversity and distribution of floral resources influence the restoration of plant-pollinator networks on a reclaimed strip mine |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Other or unclear (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | webpage | Published report | Published journal manuscript | Website | Published report | Published report | Published journal manuscript |
EM ID
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EM-68 | EM-71 | EM-79 | EM-93 |
EM-125 ![]() |
EM-127 ![]() |
EM-133 |
EM-186 ![]() |
EM-208 ![]() |
EM-275 ![]() |
EM-326 |
EM-333 ![]() |
EM-424 | EM-464 |
EM-485 ![]() |
EM-549 | EM-592 | EM-657 | EM-712 |
EM-718 ![]() |
EM-743 ![]() |
EM-774 ![]() |
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://swat.tamu.edu/software/arcswat/ | http://aries.integratedmodelling.org/ | http://evoland.bioe.orst.edu/ | Not applicable | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | http://ntt.tiaer.tarleton.edu/welcomes/new?locale=en | https://epicapex.tamu.edu/manuals-and-publications/ | Not applicable | https://www.esiitool.com/ | Not applicable | Not applicable | Not applicable | |
Contact Name
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Sandra Lavorel | Sandra Lavorel | Sandra Lavorel | Brian Hill | Marion Potschin | Neville D. Crossman | Stoyan Nedkov | Richard T. Busing | Richard T. Busing | Ane Zabaleta | Ken Bagstad | Michael R. Guzy | Susan H. Yee | Susan H. Yee | Markus Didion |
Ali Saleh ?Comment:Phone # 254-968-9079 |
E. M. Steglich | Elisa Morri | Not reported | Chris Murphy | Chris Murphy |
Sarah Cusser ?Comment:Department of Evolution, Ecology, and Organismal Biology, Ohio State University, 318 West 12th Avenue, Columbus, OH 43202, U.S.A. |
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 | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Mid-Continent Ecology Division NHEERL, ORD. USEPA 6201 Congdon Blvd. Duluth, MN 55804, USA | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia | National Institute of Geophysics, Geodesy and Geography, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, bl.3, 1113 Sofia, Bulgaria | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | 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 | Oregon State University, Dept. of Biological and Ecological Engineering | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Associate Director, Texas Institute for Applied Environmental Research, P.O. Box T410, Tarleton State University Stephenville, TX 76402 | Blackland Research and Extension Center, 720 East Blackland Road, Temple, TX 76502 | Dept. of Earth, Life, and Environmental Sciences, Urbino university, via ca le suore, campus scientifico Enrico Mattei, Urbino 61029 Italy | Not reported | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Department of Evolution, Ecology, and Behavior, School of Biological Sciences, The University of Texas at Austin, 100 East 24th Street Stop A6500, Austin, TX 78712-1598, U.S.A. |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | hill.brian@epa.gov | marion.potschin@nottingham.ac.uk | neville.crossman@csiro.au | snedkov@abv.bg | rtbusing@aol.com | rtbusing@aol.com | ane.zabaleta@ehu.es | kjbagstad@usgs.gov | Not reported | yee.susan@epa.gov | yee.susan@epa.gov | markus.didion@wsl.ch | saleh@tarleton.edu | epicapex@brc.tamus.edu | elisa.morri@uniurb.it | Not reported | chris.murphy@idfg.idaho.gov | chris.murphy@idfg.idaho.gov | sarah.cusser@gmail.com |
EM ID
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EM-68 | EM-71 | EM-79 | EM-93 |
EM-125 ![]() |
EM-127 ![]() |
EM-133 |
EM-186 ![]() |
EM-208 ![]() |
EM-275 ![]() |
EM-326 |
EM-333 ![]() |
EM-424 | EM-464 |
EM-485 ![]() |
EM-549 | EM-592 | EM-657 | EM-712 |
EM-718 ![]() |
EM-743 ![]() |
EM-774 ![]() |
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 (e.g., fodder crude protein content), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in fodder crude protein content was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy…Fodder crude protein for each pixel was calculated and mapped using model estimates...This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on fodder protein content. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use." | 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: "Community-weighted mean date of flowering onset 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. 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: "We used stream chemistry and hydrogeomorphology data from 549 stream and 447 river sites to estimate NO3–N removal in the Upper Mississippi, Missouri, and Ohio Rivers. We used two N removal models to predict NO3–N input and removal. NO3–N input ranged from 0.01 to 338 kg/km*d in the Upper Mississippi River to 0.01–54 kg/ km*d in the Missouri River. Cumulative river network NO3–N input was 98700–101676 Mg/year in the Ohio River, 85,961–89,288 Mg/year in the Upper Mississippi River, and 59,463–61,541 Mg/year in the Missouri River. NO3–N output was highest in the Upper Mississippi River (0.01–329 kg/km*d ), followed by the Ohio and Missouri Rivers (0.01–236 kg/km*d ) sub-basins. Cumulative river network NO3–N output was 97,499 Mg/year for the Ohio River, 84,361 Mg/year for the Upper Mississippi River, and 59,200 Mg/year for the Missouri River. Proportional NO3–N removal (PNR) based on the two models ranged from 0.01 to 0.28. NO3–N removal was inversely correlated with stream order, and ranged from 0.01 to 8.57 kg/km*d in the Upper Mississippi River to 0.001–1.43 kg/km*d in the Missouri River. Cumulative river network NO3–N removal predicted by the two models was: Upper Mississippi River 4152 and 4152 Mg/year, Ohio River 3743 and 378 Mg/year, and Missouri River 2,277 and 197 Mg/year. PNR removal was negatively correlated with both stream order (r = −0.80–0.87) and the percent of the catchment in agriculture (r = −0.38–0.76)." | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The novel aspect of this work is an analysis of whether the historical and the projected land use changes for the periods 1990–2000, 2000–2006, and 2000–2030 are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Recreation); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000 and 2000–2006 (LEAC, EEA, 2006) and EURURALIS 2.0 land use scenarios for 2000–2030. The results are reported at three spatial reporting units, i.e. (1) the NUTS-X regions, (2) the bioclimatic regions, and (3) the dominant landscape types." AUTHOR'S DESCRIPTION: " 'Recreation' is broadly defined as all areas where landscape properties are favourable for active recreation purposes….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers…The futures component of the work was based on EURURALIS 2.0 land use scenarios for 2000–2030, which are based on the four IPCC SRES land use scenarios." | ABSTRACT: "A price on carbon is expected to generate demand for carbon offset schemes. This demand could drive investment in tree-based monocultures that provide higher carbon yields than diverse plantings of native tree and shrub species, which sequester less carbon but provide greater variation in vegetation structure and composition. Economic instruments such as species conservation banking, the creation and trading of credits that represent biological-diversity values on private land, could close the financial gap between monocultures and more diverse plantings by providing payments to individuals who plant diverse species in locations that contribute to conservation and restoration goals. We studied a highly modified agricultural system in southern Australia that is typical of many temperate agriculture zones globally (i.e., has a high proportion of endangered species, high levels of habitat fragmentation, and presence of non-native species). We quantified the economic returns...from carbon plantings (monoculture and mixed tree and shrubs) under six carbon-price scenarios." AUTHOR'S DESCRIPTION: "The economic returns of carbon plantings are highly variable and depend primarily on carbon yield and price and opportunity costs (Newell & Stavins 2000; Richards & Stokes 2004; Torres et al. 2010)...The spatial variation in carbon yield and costs, including establishment, maintenance, transaction, and opportunity costs, means that the net economic returns of carbon plantings are also likely to vary spatially." | ABSTRACT: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. Maps of demands for flood regulating ecosystem services in the study region were compiled based on a digital elevation model, land use information and accessibility data. Finally, the flood regulating ecosystem service supply and demand data were merged in order to produce a map showing regional supply-demand balances.The flood regulation ecosystem service demand map shows that areas of low or no relevant demands far exceed the areas of high and very high demands, which comprise only 0.6% of the municipality’s area. According to the flood regulation supply-demand balance map, areas of high relevant demands are located in places of low relevant supply capacities" AUTHOR'S DESCRIPTION: "A similar relative scale ranging from 0 to 5 was applied to assess the demands for flood regulation. A 0-value indicates that there is no relevant demand for flood regulation and 5 would indicate the highest demand for flood regulation within the case study region. Values of 2, 3 and 4 represent respective intermediate demands. The calculations were based on the assumption that the most vulnerable areas would have the highest demand for flood regulation. The vulnerability, defined as “the characteristics and circumstances of a community, system or asset that make it susceptible to the damaging effects of a hazard” (UN/ISDR, 2009), has different dimensions (e.g. social, economic, environmental, institutional). The most vulnerable places in the case study area were defined by using different sources of demographic, statistical, topographic and economic data (Nikolova et al., 2009). These areas will have the highest (5-value) demand for flood regulation…For analyzing source and sink dynamics and to identify flows of ecosystem services, the information in the matrixes and in the maps of ecosystem service supply and demand can be merged (Burkhard et al., 2012). As the landscapes’ flood regulation supply and demand are not analyzed and modeled in the same units it is not possible to calculate the balance between them quantitatively. Using the relative scale (0–5) it becomes possible to compare them and to calculate supply-demand budgets. Although this does not providea clear indication of whether there is excess supply or demand, the resulting map shows where areas of qualitatively high demand correspond with low supply and vice versa." | ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range…The simulation of both stand-replacing and partial-stand disturbances across western Oregon improved agreement between simulated and actual data." Western Oregon forested ecoregions (Omernick classification) were Coastal Volcanics (1d), Mid-coastal Sedimentary (1g), Willamette Valley (3), West Cascade Lowlands (4a), West Cascade Montane (4b), Cascade Crest (4c), East Cascade Ponderosa Pine (9d), and East Cascade Pumice Plateau (9e). | ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range. It was then applied to simulate present and future (1990-2050) forest landscape dynamics of a watershed in the west Cascades. Various regimes of climate change and harvesting in the watershed were considered in the landscape application." AUTHOR'S DESCRIPTION: "Effects of different management histories on the landscape were incorporated using the land management (conservation, plan, or development trend) and forest age categories…the plan trend was an intermediate alternative, representing the continuation of current policies and trends, whereas the conservation and development trends were possible alternatives…Non-forested areas were given a forest age of zero; forested areas were assigned to one of eight forest age classes: >0-20 yr, 21-40 yr, 41-60 yr, 61-80 yr, 81-200 yr, 201-400 yr, and >600 yr in 1990…two climate change scenarios were used, representing lower and upper extremes projected by a set of global climate models: (1) minor warming with drier summers, and (2) major warming with wetter conditions…For the first scenario, temperature was increased by 0.5°C in 2025 and by 1.5°C in 2045. Precipitation from October to March was increased 2% in 2025 and decreased 2% in 2045. Precipitation from April to September was decreased 4% in 2025 and 7% in 2045. For the second scenario, temperature was by increased 2.6°C in 2025 and by 3.2°C in 2045. Precipitation from October to March was increased 18% in 2025 and 22% in 2045. Precipitation from April to September was increased 14% in 2025 and 9% in 2045. | 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: "We estimated flood sinks, i.e., the capacity of the landscape to intercept, absorb, or detain floodwater, using a Bayesian model of vegetation, topography, and soil influences (Bagstad et al. 2011). This green infrastructure, the ecosystem service that we used for subsequent analysis, can combine with anthropogenic gray infrastructure, such as dams and detention basins, to provide flood regulation. Since flood regulation implies a hydrologic connection between sources, sinks, and users, we simulated its flow through a threestep process. First, we aggregated values for precipitation (sources of floodwater), flood mitigation (sinks), and users (developed land located in the 100-year floodplain) within each of the 502 12-digit Hydrologic Unit Code (HUC) watersheds within the Puget Sound region. Second, we subtracted the sink value from the source value for each subwatershed to quantify remaining floodwater and the proportion of mitigated floodwater. Third, we multiplied the proportion of mitigated floodwater for each subwatershed by the number of developed raster cells within the 100-year floodplain to yield a ranking of flood mitigation for each subwatershed...We calculated the ratio of actual to theoretical flood sinks by dividing summed flood sink values for subwatersheds providing flood mitigation to users by summed flood sink values for the entire landscape without accounting for the presence of at-risk structures." | **Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | AUTHOR'S DESCRIPTION: "Improving water quality was an objective of stakeholders in order to improve human health and reduce impacts to coral reef habitats. Four ecosystem services contributing to water quality were identified: denitrification...Denitrification rates were assigned to each land cover class, applying the mean of rates for natural sub-tropical ecosystems obtained from the literature…" | 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…An alternative method to estimate potential fisheries production is to quantify not just the percent coverages of key habitats (F1)–(F6), but the degree of connectivity among those habitats. Many species that utilize coral reef habitat as adults are dependent on mangrove or seagrass nursery habitats as juveniles (Nagelkerken et al., 2000; Dorenbosch et al., 2006). In the Caribbean, the community structure or adult biomass of more than 150 reef fish species was affected by the presence of mangroves in the vicinity of reefs (Mumby et al., 2004). The value of habitat for fish production will therefore depend on the degree of connectivity between reefs and nearby mangroves (Mumby, 2006) and can be estimated as Cij = D - √(mix-rix)2+(mjy-rjy)2 where Cij is the connectivity between each reef cell i and nearby mangrove cell j, and D is the maximum migratory distance between mangroves and reefs (assumed to be 10 km), weighted by the distance between cells (x,y coordinates) such that shorter distances result in greater connectivity. The row sums then give the total connectivity of each reef cell to mangroves." | 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... (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests; and (iii) evaluate the suitability of Yasso07 for regional and national scale applications 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)." "The decomposition of below- and aboveground litter was studied over 10 years on five forest sites in Switzerland…" "At the time of this study, three parameter sets have been developed and published:... (3): Rantakari et al., 2012 (henceforth P12)… For the development of P12, Rantakari et al. (2012) obtained a subset of the previously used data which was restricted to European sites." "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 lit-ter (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 r | AUTHOR'S DESCRIPTION: "The Nutrient Tracking Tool (NTT) was designed and developed by the Texas Institute for Applied Environmental Research (TIAER), Tarleton State University with funding from USDA Office of Environmental Markets, USDA-NRCS Conservation Innovation Grants program, and various state agencies. NTT is a web-based, site-specific application that estimates nutrient and sediment losses at the field scale or at the small watershed scale. Agricultural producers and land managers can define a number of management scenarios and generate a report showing the expected nutrient loss differences between any selected scenarios for a given field or small watershed. NTT compares agricultural management systems to calculate a change in expected flow, nitrogen, phosphorus, sediment losses, and crop yield. Estimates are made using the APEX model (Williams et al. 2000). Results represent average losses from the field based on 35 years of simulated weather. NTT requires regional soils, climate and site-specific crop management information. NTT currently provides selections for all regions of U.S. and Puerto Rico territory, but it has only been validated for a limited number of states and counties. As validation becomes possible in other parts of the country, parameter files may be updated for additional counties in future versions. There are two versions of new NTT program available: The BASIC version is a user-friendly version of NTT that allows users to estimate N, P and sediment from crop and pasture lands. The Research and Education version of NTT (NTT-RE) was developed for researchers and educational institutes for teaching and training purposes. NTT-RE includes additional functions allowing the user to view and edit soil layers, view crop water and nutrient stresses, and modify and the APEX parameters file for calibration and validation purposes. The data sources and APEX simulations in both versions are identical. For more information regarding NTT, please refer to Saleh et al. (2011 and 2015)." | ABSTRACT: "APEX is a tool for managing whole farms or small watersheds to obtain sustainable production efficiency and maintain environmental quality. APEX operates on a daily time step and is capable of performing long term simulations (1-4000 years) at the whole farm or small watershed level. The watershed may be divided into many homogeneous (soils, land use, topography, etc.) subareas (<4000). The routing component simulates flow from one subarea to another through channels and flood plains to the watershed outlet and transports sediment, nutrients, and pesticides. This allows evaluation of interactions between fields in respect to surface run-on, sediment deposition and degradation, nutrient and pesticide transport and subsurface flow. Effects of terrace systems, grass waterways, strip cropping, buffer strips/vegetated filter strips, crop rotations, plant competition, plant burning, grazing patterns of multiple herds, fertilizer, irrigation, liming, furrow diking, drainage systems, and manure management (feed yards and dairies with or without lagoons) can be simulated and assessed. Most recent developments in APEX1501 include: • Flexible grazing schedule of multiple owners and herds across landscape and paddocks. • Wind dust distribution from feedlots. • Manure erosion from feedlots and grazing fields. • Optional pipe and crack flow in soil due to tree root growth. • Enhanced filter strip consideration. • Extended lagoon pumping and manure scraping options. • Enhanced burning operation. • Carbon pools and transformation equations similar to those in the Century model with the addition of the Phoenix C/N microbial biomass model. • Enhanced water table monitoring. • Enhanced denitrification methods. • Variable saturation hydraulic conductivity method. • Irrigation using reservoir and well reserves. • Paddy module for use with rice or wetland areas." | ABSTRACT: "Riparian areas support a set of river functions and of ecosystem services (ESs). Their role is essential in reducing negative human impacts on river functionality. These aspects could be contained in the River Basin Management Plan, which is the tool for managing and planning freshwater ecosystems in a river basin. In this paper, a new index was developed, namely the River Ecosystem Quality Index (REQI). It is composed of five ecological indices, which assess the quality of riparian areas, and it was first applied to the Marecchia river (central Italy). The REQI was also compared with the Italian River Functionality Index (IFF) and the ESs measured as the capacity of land cover in providing human benefits. Data have shown a decrease in the quality of riparian areas, from the upper to lower part of river, with 53% of all subareas showing medium-quality values…" AUTHOR'S DESCRIPTION: "The evaluation of the quality of the riparian areas is based on the analysis of two fundamental elements of riparian areas: vegetation (characteristics and distribution) and wild birds, measured with standardized methodology and used as indicators of environmental quality and changes...To represent the REQI, each of the five indicators was initially scored with its own range (Figure 3(a)—(e)). Then, all results were redistributed in ranges from 1 to 5, where 5 is the best condition of all indices. Redistributed results were finally summed." | AUTHORS DESCRIPTION: "The Nature Conservancy (TNC) and The Dow Chemical Company (Dow) initiated a collaborative effort to develop models that would help Dow and the wider business community identify and incorporate the value of nature into business decision making…the ESII Tool models and outputs were constructed and tested with an engineering and design perspective to facilitate actionable land use and management decisions. The ESII Tool helps non-ecologists make relative comparisons of the expected levels of ecosystem service performance across a given site, under a variety of conditions. As a planning-level tool, it can inform business decisions while enhancing the user’s relationship with nature. However, other uses that require ecological models of a higher degree of accuracy and/or precision, expert data collection, extensive sampling, and analysis of ecological relationships are beyond the intended scope of this tool." "The ESII App is your remote interface to the ESII Tool. It enables you to collect spatially-explicit ecological data, make maps, collect survey data, take photos, and record notes about your observations. With a Wi-Fi connection, the ESII App can upload and download information stored on the ESII Project Workspace, where final analyses and reports are generated. Because sites may be large and may include several different types of habitats, each Site to be assessed using the ESII Tool is divided into smaller areas called map units, and field data is collected on a map unit basis." "Once a map unit has been selected from the list of map units, the first survey question will always be “Map Unit Habitat Type” (Figure 12). The survey will progress through four categories of questions: habitat, vegetation, surface characteristics, and noise and visual screening. The questions are designed to enable you to select the most appropriate response easily and quickly." "Ecosystem Functions and Services scores are shown in units of percent performance, while each Units of Measure score will be shown in the engineering units appropriate to each attribute. At a map unit level, percent performance predicts how well a map unit would perform a given function or service as a proportion of the maximum potential you would expect from ideal attribute conditions. At a Site or Scenario level, percent performance is calculated as the area weighted average of the individual map unit’s percent performance values; it provides a normalized comparative metric between Sites or Scenarios. At both the map unit and the Site or Scenario levels, the units of measure represent absolute values (such as gallons of runoff or BTU reduction through shading) and can be either summed to show absolute performance of a Scenario, or normalized by area to show area-based rates of performance." | 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. | 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: "Plant–pollinator mutualisms are one of the several functional relationships that must be reinstated to ensure the long-term success of habitat restoration projects. These mutualisms are unlikely to reinstate themselves until all of the resource requirements of pollinators have been met. By meeting these requirements, projects can improve their long-term success. We hypothesized that pollinator assemblage and structure and stability of plant–pollinator networks depend both on aspects of the surrounding landscape and of the restoration effort itself. We predicted that pollinator species diversity and network stability would be negatively associated with distance from remnant habitat, but that local floral diversity might rescue pollinator diversity and network stability in locations distant from the remnant. We created plots of native prairie on a reclaimed strip mine in central Ohio, U.S.A. that ranged in floral diversity and isolation from the remnant habitat. We found that the pollinator diversity declined with distance from the remnant habitat. Furthermore, reduced pollinator diversity in low floral diversity plots far from the remnant habitat was associated with loss of network stability. High floral diversity, however, compensated for losses in pollinator diversity in plots far from the remnant habitat through the attraction of generalist pollinators. Generalist pollinators increased network connectance and plant-niche overlap. Asa result, network robustness of high floral diversity plots was independent of isolation. We conclude that the aspects of the restoration effort itself, such as floral community composition, can be successfully tailored to incorporate the restoration of pollinators and improve success given a particular landscape context." |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | Not applicable | None identified | None identified | None identified | None Identified | None identified | Transport of solids for characterizing rivers in the European Water Framework Directive (WFD) | None identified | Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominantely south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Agricultural landuse , 1st-10th order streams | No additional description provided | Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm. | Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak. | Coastal to montane, Pacific Northwest US (Oregon) forests. | No additional description provided | 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 | 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 | No additional description provided | No additional description provided | Not applicable | restored, enhanced and created wetlands | restored, enhanced and created wetlands | The site was surface mined for coal until the mid-1980s and soon after recontoured and seeded with a low diversity of non-native grasses and forbes. The property is grassland in a state of arrested succession, unable to support tree growth because of shallow, infertile soils. |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | Not applicable | Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use change (2000-2030) | Carbon prices at $10/t CO2^-e, $15/t CO2^-e, $20/t CO2^-e, $25/t CO2^-e, $30/t CO2^-e, and $40/t CO2^-e | No scenarios presented | Two scenarios modelled, forests with and without fire | Land Management (3); Climate Change (3) | Four future climate change scenarios combining two IPCC SRES scenarios and two GCMs | No scenarios presented | Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves. | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Sites, function or habitat focus | Sites, function or habitat focus | No scenarios presented |
EM ID
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EM-68 | EM-71 | EM-79 | EM-93 |
EM-125 ![]() |
EM-127 ![]() |
EM-133 |
EM-186 ![]() |
EM-208 ![]() |
EM-275 ![]() |
EM-326 |
EM-333 ![]() |
EM-424 | EM-464 |
EM-485 ![]() |
EM-549 | EM-592 | EM-657 | EM-712 |
EM-718 ![]() |
EM-743 ![]() |
EM-774 ![]() |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs |
Method + Application (multiple runs exist) View EM Runs ?Comment:Runs are differentiated based on the the expected annual profit from two types of carbon plantings: 1) Tree-based monocultures (i.e., monoculture carbon planting) and 2) Diverse plantings of native tree and shrub species (i.e., ecological carbon planting) |
Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
Method + Application (multiple runs exist) View EM Runs ?Comment:Runs differentiated by scenario combination. |
Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Model runs are for different sites (Beatenberg, Vordemwald, Bettlachstock, Schanis, and Novaggio) differentiated by climate and forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). |
Method Only | Method Only | Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs |
New or Pre-existing EM?
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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 | Application of existing model | Application of existing model | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
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EM-68 | EM-71 | EM-79 | EM-93 |
EM-125 ![]() |
EM-127 ![]() |
EM-133 |
EM-186 ![]() |
EM-208 ![]() |
EM-275 ![]() |
EM-326 |
EM-333 ![]() |
EM-424 | EM-464 |
EM-485 ![]() |
EM-549 | EM-592 | EM-657 | EM-712 |
EM-718 ![]() |
EM-743 ![]() |
EM-774 ![]() |
EM Temporal Extent
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2007-2009 | 2007-2008 | 2007-2008 | 2000-2008 | 1990-2030 | 2009-2050 | Not reported | >650 yrs | 1990-2050 | 1961-2100 | 1971-2006 | 1990-2050 |
1989 - 2011 ?Comment:6/21/16 BH - Rates were assigned from literature, ranging from 1989 - 2006, and the denitrification rate for urban lawns comes from 2011 literature. |
2006-2007, 2010 | 2000-2010 | 35 yr | Not applicable |
1996-2003 ?Comment:All the ecological analyses are based on the production of a 1:10,000 scale map of land cover with detailed classes for the vegetation obtained by overlapping the photogrammetric analysis (AIMA flight 1996) and the 2003 land-use map. |
Not applicable | 2010-2011 | 2010-2012 | 2009-2010 |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | past time | future time | future time | Not applicable | future time | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | past time | past time | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete | discrete | continuous | Not applicable | discrete | Not applicable | Not applicable | discrete | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | 1 | 1 | Not applicable | Not applicable | 2 | Not applicable | Not applicable | 1 | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Year | Year | Not applicable | Not applicable | Year | Not applicable | Not applicable | Year | Day | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
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EM-68 | EM-71 | EM-79 | EM-93 |
EM-125 ![]() |
EM-127 ![]() |
EM-133 |
EM-186 ![]() |
EM-208 ![]() |
EM-275 ![]() |
EM-326 |
EM-333 ![]() |
EM-424 | EM-464 |
EM-485 ![]() |
EM-549 | EM-592 | EM-657 | EM-712 |
EM-718 ![]() |
EM-743 ![]() |
EM-774 ![]() |
Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Watershed/Catchment/HUC | Geopolitical | Physiographic or Ecological | Geopolitical | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical | Not applicable | Not applicable | Watershed/Catchment/HUC | Not applicable | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological |
Spatial Extent Name
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Central French Alps | Central French Alps | Central French Alps | Upper Mississippi, Ohio and Missouri River sub-basins | The EU-25 plus Switzerland and Norway | Agricultural districts of the state of South Australia | Municipality of Etropole | Western Oregon, north of 43.00 N to Washington border | South Santiam watershed | Aixola watershed | Puget Sound Region | Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Guanica Bay watershed | Coastal zone surrounding St. Croix | Switzerland | Not applicable | Not applicable | Marecchia river catchment | Not applicable | Wetlands in idaho | Wetlands in idaho | The Wilds |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | 100-1000 km^2 | 1-10 km^2 | 10,000-100,000 km^2 | 10-100 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 10,000-100,000 km^2 | Not applicable | Not applicable | 100-1000 km^2 | Not applicable | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | 1-10 km^2 |
EM ID
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EM-68 | EM-71 | EM-79 | EM-93 |
EM-125 ![]() |
EM-127 ![]() |
EM-133 |
EM-186 ![]() |
EM-208 ![]() |
EM-275 ![]() |
EM-326 |
EM-333 ![]() |
EM-424 | EM-464 |
EM-485 ![]() |
EM-549 | EM-592 | EM-657 | EM-712 |
EM-718 ![]() |
EM-743 ![]() |
EM-774 ![]() |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Distributed by land cover and soil type polygons |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:map units delineated by user based on project. |
spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | area, for pixel or radial feature |
Spatial Grain Size
em.detail.spGrainSizeHelp
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20 m x 20 m | 20 m x 20 m | 20 m x 20 m | 1 km | 1 km x 1 km | 1 ha x 1 ha | Distributed by irregular land cover and soil type polygons | 0.08 ha | 0.08 ha | Average size 0.2 km^2 | 200m x 200m | varies | 30 m x 30 m | 10 m x 10 m | Not applicable | Not applicable | homogenous subareas | 500 m x 1000 m | map units | Not applicable | Not applicable | 10 m radius |
EM ID
em.detail.idHelp
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EM-68 | EM-71 | EM-79 | EM-93 |
EM-125 ![]() |
EM-127 ![]() |
EM-133 |
EM-186 ![]() |
EM-208 ![]() |
EM-275 ![]() |
EM-326 |
EM-333 ![]() |
EM-424 | EM-464 |
EM-485 ![]() |
EM-549 | EM-592 | EM-657 | EM-712 |
EM-718 ![]() |
EM-743 ![]() |
EM-774 ![]() |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Analytic | Analytic | Analytic | Logic- or rule-based | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Numeric | Analytic | Analytic | Numeric | Numeric | Numeric | Analytic | Analytic | Numeric | Numeric | Analytic |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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EM ID
em.detail.idHelp
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EM-68 | EM-71 | EM-79 | EM-93 |
EM-125 ![]() |
EM-127 ![]() |
EM-133 |
EM-186 ![]() |
EM-208 ![]() |
EM-275 ![]() |
EM-326 |
EM-333 ![]() |
EM-424 | EM-464 |
EM-485 ![]() |
EM-549 | EM-592 | EM-657 | EM-712 |
EM-718 ![]() |
EM-743 ![]() |
EM-774 ![]() |
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | No | No | No | No | No | No | No | Yes | No | Unclear | No | Yes | No | Not applicable | Not applicable | Not applicable | Not applicable | No | No | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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Yes | Yes | Yes | No | No | No | No | No | No | No | No | No | No | No | No | Not applicable | Not applicable | Not applicable | Not applicable | No | No | Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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Yes | No | No | No | No | No | No | Yes | No | Yes | No | No | No | Yes | Yes | Unclear | Not applicable |
Yes ?Comment:R2 values of the analysis between the REQI, the capacity of land cover to provide ESs, and the Italian River Functionality Quality Index ? IFF. |
Not applicable | No | No | Yes |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | Yes | No | No | No | No | No | No | No | No | No | No | Yes | Not applicable | Not applicable | Not applicable | Not applicable | No | No | Yes |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | Unclear | No | No | No | No | No | Yes | No | No | No | No | No | Not applicable | Not applicable | Not applicable | Not applicable | No | No | No |
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 | N/A | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-68 | EM-71 | EM-79 | EM-93 |
EM-125 ![]() |
EM-127 ![]() |
EM-133 |
EM-186 ![]() |
EM-208 ![]() |
EM-275 ![]() |
EM-326 |
EM-333 ![]() |
EM-424 | EM-464 |
EM-485 ![]() |
EM-549 | EM-592 | EM-657 | EM-712 |
EM-718 ![]() |
EM-743 ![]() |
EM-774 ![]() |
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None |
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None | None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-68 | EM-71 | EM-79 | EM-93 |
EM-125 ![]() |
EM-127 ![]() |
EM-133 |
EM-186 ![]() |
EM-208 ![]() |
EM-275 ![]() |
EM-326 |
EM-333 ![]() |
EM-424 | EM-464 |
EM-485 ![]() |
EM-549 | EM-592 | EM-657 | EM-712 |
EM-718 ![]() |
EM-743 ![]() |
EM-774 ![]() |
None | None | None | None | None | None | None | None | None | None | None | None |
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None | None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-68 | EM-71 | EM-79 | EM-93 |
EM-125 ![]() |
EM-127 ![]() |
EM-133 |
EM-186 ![]() |
EM-208 ![]() |
EM-275 ![]() |
EM-326 |
EM-333 ![]() |
EM-424 | EM-464 |
EM-485 ![]() |
EM-549 | EM-592 | EM-657 | EM-712 |
EM-718 ![]() |
EM-743 ![]() |
EM-774 ![]() |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | 45.05 | 45.05 | 36.98 | 50.53 | -34.9 | 42.8 | 44.66 | 44.24 | 43 | 48 | 44.11 | 17.96 | 17.73 | 46.82 | Not applicable | Not applicable | 43.89 | Not applicable | 44.06 | 44.06 | 39.82 |
Centroid Longitude
em.detail.ddLongHelp
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6.4 | 6.4 | 6.4 | -89.13 | 7.6 | 138.7 | 24 | -122.56 | -122.24 | -1 | -123 | -123.09 | -67.02 | -64.77 | 8.23 | Not applicable | Not applicable | 12.3 | Not applicable | -114.69 | -114.69 | -81.75 |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | Not applicable | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Provided | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Not applicable | Estimated | Not applicable | Estimated | Estimated | Provided |
EM ID
em.detail.idHelp
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EM-68 | EM-71 | EM-79 | EM-93 |
EM-125 ![]() |
EM-127 ![]() |
EM-133 |
EM-186 ![]() |
EM-208 ![]() |
EM-275 ![]() |
EM-326 |
EM-333 ![]() |
EM-424 | EM-464 |
EM-485 ![]() |
EM-549 | EM-592 | EM-657 | EM-712 |
EM-718 ![]() |
EM-743 ![]() |
EM-774 ![]() |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Rivers and Streams | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Rivers and Streams | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Forests | Forests | Rivers and Streams | Forests | Barren | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Inland Wetlands | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Agroecosystems | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Inland Wetlands | Grasslands |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Subalpine terraces, grasslands, and meadows | Not applicable | Not applicable | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Mountainous flood-prone region | Primarily conifer forest | primarily Conifer Forest | Forested watershed used for commercial forestry | Terrestrial environment surrounding a large estuary | Agricultural-urban interface at river junction | Thirteen land use land cover classes were used | Coral reefs and mangroves | forests | Agroecosystems | Terrestrial environment associated with agroecosystems | Riparian zone along major river | Not applicable | created, restored and enhanced wetlands | created, restored and enhanced wetlands | Grassland |
EM Ecological Scale
em.detail.ecoScaleHelp
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Not applicable | Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | 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 corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-68 | EM-71 | EM-79 | EM-93 |
EM-125 ![]() |
EM-127 ![]() |
EM-133 |
EM-186 ![]() |
EM-208 ![]() |
EM-275 ![]() |
EM-326 |
EM-333 ![]() |
EM-424 | EM-464 |
EM-485 ![]() |
EM-549 | EM-592 | EM-657 | EM-712 |
EM-718 ![]() |
EM-743 ![]() |
EM-774 ![]() |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Community | Community | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Species | Species | Not applicable | Not applicable | Not applicable | Not applicable | Community | Community | Not applicable | Not applicable |
Species ?Comment:Bird species for faunistic index of conservation. |
Not applicable | Not applicable | Not applicable | Species |
Taxonomic level and name of organisms or groups identified
EM-68 | EM-71 | EM-79 | EM-93 |
EM-125 ![]() |
EM-127 ![]() |
EM-133 |
EM-186 ![]() |
EM-208 ![]() |
EM-275 ![]() |
EM-326 |
EM-333 ![]() |
EM-424 | EM-464 |
EM-485 ![]() |
EM-549 | EM-592 | EM-657 | EM-712 |
EM-718 ![]() |
EM-743 ![]() |
EM-774 ![]() |
None Available | None Available | None Available | None Available | None Available |
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None Available |
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None Available | None Available |
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None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available |
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EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-68 | EM-71 | EM-79 | EM-93 |
EM-125 ![]() |
EM-127 ![]() |
EM-133 |
EM-186 ![]() |
EM-208 ![]() |
EM-275 ![]() |
EM-326 |
EM-333 ![]() |
EM-424 | EM-464 |
EM-485 ![]() |
EM-549 | EM-592 | EM-657 | EM-712 |
EM-718 ![]() |
EM-743 ![]() |
EM-774 ![]() |
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None | None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-68 | EM-71 | EM-79 | EM-93 |
EM-125 ![]() |
EM-127 ![]() |
EM-133 |
EM-186 ![]() |
EM-208 ![]() |
EM-275 ![]() |
EM-326 |
EM-333 ![]() |
EM-424 | EM-464 |
EM-485 ![]() |
EM-549 | EM-592 | EM-657 | EM-712 |
EM-718 ![]() |
EM-743 ![]() |
EM-774 ![]() |
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None | None |
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None |
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None | None |
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None | None | None |
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None |
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None | None | None |