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
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EM ID
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EM-12 |
EM-51 |
EM-63 | EM-82 | EM-84 | EM-91 | EM-103 | EM-137 | EM-154 | EM-317 |
EM-338 |
EM-344 |
EM-359 |
EM-397 |
EM-485 |
EM-493 | EM-630 |
EM-686 |
EM-697 |
EM-702 | EM-706 |
EM-735 |
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EM Short Name
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Evoland v3.5 (bounded growth), Eugene, OR, USA | EnviroAtlas-Nat. filtration-water | EnviroAtlas - Natural biological nitrogen fixation | Pollination ES, Central French Alps | ACRU, South Africa | RHyME2, Upper Mississippi River basin, USA | Birds in estuary habitats, Yaquina Estuary, WA, USA | i-Tree Hydro v4.0 | Mangrove development, Tampa Bay, FL, USA | ARIES carbon, Puget Sound Region, USA | InVEST crop pollination, California, USA | InVEST water yield, Xitiaoxi River basin, China | InVEST (v1.004) sediment retention, Indonesia | Wetland shellfish production, Gulf of Mexico, USA | Yasso07 v1.0.1, Switzerland, site level | EnviroAtlas-Carbon sequestered by trees | WaterWorld v2, Santa Basin, Peru | Estuary recreational use, Cape Cod, MA | Floral resources on landfill sites, United Kingdom | Northern Shoveler recruits, CREP wetlands, IA, USA | WESP Method | C sequestration in grassland restoration, England |
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EM Full Name
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Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA | US EPA EnviroAtlas - Natural filtration (of water by tree cover); Example is shown for Durham NC and vicinity, USA | US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Pollination ecosystem service estimated from plant functional traits, Central French Alps | ACRU (Agricultural Catchments Research Unit), South Africa | RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | Bird use of estuarine habitats, Yaquina Estuary, WA, USA | i-Tree Hydro v4.0 (default data option) | Mangrove wetland development, Tampa Bay, FL, USA | ARIES (Artificial Intelligence for Ecosystem Services) Carbon Storage and Sequestration, Puget Sound Region, Washington, USA | InVEST crop pollination, California, USA | InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) water yield, Xitiaoxi River basin, China | InVEST (Integrated Valuation of Environmental Services and Tradeoffs v1.004) sediment retention, Sumatra, Indonesia | Wetland shellfish production, Gulf of Mexico, USA | Yasso07 v1.0.1 forest litter decomposition, Switzerland, site level | US EPA EnviroAtlas - Total carbon sequestered by tree cover; Example is shown for Durham NC and vicinity, USA | WaterWorld v2, Santa Basin, Peru | Estuary recreational use, Cape Cod, MA | Floral resources on landfill sites, East Midlands, United Kingdom | Northern Shoveler duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Method for the Wetland Ecosystem Services Protocol (WESP) | Carbon sequestration in grassland diversity restoration, England |
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EM Source or Collection
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Envision |
US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Hydro model. |
US EPA | EnviroAtlas | EU Biodiversity Action 5 | None | US EPA | US EPA | i-Tree | USDA Forest Service | US EPA | ARIES | InVEST | InVEST | InVEST |
US EPA ?Comment:Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science |
None | US EPA | EnviroAtlas | i-Tree | None | US EPA | None | None | None | None |
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EM Source Document ID
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47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
223 |
262 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
260 | 271 | 123 | 275 | 198 | 97 | 302 | 279 | 307 | 309 | 324 | 343 |
223 ?Comment:Additional source: I-tree Eco (doc# 345). |
368 | 387 | 389 |
372 ?Comment:Document 373 is a secondary source for this EM. |
390 | 396 |
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Document Author
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Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | US EPA Office of Research and Development - National Exposure Research Laboratory | US EPA Office of Research and Development - National Exposure Research Laboratory | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C. | Frazier, M. R., Lamberson, J. O. and Nelson, W. G. | USDA Forest Service | Osland, M. J., Spivak, A. C., Nestlerode, J. A., Lessmann, J. M., Almario, A. E., Heitmuller, P. T., Russell, M. J., Krauss, K. W., Alvarez, F., Dantin, D. D., Harvey, J. E., From, A. S., Cormier, N. and Stagg, C.L. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | Zhang C., Li, W., Zhang, B., and Liu, M. | Bhagabati, N. K., Ricketts, T., Sulistyawan, T. B. S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A., Tallis, H., and Wolney, S. | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Didion, M., B. Frey, N. Rogiers, and E. Thurig | US EPA Office of Research and Development - National Exposure Research Laboratory | Van Soesbergen, A. and M. Mulligan | Mulvaney, K K., Atkinson, S.F., Merrill, N.H., Twichell, J.H., and M.J. Mazzotta | Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin | 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 | Adamus, P. R. | 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 |
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Document Year
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2008 | 2013 | 2013 | 2011 | 2008 | 2013 | 2014 | Not Reported | 2012 | 2014 | 2009 | 2012 | 2014 | 2012 | 2014 | 2013 | 2018 | 2019 | 2013 | 2010 | 2016 | 2011 |
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Document Title
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Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | EnviroAtlas - Featured Community | EnviroAtlas - National | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Mapping ecosystem services for planning and management | Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads | Intertidal habitat utilization patterns of birds in a Northeast Pacific estuary | i-Tree Hydro User's Manual v. 4.0 | Ecosystem development after mangrove wetland creation: plant–soil change across a 20-year chronosequence | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Modelling pollination services across agricultural landscapes | Water yield of Xitiaoxi River basin based on InVEST modeling | Ecosystem services reinforce Sumatran tiger conservation in land use plans | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Validating tree litter decomposition in the Yasso07 carbon model | EnviroAtlas - Featured Community | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | Quantifying Recreational Use of an Estuary: A case study of three bays, Cape Cod, USA | Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Manual for the Wetland Ecosystem Services Protocol (WESP) v. 1.3. | Additional carbon sequestration benefits of grassland diversity restoration |
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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 but unpublished (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published on US EPA EnviroAtlas website | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Webpage | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Draft manuscript-work progressing | Published journal manuscript | Published report | Published report | Published journal manuscript |
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EM ID
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EM-12 |
EM-51 |
EM-63 | EM-82 | EM-84 | EM-91 | EM-103 | EM-137 | EM-154 | EM-317 |
EM-338 |
EM-344 |
EM-359 |
EM-397 |
EM-485 |
EM-493 | EM-630 |
EM-686 |
EM-697 |
EM-702 | EM-706 |
EM-735 |
http://evoland.bioe.orst.edu/ ?Comment:Software is likely available. |
https://www.epa.gov/enviroatlas | https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | http://www.itreetools.org | Not applicable | http://aries.integratedmodelling.org/ | http://www.naturalcapitalproject.org/models/crop_pollination.html | https://www.naturalcapitalproject.org/invest/ | https://www.naturalcapitalproject.org/invest/ | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | https://www.epa.gov/enviroatlas | www.policysupport.org/waterworld | Not applicable | Not applicable | Not applicable |
http://people.oregonstate.edu/~adamusp/WESP/ ?Comment:This is an Excel spreadsheet calculator |
Not applicable | |
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Contact Name
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Michael R. Guzy | EnviroAtlas Team |
EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Sandra Lavorel | Roland E Schulze | Liem Tran |
M. R. Frazier ?Comment:Present address: M. R. Frazier National Center for Ecological Analysis and Synthesis, 735 State St. Suite 300, Santa Barbara, CA 93101, USA |
Not applicable | Michael Osland | Ken Bagstad | Eric Lonsdorf | Li Wenhua | Nirmal K. Bhagabati | Stephen J. Jordan | Markus Didion | EnviroAtlas Team | Arnout van Soesbergen | Mulvaney, Kate | Sam Tarrant | David Otis | Paul R. Adamus | Gerlinde B. De Deyn |
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Contact Address
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Oregon State University, Dept. of Biological and Ecological Engineering | Not reported | Not reported | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | School of Bioresources Engineering and Environmental Hydrology, University of Natal, South Africa | Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA | Western Ecology Division, Office of Research and Development, U.S. Environmental Protection Agency, Pacific coastal Ecology Branch, 2111 SE marine Science Drive, Newport, OR 97365 | Not applicable | U.S. Environmental Protection Agency, Gulf Ecology Division, gulf Breeze, FL 32561 | Geosciences and Environmental Change Science Center, US Geological Survey | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China | The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | U.S. Environmental Protection Agency, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Not reported | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | US EPA, ORD, NHEERL, Atlantic Ecology Division, Narragansett, RI | RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K. | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | 6028 NW Burgundy Dr. Corvallis, OR 97330 | Dept. of Terrestrial Ecology, Netherlands Institute of Ecology, P O Box 40, 6666 ZG Heteren, The Netherlands |
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Contact Email
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Not reported | enviroatlas@epa.gov | enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | schulzeR@nu.ac.za | ltran1@utk.edu | frazier@nceas.ucsb.edu | Not applicable | mosland@usgs.gov | kjbagstad@usgs.gov | ericlonsdorf@lpzoo.org | liwh@igsnrr.ac.cn | nirmal.bhagabati@wwfus.org | jordan.steve@epa.gov | markus.didion@wsl.ch | enviroatlas@epa.gov | arnout.van_soesbergen@kcl.ac.uk | Mulvaney.Kate@epa.gov | sam.tarrant@rspb.org.uk | dotis@iastate.edu | adamus7@comcast.net | g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com |
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EM ID
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EM-12 |
EM-51 |
EM-63 | EM-82 | EM-84 | EM-91 | EM-103 | EM-137 | EM-154 | EM-317 |
EM-338 |
EM-344 |
EM-359 |
EM-397 |
EM-485 |
EM-493 | EM-630 |
EM-686 |
EM-697 |
EM-702 | EM-706 |
EM-735 |
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Summary Description
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**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." | The Natural Filtration model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. METADATA ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina... runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA DESCRIPTION: "The i-Tree Hydro model estimates the effects of tree and impervious cover on hourly stream flow values for a watershed (Wang et al 2008). i-Tree Hydro also estimates changes in water quality using hourly runoff estimates and mean and median national event mean concentration (EMC) values. The model was calibrated using hourly stream flow data to yield the best fit between model and measured stream flow results… After calibration, the model was run a number of times under various conditions to see how the stream flow would respond given varying tree and impervious cover in the watershed… The term event mean concentration (EMC) is a statistical parameter used to represent the flow-proportional average concentration of a given parameter during a storm event. EMC data is used for estimating pollutant loading into watersheds. The response outputs were calculated as kg of pollutant per square meter of land area for pollutants. These per square meter values were multiplied by the square meters of land area in the block group to estimate the effects at the block group level." METADATA DESCRIPTION PARAPHRASED: Changes in water quality were estimated for the following pollutants (entered as separate runs); total suspended solids (TSS), total phosphorus, soluble phosphorus, nitrites and nitrates, total Kjeldahl nitrogen (TKN), biochemical oxygen demand (BOD5), chemical oxygen demand (COD5), and copper. "Reduction in annual runoff (census block group)" variable data was derived from the EnviroAtlas water recharge coverage which used the i-Tree Hydro model. | DATA FACT SHEET: "This EnviroAtlas national map displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The pollination ecosystem service map was a simple sums of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to pollination ecosystem services are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions." | AUTHOR'S DESCRIPTION (Doc ID 272): "ACRU is a daily timestep, physical conceptual and multipurpose model structured to simulate impacts of land cover/ use change. The model can output, inter alia, components of runoff, irrigation supply and demand, reservoir water budgets as well as sediment and crop yields." AUTHOR'S DESCRIPTION (Doc ID 271): "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…The total benefit to people of water supply is a function of both the quantity and quality with the ecosystem playing a key role in the latter. However, due to the lack of suitable national scale data on water quality for quantifying the service, runoff was used as an estimate of the benefit where runoff is the total water yield from a watershed including surface and subsurface flow. This assumes that runoff is positively correlated with quality, which is the case in South Africa (Allanson et al., 1990)…In South Africa, water resources are mapped in water management areas called catchments (vs. watersheds) where a catchment is defined as the area of land that is drained by a single river system, including its tributaries (DWAF, 2004). There are 1946 quaternary (4th order) catchments in South Africa, the smallest is 4800 ha and the average size is 65,000 ha. Schulze (1997) modelled annual runoff for each quaternary catchment. During modelling of runoff, he used rainfall data collected over a period of more than 30 years, as well as data on other climatic factors, soil characteristics and grassland as the land cover. In this study, median annual simulated runoff was used as a measure of surface water supply. The volume of runoff per quaternary catchment was calculated for surface water supply. The range (areas with runoff of 30 million m^3 or more) and hotspots (areas with runoff of 70 million m^3 or more) were defined using a combination of statistics and expert inputs due to a lack of published thresholds in the literature." | ABSTRACT: "We describe a framework called Regional Hydrologic Modeling for Environmental Evaluation (RHyME2) for hydrologic modeling across scales. Rooted from hierarchy theory, RHyME2 acknowledges the rate-based hierarchical structure of hydrological systems. Operationally, hierarchical constraints are accounted for and explicitly described in models put together into RHyME2. We illustrate RHyME2with a two-module model to quantify annual nutrient loads in stream networks and watersheds at regional and subregional levels. High values of R2 (>0.95) and the Nash–Sutcliffe model efficiency coefficient (>0.85) and a systematic connection between the two modules show that the hierarchy theory-based RHyME2 framework can be used effectively for developing and connecting hydrologic models to analyze the dynamics of hydrologic systems." Two EMs will be entered in EPF-Library: 1. Regional scale module (Upper Mississippi River Basin) - this entry 2. Subregional scale module (St. Croix River Basin) | AUTHOR'S DESCRIPTION: "To describe bird utilization patterns of intertidal habitats within Yaquina estuary, Oregon, we conducted censuses to obtain bird species and abundance data for the five dominant estuarine intertidal habitats: Zostera marina (eelgrass), Upogebia (mud shrimp)/ mudflat, Neotrypaea (ghost shrimp)/sandflat, Zostera japonica (Japanese eelgrass), and low marsh. EPFs were developed for the following metrics of bird use: standardized species richness; Shannon diversity; and density for the following four groups: all birds, all birds excluding gulls, waterfowl (ducks and geese), and shorebirds." | ABSTRACT: "i-Tree Hydro is the first urban hydrology model that is specifically designed to model vegetation effects and to be calibrated against measured stream flow data. It is designed to model the effects of changes in urban tree cover and impervious surfaces on hourly stream flows and water quality at the watershed level." AUTHOR'S DESCRIPTION: "The purpose of i-Tree Hydro is to simulate hourly changes in stream flow (and water quality) given changes in tree and impervious cover in the watershed. The following is an overview of the process: 1) Determine your watershed of analysis and stream gauge station. i-Tree Hydro works on a watershed basis with the watershed determined as the total drainage area upstream from a measured stream gauge. Stream gauge availability varies. 2) Download national digital elevation data. Once the area and location of the watershed are known, digital elevation data are downloaded from the USGS for an area that encompasses the entire watershed. ArcGIS software is then used to create a digital elevation map and to determine the exact boundary for the watershed upstream from the gauge station location. 3) Determine cover attributes of the watershed and gather other required data. i-Tree Canopy and other sources can be used to determine the tree cover, shrub cover, impervious surface and other cover types. Information about other aspects of the watershed such as proportion of evergreen trees and shrubs, leaf area index, and a variety of hydrologic parameters must be collected. 4) Get started with Hydro. Once these input data are ready, they are loaded into Hydro to begin analysis. 5) Calibrate the model. The Hydro model contains an auto-calibration routine that tries to find the best fit between the stream flow predicted by the model and the stream flow measured at the stream gauge station given the various inputs. The model can also be manually calibrated to improve the fit by changing the parameters as needed. 6) Model new scenarios: Once the model is properly calibrated, tree and impervious cover parameters can be changed to illustrate the impact on stream flow and water quality." | ABSTRACT: "Mangrove wetland restoration and creation effortsare increasingly proposed as mechanisms to compensate for mangrove wetland losses. However, ecosystem development and functional equivalence in restored and created mangrove wetlands are poorly understood. We compared a 20-year chronosequence of created tidal wetland sites in Tampa Bay, Florida (USA) to natural reference mangrove wetlands. Across the chronosequence, our sites represent the succession from salt marsh to mangrove forest communities. Our results identify important soil and plant structural differences between the created and natural reference wetland sites; however, they also depict a positive developmental trajectory for the created wetland sites that reflects tightly coupled plant-soil development. Because upland soils and/or dredge spoils were used to create the new mangrove habitats, the soils at younger created sites and at lower depths (10–30 cm) had higher bulk densities, higher sand content, lower soil organic matter (SOM), lower total carbon (TC), and lower total nitrogen (TN) than did natural reference wetland soils. However, in the upper soil layer (0–10 cm), SOM, TC, and TN increased with created wetland site age simultaneously with mangrove forest growth. The rate of created wetland soil C accumulation was comparable to literature values for natural mangrove wetlands. Notably, the time to equivalence for the upper soil layer of created mangrove wetlands appears to be faster than for many other wetland ecosystem types. Collectively, our findings characterize the rate and trajectory of above- and below-ground changes associated with ecosystem development in created mangrove wetlands; this is valuable information for environmental managers planning to sustain existing mangrove wetlands or mitigate for mangrove wetland losses." | 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 quantified carbon sequestration and storage in vegetation and soils using Bayesian models (Bagstad et al. 2011) calibrated with Moderate-resolution Imaging Spectroradiometer Net Primary Productivity (MODIS GPP/NPP Project, http://secure.ntsg.umt. edu/projects/index.php/ID/ca2901a0/fuseaction/prohttp://www.whrc.org/ational Bwww.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_053627)vey Geographic Dahttp://www.geomac.gov/index.shtml)wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_053627) soils data, respectively. By overlaying fire boundary polygons from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov/index.shtml) we estimated carbon storage losses caused by wildfire, using fuel consumption coefficients from Spracklen et al. (2009) and carbon pool data from Smith et al. (2006). By incorporating the impacts of land-cover change from urbanization (Bolte and Vache 2010) within carbon models, we quantified resultant changes in carbon storage." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "Background and Aims: Crop pollination by bees and other animals is an essential ecosystem service. Ensuring the maintenance of the service requires a full understanding of the contributions of landscape elements to pollinator populations and crop pollination. Here, the first quantitative model that predicts pollinator abundance on a landscape is described and tested. Methods: Using information on pollinator nesting resources, floral resources and foraging distances, the model predicts the relative abundance of pollinators within nesting habitats. From these nesting areas, it then predicts relative abundances of pollinators on the farms requiring pollination services. Model outputs are compared with data from coffee in Costa Rica, watermelon and sunflower in California and watermelon in New Jersey–Pennsylvania (NJPA). Key Results: Results from Costa Rica and California, comparing field estimates of pollinator abundance, richness or services with model estimates, are encouraging, explaining up to 80 % of variance among farms. However, the model did not predict observed pollinator abundances on NJPA, so continued model improvement and testing are necessary. The inability of the model to predict pollinator abundances in the NJPA landscape may be due to not accounting for fine-scale floral and nesting resources within the landscapes surrounding farms, rather than the logic of our model. Conclusions: The importance of fine-scale resources for pollinator service delivery was supported by sensitivity analyses indicating that the model's predictions depend largely on estimates of nesting and floral resources within crops. Despite the need for more research at the finer-scale, the approach fills an important gap by providing quantitative and mechanistic model from which to evaluate policy decisions and develop land-use plans that promote pollination conservation and service delivery. " | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "A water yield model based on InVEST was employed to estimate water runoff in the Xitiaoxi River basin…In order to test model accuracy the natural runoff of Xitiaoxi River was estimated based on linear regression relation of rainfall-runoff in a 'reference period'." AUTHOR'S DESCRIPTION: "The water yield model is based on the Budyko curve (1974) and annual precipitation…Water yield models require land use and land cover, precipitation, average annual potential evapotranspiration, soil depth, plant available water content, watersheds and sub-watersheds as well as a biophysical table reflecting the attributes of each land use and land cover." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "...Here we use simple spatial analyses on readily available datasets to compare the distribution of five ecosystem services with tiger habitat in central Sumatra. We assessed services and habitat in 2008 and the changes in these variables under two future scenarios: a conservation-friendly Green Vision, and a Spatial Plan developed by the Indonesian government..." AUTHOR'S DESCRIPTION: "We used a modeling tool, InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004; Tallis et al., 2010), to map and quantify tiger habitat quality and five ecosystem services. InVEST maps ecosystem services and the quality of species habitat as production functions of LULC using simple biophysical models. Models were parameterized using data from regional agencies, literature surveys, global databases, site visits and prior field experience (Table 1)... The sediment retention model is based on the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978). It estimates erosion as ton y^-1 of sediment load, based on the energetic ability of rainfall to move soil, the erodibility of a given soil type, slope, erosion protection provided by vegetated LULC, and land management practices. The model routes sediment originating on each land parcel along its flow path, with vegetated parcels retaining a fraction of sediment with varying efficiencies, and exporting the remainder downstream. ...Although InVEST reports ecosystem services in biophysical units, its simple models are best suited to understanding broad patterns of spatial variation (Tallis and Polasky, 2011), rather than for precise quantification. Additionally, we lacked field measurements against which to calibrate our outputs. Therefore, we focused on relative spatial distribution across the landscape, and relative change to scenarios." | ABSTRACT: "We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for … commercial blue crab Callinectes sapidus and penaeid shrimp fisheries in the Gulf of Mexico." | 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 | The Total carbon sequestered by tree cover model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. DATA FACT SHEET: "This EnviroAtlas community map estimates the total metric tons (mt) of carbon that are removed annually from the atmosphere and sequestered in the above-ground biomass of trees in each census block group. The data for this map were derived from a high-resolution tree cover map developed by EPA. Within each census block group derived from U.S. Census data, the total amount of tree cover (m2) was determined using this remotely-sensed land cover data. The USDA Forest Service i-Tree model was used to estimate the annual carbon sequestration rate from state-based rates of kgC/m2 of tree cover/year. The state rates vary based on length of growing season and range from 0.168 kgC/m2 of tree cover/year (Alaska) to 0.581 kgC/m2 of tree cover/year (Hawaii). The national average rate is 0.306 kgC/m2 of tree cover/year. These national and state values are based on field data collected and analyzed in several cities by the U.S. Forest Service. These values were converted to metric tons of carbon removed and sequestered per year by census block group." | ABSTRACT: "Water resources in the Santa basin in the Peruvian Andes are increasingly under pressure from climate change and population increases. Impacts of temperature-driven glacier retreat on stream flow are better studied than those from precipitation changes, yet present and future water resources are mostly dependent on precipitation which is more difficult to predict with climate models. This study combines a broad range of projections from climate models with a hydrological model (WaterWorld), showing a general trend towards an increase in water availability due to precipitation increases over the basin. However, high uncertainties in these projections necessitate the need for basin-wide policies aimed at increased adaptability." AUTHOR'S DESCRIPTION: "WaterWorld is a fully distributed, process-based hydrological model that utilises remotely sensed and globally available datasets to support hydrological analysis and decision-making at national and local scales globally, with a particular focus on un-gauged and/or data-poor environments, which makes it highly suited to this study. The model (version 2) currently runs on either 10 degree tiles, large river basins or countries at 1-km2 resolution or 1 degree tiles at 1-ha resolution utilising different datasets. It simulates a hydrological baseline as a mean for the period 1950-2000 and can be used to calculate the hydrological impact of scenarios of climate change, land use change, land management options, impacts of extractives (oil & gas and mining) and impacts of changes in population and demography as well as combinations of these. The model is ‘self parameterising’ (Mulligan, 2013a) in the sense that all data required for model application anywhere in the world is provided with the model, removing a key barrier to model application. However, if users have better data than those provided, it is possible to upload these to WaterWorld as GIS files and use them instead. Results can be viewed visually within the web browser or downloaded as GIS maps. The model’s equations and processes are described in more detail in Mulligan and Burke (2005) and Mulligan (2013b). The model parameters are not routinely calibrated to observed flows as it is designed for hydrological scenario analysis in which the physical basis of its parameters must be retained and the model is also often used in un-gauged basins. Calibration is inappropriate under these circumstances (Sivapalan et al., 2003). The freely available nature of the model means that anyone can apply it and replicate the results shown here. WaterWorld’s (V2) snow and ice module is capable of simulating the processes of melt water production, snow fall and snow pack, making this version highly suited to the current application. The model component is based on a full energy-balance for snow accumulation and melting based on Walter et al., (2005) with input data provided globally by the SimTerra database (Mulligan, 2011) upon which the model r | [ABSTRACT: "Estimates of the types and number of recreational users visiting an estuary are critical data for quantifying the value of recreation and how that value might change with variations in water quality or other management decisions. However, estimates of recreational use are minimal and conventional intercept surveys methods are often infeasible for widespread application to estuaries. Therefore, a practical observational sampling approach was developed to quantify the recreational use of an estuary without the use of surveys. Designed to be simple and fast to allow for replication, the methods involved the use of periodic instantaneous car counts multiplied by extrapolation factors derived from all-day counts. This simple sampling approach can be used to estimate visitation to diverse types of access points on an estuary in a single day as well as across multiple days. Evaluation of this method showed that when periodic counts were taken within a preferred time window (from 11am-4:30pm), the estimates were within 44 percent of actual daily visitation. These methods were applied to the Three Bays estuary system on Cape Cod, USA. The estimated combined use across all its public access sites is similar to the use at a mid-sized coastal beach, demonstrating the value of estuarine systems. Further, this study is the first to quantify the variety and magnitude of recreational uses at several different types of access points throughout the estuary using observational methods. This model focused on the various use by access point type (beaches, landings and way to water, boat use). This work can be transferred to the many small coastal access points used for recreation across New England and beyond." ] | ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level… A “floral cover” method to represent available floral resources was used which combines floral abundance with inflorescence size. Mean area of the floral unit from above was measured for each flowering plant species and then multiplied by their frequencies." "Insect pollinated flowering plant species composition and floral abundance between sites by type were represented by non-metric multidimensional scaling (NMDS)...This method is sensitive to showing outliers and the distance between points shows the relative similarity (McCune & Grace 2002; Ollerton et al. 2009)." (This data is not entered into ESML) | ABSTRACT: "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…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2*R*n where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = H*Z*B/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632). | Author Description: " The Wetland Ecosystem Services Protocol (WESP) is a standardized template for creating regionalized methods which then can be used to rapid assess ecosystem services (functions and values) of all wetland types throughout a focal region. To date, regionalized versions of WESP have been developed (or are ongoing) for government agencies or NGOs in Oregon, Alaska, Alberta, New Brunswick, and Nova Scotia. WESP also may be used directly in its current condition to assess these services at the scale of an individual wetland, but without providing a regional context for interpreting that information. Nonetheless, WESP takes into account many landscape factors, especially as they relate to the potential or actual benefits of a wetland’s functions. A WESP assessment requires completing a single three-part data form, taking about 1-3 hours. Responses to questions on that form are based on review of aerial imagery and observations during a single site visit; GIS is not required. After data are entered in an Excel spreadsheet, the spreadsheet uses science-based logic models to automatically generate scores intended to reflect a wetland’s ability to support the following functions: Water Storage and Delay, Stream Flow Support, Water Cooling, Sediment Retention and Stabilization, Phosphorus Retention, Nitrate Removal and Retention, Carbon Sequestration, Organic Nutrient Export, Aquatic Invertebrate Habitat, Anadromous Fish Habitat, Non-anadromous Fish Habitat, Amphibian & Reptile Habitat, Waterbird Feeding Habitat, Waterbird Nesting Habitat, Songbird, Raptor and Mammal Habitat, Pollinator Habitat, and Native Plant Habitat. For all but two of these functions, scores are given for both components of an ecosystem service: function and benefit. In addition, wetland Ecological Condition (Integrity), Public Use and Recognition, Wetland Sensitivity, and Stressors are scored. Scores generated by WESP may be used to (a) estimate a wetland’s relative ecological condition, stress, and sensitivity, (b) compare relative levels of ecosystem services among different wetland types, or (c) compare those in a single wetland before and after restoration, enhancement, or loss."] | 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…" |
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Specific Policy or Decision Context Cited
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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 | Not reported | None identified | None identified | Not applicable | None identified | None identified | None identified | This analysis provided input to government-led spatial planning and strategic environmental assessments in the study area. This region contains some of the last remaining forest habitat of the critically endangered Sumatran tiger, Panthera tigris sumatrae. | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified |
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Biophysical Context
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No additional description provided | No additional description provided | No additional description provided | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | No additional description provided | Estuarine intertidal, eelgrass, mudflat, sandflat and low marsh | No additional description provided | mangrove forest,Salt marsh, estuary, sea level, | No additional description provided | No additional description provided | Mean elevation of 266 m, with southwestern mountainous area. Subtropical monsoon climate. Annual average temperature of 12.2-15.6 °C. Annual mean precipitation is 1500 mm, and over 70% of rainfall occurs in the flood season (Apr-Oct). | Six watersheds in central Sumatra covering portions of Riau, Jambi and West Sumatra provinces. The Barisan mountain range comprises the western edge of the watersheds, while peat swamps predominate in the east. | Estuarine environments and marsh-land interfaces | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | No additional description provided | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | None identified | No additional description provided | Prairie Pothole Region of Iowa | None | Lolium perenne-Cynosorus cristatus grassland; The soil is a shallow brown-earth (average depth 28 cm) over limestone of moderate-high residual fertility. |
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EM Scenario Drivers
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Five scenarios that include urban growth boundaries and various combinations of unconstrainted development, fish conservation, agriculture and forest reserves. ?Comment:Additional alternatives included adding agricultural and forest reserves, and adding or removing urban growth boundaries to the three main scenarios. |
No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Not applicable | No scenarios presented | No scenarios presented | No scenarios presented | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | Shellfish type; Changes to submerged aquatic vegetation (SAV) | No scenarios presented | No scenarios presented | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | N/A | No scenarios presented | No scenarios presented | N/A | Additional benefits due to biodiversity restoration practices |
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EM ID
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EM-12 |
EM-51 |
EM-63 | EM-82 | EM-84 | EM-91 | EM-103 | EM-137 | EM-154 | EM-317 |
EM-338 |
EM-344 |
EM-359 |
EM-397 |
EM-485 |
EM-493 | EM-630 |
EM-686 |
EM-697 |
EM-702 | EM-706 |
EM-735 |
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Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method Only | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs |
Method + Application (multiple runs exist) View EM Runs ?Comment:Ten runs; blue crab and penaeid shrimp, each combined with five different submerged aquatic vegetation habitat areas. |
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 + Application | Method + Application (multiple runs exist) | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs |
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New or Pre-existing EM?
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New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | 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 | 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 | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-12 |
EM-51 |
EM-63 | EM-82 | EM-84 | EM-91 | EM-103 | EM-137 | EM-154 | EM-317 |
EM-338 |
EM-344 |
EM-359 |
EM-397 |
EM-485 |
EM-493 | EM-630 |
EM-686 |
EM-697 |
EM-702 | EM-706 |
EM-735 |
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Document ID for related EM
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Doc-47 | Doc-313 | Doc-314 ?Comment:Doc 183 is a secondary source for the Evoland model. |
Doc-198 |
Doc-346 | Doc-347 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
Doc-260 |
Doc-272 ?Comment:Doc ID 272 was also used as a source document for this EM |
Doc-123 | None | Doc-190 | Doc-223 | None | Doc-303 | Doc-305 | Doc-279 | Doc-280 | Doc-311 | Doc-338 | Doc-205 | Doc-338 | None | Doc-342 | Doc-343 | Doc-345 | None | None | None | Doc-372 | Doc-373 | None | None |
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EM ID for related EM
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EM-333 | EM-369 | EM-137 | EM-142 | None | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-83 | None | None | None | EM-109 | EM-142 | EM-51 | None | None | EM-340 | EM-339 | EM-148 | EM-368 | EM-437 | EM-111 | EM-435 | EM-604 | EM-603 | EM-466 | EM-467 | EM-469 | EM-480 | None | None | EM-682 | EM-684 | EM-685 | EM-709 | EM-705 | EM-704 | EM-703 | EM-701 | EM-700 | EM-632 | EM-718 | None |
EM Modeling Approach
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EM ID
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EM-12 |
EM-51 |
EM-63 | EM-82 | EM-84 | EM-91 | EM-103 | EM-137 | EM-154 | EM-317 |
EM-338 |
EM-344 |
EM-359 |
EM-397 |
EM-485 |
EM-493 | EM-630 |
EM-686 |
EM-697 |
EM-702 | EM-706 |
EM-735 |
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EM Temporal Extent
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1990-2050 | 1999-2010 | 2006-2010 | Not reported | 1950-1993 | 1987-1997 | December 2007 - November 2008 | Not applicable | 1990-2010 | 1950-2007 | 2001-2002 | 2003-2007 | 2008-2020 | 1950 - 2050 | 2000-2010 | 2010-2013 | 1950-2071 | Summer 2017 | 2007-2008 | 1987-2007 | Not applicable | 1990-2007 |
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EM Time Dependence
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time-dependent |
time-stationary ?Comment:The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, operated on an hourly timestep. The final annual flow parameter however is time stationary. |
time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | future time | future time | Not applicable | both | past time | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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discrete | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | continuous | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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2 | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Varies by Run | 1 | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Hour | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Year | Not applicable | Month | Day | Not applicable | Not applicable | Not applicable | Not applicable |
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EM ID
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EM-12 |
EM-51 |
EM-63 | EM-82 | EM-84 | EM-91 | EM-103 | EM-137 | EM-154 | EM-317 |
EM-338 |
EM-344 |
EM-359 |
EM-397 |
EM-485 |
EM-493 | EM-630 |
EM-686 |
EM-697 |
EM-702 | EM-706 |
EM-735 |
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Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Geopolitical | Geopolitical | Physiographic or Ecological | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Not applicable | Physiographic or Ecological | Physiographic or ecological | Other | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Not applicable | Other |
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Spatial Extent Name
em.detail.extentNameHelp
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Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Durham, NC and vicinity | counterminous United States | Central French Alps | South Africa | Upper Mississippi River basin; St. Croix River Watershed | Yaquina Estuary (intertidal), Oregon, USA | Not applicable | Tampa Bay | Puget Sound Region | Agricultural landscape, Yolo County, Central Valley | Xitiaoxi River basin | central Sumatra | Gulf of Mexico (estuarine and coastal) | Switzerland | Durham NC and vicinity | Santa Basin | Three Bays, Cape Cod | East Midlands | CREP (Conservation Reserve Enhancement Program | Not applicable | Colt Park meadows, Ingleborough National Nature Reserve, northern England |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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10-100 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 10-100 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 1-10 km^2 | Not applicable | 100-1000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | 10,000-100,000 km^2 | 10,000-100,000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | 10,000-100,000 km^2 | Not applicable | <1 ha |
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EM ID
em.detail.idHelp
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EM-12 |
EM-51 |
EM-63 | EM-82 | EM-84 | EM-91 | EM-103 | EM-137 | EM-154 | EM-317 |
EM-338 |
EM-344 |
EM-359 |
EM-397 |
EM-485 |
EM-493 | EM-630 |
EM-686 |
EM-697 |
EM-702 | EM-706 |
EM-735 |
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EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) ?Comment:Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. |
spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Watersheds (12-digit HUCs). |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Computations at this pixel scale pertain to certain variables specific to Mobile Bay. |
spatially lumped (in all cases) |
spatially distributed (in at least some cases) ?Comment:Census block groups |
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) |
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Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | NHDplus v1 | other (habitat type) | 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 | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | 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 |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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varies | irregular | irregular | 20 m x 20 m | Distributed by catchments with average size of 65,000 ha | NHDplus v1 | 0.87-104.29 ha | 30 x 30 m | m^2 | 200m x 200m | 30 m x 30 m | Not reported | 30 m x 30 m | 55.2 km^2 | Not applicable | irregular | 1 km2 | beach length | multiple unrelated locations | multiple, individual, irregular sites | not reported | 3 m x 3 m |
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EM ID
em.detail.idHelp
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EM-12 |
EM-51 |
EM-63 | EM-82 | EM-84 | EM-91 | EM-103 | EM-137 | EM-154 | EM-317 |
EM-338 |
EM-344 |
EM-359 |
EM-397 |
EM-485 |
EM-493 | EM-630 |
EM-686 |
EM-697 |
EM-702 | EM-706 |
EM-735 |
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EM Computational Approach
em.detail.emComputationalApproachHelp
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Numeric |
Analytic ?Comment:The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, was numeric. The final parameter however did not require iteration. |
Analytic | Analytic | Numeric | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Numeric | * | Numeric | Analytic | Analytic | Analytic | Analytic |
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EM Determinism
em.detail.deterStochHelp
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stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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Comment:Agent based modeling results in response indices. |
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None |
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EM ID
em.detail.idHelp
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EM-12 |
EM-51 |
EM-63 | EM-82 | EM-84 | EM-91 | EM-103 | EM-137 | EM-154 | EM-317 |
EM-338 |
EM-344 |
EM-359 |
EM-397 |
EM-485 |
EM-493 | EM-630 |
EM-686 |
EM-697 |
EM-702 | EM-706 |
EM-735 |
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Model Calibration Reported?
em.detail.calibrationHelp
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Unclear | Unclear | No | No | No | Yes | Unclear | Not applicable | No | Yes | Unclear | Yes | No | Yes | No | No | No | Yes | Not applicable | Unclear | Not applicable | Not applicable |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | No | No | No | Yes | No | Not applicable | No | No | No | No | No | No | No | No | No | No | Not applicable | No | Not applicable | Not applicable |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None |
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None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | Unclear | No | No | No | No | No | Not applicable | No | No |
Yes ?Comment:Performed just for "Total pollinator abundance service score". |
No | No | No | Yes | No | Yes | No | Not applicable | No | No | No |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | Unclear | No | No | No | No | No | Not applicable | Yes | No | No | No | No | No | Yes | No | No | No | Not applicable | No | Not applicable | No |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No ?Comment:Sensitivity analysis performed for agent values only. |
Unclear | No | No | No |
No ?Comment:Some model coefficients serve, by their magnitude, to indicate the proportional impact on the final result of variation in the parameters they modify. |
No | Not applicable | Yes | No | No | Yes | No | No | No | No | No | No | Not applicable | No | Not applicable | No |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | No | 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])
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EM-12 |
EM-51 |
EM-63 | EM-82 | EM-84 | EM-91 | EM-103 | EM-137 | EM-154 | EM-317 |
EM-338 |
EM-344 |
EM-359 |
EM-397 |
EM-485 |
EM-493 | EM-630 |
EM-686 |
EM-697 |
EM-702 | EM-706 |
EM-735 |
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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)
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EM-12 |
EM-51 |
EM-63 | EM-82 | EM-84 | EM-91 | EM-103 | EM-137 | EM-154 | EM-317 |
EM-338 |
EM-344 |
EM-359 |
EM-397 |
EM-485 |
EM-493 | EM-630 |
EM-686 |
EM-697 |
EM-702 | EM-706 |
EM-735 |
| None | None | None | None | None | None |
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None |
Comment:Realm: Tropical Atlantic Region: West Tropical Atlantic Province: Tropical Northwestern Atlantic Ecoregion: Floridian |
None | None | None | None |
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None | None | None |
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None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-12 |
EM-51 |
EM-63 | EM-82 | EM-84 | EM-91 | EM-103 | EM-137 | EM-154 | EM-317 |
EM-338 |
EM-344 |
EM-359 |
EM-397 |
EM-485 |
EM-493 | EM-630 |
EM-686 |
EM-697 |
EM-702 | EM-706 |
EM-735 |
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Centroid Latitude
em.detail.ddLatHelp
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44.11 | 35.99 | 39.5 | 45.05 | -30 | 42.5 | 44.62 | -9999 | 27.8 | 48 | 38.7 | 30.55 | 0 | 30.44 | 46.82 | 35.99 | -9.05 | 41.62 | 52.22 | 42.62 | Not applicable | 54.2 |
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Centroid Longitude
em.detail.ddLongHelp
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-123.09 | -78.96 | -98.35 | 6.4 | 25 | -90.63 | -124.06 | -9999 | -82.4 | -123 | -121.8 | 119.5 | 102 | -87.99 | 8.23 | -78.96 | -77.81 | -70.42 | -0.91 | -93.84 | Not applicable | -2.35 |
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Centroid Datum
em.detail.datumHelp
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WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | None provided | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Provided | Not applicable | Estimated | Estimated | Estimated | Provided | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Provided |
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EM ID
em.detail.idHelp
?
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EM-12 |
EM-51 |
EM-63 | EM-82 | EM-84 | EM-91 | EM-103 | EM-137 | EM-154 | EM-317 |
EM-338 |
EM-344 |
EM-359 |
EM-397 |
EM-485 |
EM-493 | EM-630 |
EM-686 |
EM-697 |
EM-702 | EM-706 |
EM-735 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Rivers and Streams | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands | Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Near Coastal Marine and Estuarine | Rivers and Streams | Ground Water | Created Greenspace | Near Coastal Marine and Estuarine | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Forests | Atmosphere | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Forests | Created Greenspace | Atmosphere | None | Near Coastal Marine and Estuarine | Created Greenspace | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Agroecosystems | Grasslands |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Agricultural-urban interface at river junction | Urban areas including streams | Terrestrial | Subalpine terraces, grasslands, and meadows. | Not reported | None | Estuarine intertidal | Urban watersheds | Created Mangrove wetlands | Terrestrial environment surrounding a large estuary | Cropland and surrounding landscape | Watershed | 104 land use land cover classes | Submerged aquatic vegetation in estuaries and coastal lagoons | forests | Urban and vicinity | tropical, coastal to montane | Beaches | restored landfills and grasslands | Wetlands buffered by grassland within agroecosystems | Wetlands | fertilized grassland (historically hayed) |
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EM Ecological Scale
em.detail.ecoScaleHelp
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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 coarser than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecosystem | 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 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 | Other or unclear (comment) | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
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EM ID
em.detail.idHelp
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EM-12 |
EM-51 |
EM-63 | EM-82 | EM-84 | EM-91 | EM-103 | EM-137 | EM-154 | EM-317 |
EM-338 |
EM-344 |
EM-359 |
EM-397 |
EM-485 |
EM-493 | EM-630 |
EM-686 |
EM-697 |
EM-702 | EM-706 |
EM-735 |
|
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Not applicable | Community | Not applicable | Not applicable | Guild or Assemblage | Community | Not applicable | Not applicable | Species | Not applicable | Community | Species | Community | Not applicable | Not applicable | Not applicable | Individual or population, within a species | Individual or population, within a species | Not applicable | Community |
Taxonomic level and name of organisms or groups identified
|
EM-12 |
EM-51 |
EM-63 | EM-82 | EM-84 | EM-91 | EM-103 | EM-137 | EM-154 | EM-317 |
EM-338 |
EM-344 |
EM-359 |
EM-397 |
EM-485 |
EM-493 | EM-630 |
EM-686 |
EM-697 |
EM-702 | EM-706 |
EM-735 |
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None Available | None Available | None Available | None Available | None Available |
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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 |
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None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
|
EM-12 |
EM-51 |
EM-63 | EM-82 | EM-84 | EM-91 | EM-103 | EM-137 | EM-154 | EM-317 |
EM-338 |
EM-344 |
EM-359 |
EM-397 |
EM-485 |
EM-493 | EM-630 |
EM-686 |
EM-697 |
EM-702 | EM-706 |
EM-735 |
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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)
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EM-12 |
EM-51 |
EM-63 | EM-82 | EM-84 | EM-91 | EM-103 | EM-137 | EM-154 | EM-317 |
EM-338 |
EM-344 |
EM-359 |
EM-397 |
EM-485 |
EM-493 | EM-630 |
EM-686 |
EM-697 |
EM-702 | EM-706 |
EM-735 |
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None |
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None |
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None |
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None |
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None | None | None |
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None |
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None |
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