EcoService Models Library (ESML)
loading
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
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-68 | EM-82 | EM-84 | EM-91 | EM-93 | EM-103 | EM-105 | EM-137 |
EM-208 ![]() |
EM-320 |
EM-338 ![]() |
EM-397 ![]() |
EM-469 |
EM-485 ![]() |
EM-593 ![]() |
EM-628 | EM-630 |
EM-686 ![]() |
EM-697 ![]() |
EM-698 | EM-702 | EM-706 |
EM-709 ![]() |
EM Short Name
em.detail.shortNameHelp
?
|
Evoland v3.5 (bounded growth), Eugene, OR, USA | EnviroAtlas - Natural biological nitrogen fixation | Fodder crude protein content, Central French Alps | Pollination ES, Central French Alps | ACRU, South Africa | RHyME2, Upper Mississippi River basin, USA | Stream nitrogen removal, Mississippi R. basin, USA | Birds in estuary habitats, Yaquina Estuary, WA, USA | Benthic habitat associations, Willapa Bay, OR, USA | i-Tree Hydro v4.0 | FORCLIM v2.9, Santiam watershed, OR, USA | Coastal protection, Europe | InVEST crop pollination, California, USA | Wetland shellfish production, Gulf of Mexico, USA | Yasso07 - SOC, Loess Plateau, China | Yasso07 v1.0.1, Switzerland, site level | DayCent N2O flux simulation, Ireland | SolVES, Bridger-Teton NF, WY | WaterWorld v2, Santa Basin, Peru | Estuary recreational use, Cape Cod, MA | Floral resources on landfill sites, United Kingdom | Fish species richness, St. Croix, USVI | Northern Shoveler recruits, CREP wetlands, IA, USA | WESP Method | Pollinators on landfill sites, United Kingdom |
EM Full Name
em.detail.fullNameHelp
?
|
Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA | US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Fodder crude protein content, Central French Alps | 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 | Stream nitrogen removal, Upper Mississippi, Ohio and Missouri River sub-basins, USA | Bird use of estuarine habitats, Yaquina Estuary, WA, USA | Benthic macrofaunal habitat associations, Willapa Bay, OR, USA | i-Tree Hydro v4.0 (default data option) | FORCLIM (FORests in a changing CLIMate) v2.9, Santiam watershed, OR, USA | Coastal protection, Europe | InVEST crop pollination, California, USA | Wetland shellfish production, Gulf of Mexico, USA | Yasso07 - Land Use Effects on Soil Organic Carbon Stocks in the Loess Plateau, China | Yasso07 v1.0.1 forest litter decomposition, Switzerland, site level | DayCent simulation N2O flux and climate change, Ireland | SolVES, Social Values for Ecosystem Services, Bridger-Teton National Forest, WY | WaterWorld v2, Santa Basin, Peru | Estuary recreational use, Cape Cod, MA | Floral resources on landfill sites, East Midlands, United Kingdom | Fish Species Richness, Buck Island, St. Croix , USVI | Northern Shoveler duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Method for the Wetland Ecosystem Services Protocol (WESP) | Pollinating insects on landfill sites, East Midlands, United Kingdon |
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
Envision | US EPA | EnviroAtlas | EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | US EPA | US EPA | US EPA | US EPA | i-Tree | USDA Forest Service | US EPA | EU Biodiversity Action 5 | InVEST |
US EPA ?Comment:Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science |
None | None | None | None | None | US EPA | None | None | None | None | None |
EM Source Document ID
|
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
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 | 260 | 271 | 123 | 52 | 275 | 39 | 198 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
296 | 279 | 324 | 344 | 343 | 358 | 369 | 368 | 387 | 389 | 355 |
372 ?Comment:Document 373 is a secondary source for this EM. |
390 | 389 |
Document Author
em.detail.documentAuthorHelp
?
|
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 | 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. | 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. | Hill, B. and Bolgrien, D. | Frazier, M. R., Lamberson, J. O. and Nelson, W. G. | Ferraro, S. P. and Cole, F. A. | USDA Forest Service | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Liquete, C., Zulian, G., Delgado, I., Stips, A., and Maes, J. | Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu | Didion, M., B. Frey, N. Rogiers, and E. Thurig | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | 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 | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | 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. | Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin |
Document Year
em.detail.documentYearHelp
?
|
2008 | 2013 | 2011 | 2011 | 2008 | 2013 | 2011 | 2014 | 2007 | Not Reported | 2007 | 2013 | 2009 | 2012 | 2015 | 2014 | 2010 | 2014 | 2018 | 2019 | 2013 | 2007 | 2010 | 2016 | 2013 |
Document Title
em.detail.sourceIdHelp
?
|
Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | EnviroAtlas - National | 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 | Mapping ecosystem services for planning and management | Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads | Nitrogen removal by streams and rivers of the Upper Mississippi River basin | Intertidal habitat utilization patterns of birds in a Northeast Pacific estuary | Benthic macrofauna–habitat associations in Willapa Bay, Washington, USA | i-Tree Hydro User's Manual v. 4.0 | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Assessment of coastal protection as an ecosystem service in Europe | Modelling pollination services across agricultural landscapes | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement | Validating tree litter decomposition in the Yasso07 carbon model | Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | 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 | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | 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. | Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects |
Document Status
em.detail.statusCategoryHelp
?
|
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 | 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 | Peer reviewed and published |
Comments on Status
em.detail.commentsOnStatusHelp
?
|
Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | 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 journal manuscript | Published journal manuscript | Draft manuscript-work progressing | Published journal manuscript | Published journal manuscript | Published report | Published report | Published journal manuscript |
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-68 | EM-82 | EM-84 | EM-91 | EM-93 | EM-103 | EM-105 | EM-137 |
EM-208 ![]() |
EM-320 |
EM-338 ![]() |
EM-397 ![]() |
EM-469 |
EM-485 ![]() |
EM-593 ![]() |
EM-628 | EM-630 |
EM-686 ![]() |
EM-697 ![]() |
EM-698 | EM-702 | EM-706 |
EM-709 ![]() |
http://evoland.bioe.orst.edu/ ?Comment:Software is likely available. |
https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://www.itreetools.org | Not applicable | Not applicable | http://www.naturalcapitalproject.org/models/crop_pollination.html | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | Not applicable | Not applicable | www.policysupport.org/waterworld | Not applicable | Not applicable | Not applicable | Not applicable |
http://people.oregonstate.edu/~adamusp/WESP/ ?Comment:This is an Excel spreadsheet calculator |
Not applicable | |
Contact Name
em.detail.contactNameHelp
?
|
Michael R. Guzy |
EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Sandra Lavorel | Sandra Lavorel | Roland E Schulze | Liem Tran | Brian Hill |
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 |
Steve Ferraro | Not applicable | Richard T. Busing | Camino Liquete | Eric Lonsdorf | Stephen J. Jordan | Xing Wu | Markus Didion | M. Abdalla | Benson Sherrouse | Arnout van Soesbergen | Mulvaney, Kate | Sam Tarrant | Simon Pittman | David Otis | Paul R. Adamus | Sam Tarrant |
Contact Address
|
Oregon State University, Dept. of Biological and Ecological Engineering | Not reported | 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 | 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 | Mid-Continent Ecology Division NHEERL, ORD. USEPA 6201 Congdon Blvd. Duluth, MN 55804, 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 | U.S. EPA 2111 SE Marine Science Drive Newport, OR 97365 | Not applicable | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, I-21027 Ispra, VA, Italy | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA | Chinese Academy of Sciences, Beijing 100085, China | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | 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. | 1305 East-West Highway, Silver Spring, MD 20910, USA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | 6028 NW Burgundy Dr. Corvallis, OR 97330 | RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K. |
Contact Email
|
Not reported | enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | schulzeR@nu.ac.za | ltran1@utk.edu | hill.brian@epa.gov | frazier@nceas.ucsb.edu | ferraro.steven@epa.gov | Not applicable | rtbusing@aol.com | camino.liquete@gmail.com | ericlonsdorf@lpzoo.org | jordan.steve@epa.gov | xingwu@rceesac.cn | markus.didion@wsl.ch | abdallm@tcd.ie | bcsherrouse@usgs.gov | arnout.van_soesbergen@kcl.ac.uk | Mulvaney.Kate@epa.gov | sam.tarrant@rspb.org.uk | simon.pittman@noaa.gov | dotis@iastate.edu | adamus7@comcast.net | sam.tarrant@rspb.org.uk |
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-68 | EM-82 | EM-84 | EM-91 | EM-93 | EM-103 | EM-105 | EM-137 |
EM-208 ![]() |
EM-320 |
EM-338 ![]() |
EM-397 ![]() |
EM-469 |
EM-485 ![]() |
EM-593 ![]() |
EM-628 | EM-630 |
EM-686 ![]() |
EM-697 ![]() |
EM-698 | EM-702 | EM-706 |
EM-709 ![]() |
Summary Description
em.detail.summaryDescriptionHelp
?
|
**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." | 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. 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: "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) | 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)." | 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." | AUTHOR'S DESCRIPTION: "In this paper we report the results of 2 estuary-wide studies of benthic macrofaunal habitat associations in Willapa Bay, Washington, USA. This research is part of an effort to develop empirical models of biota-habitat associations that can be used to help identify critical habitats, prioritize habitats for environmental protection, index habitat suitability (U.S. Fish and Wildlife Service, 1980; Kapustka, 2003), perform habitat equivalency and compensatory restoration analyses (Fonseca et al., 2002; Kirsch et al., 2005), and as habitat value criteria in ecological risk assessments (Obery and Landis, 2002; Ferraro and Cole, 2004; Landis et al., 2004)." (491) | 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: "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: "Mapping and assessment of ecosystem services is essential to provide scientific support to global and EU biodiversity policy. Coastal protection has been mostly analysed in the frame of coastal vulnerability studies or in local, habitat-specific assessments. This paper provides a conceptual and methodological approach to assess coastal protection as an ecosystem service at different spatial–temporal scales, and applies it to the entire EU coastal zone. The assessment of coastal protection incorporates 14 biophysical and socio-economic variables from both terrestrial and marine datasets. Those variables define three indicators: coastal protection capacity, coastal exposure and human demand for protection. A questionnaire filled by coastal researchers helped assign ranks to categorical parameters and weights to the individual variables. The three indicators are then framed into the ecosystem services cascade model to estimate how coastal ecosystems provide protection, in particular describing the service function, flow and benefit. The results are comparative and aim to support integrated land and marine spatial planning. The main drivers of change for the provision of coastal protection come from the widespread anthropogenic pressures in the European coastal zone, for which a short quantitative analysis is provided." | Please note: This ESML entry describes 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. " | 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: "Land use changes are known to significantly affect the soil C balance by altering both C inputs and losses. Since the late 1990s, a large area of the Loess Plateau has undergone intensive land use changes during several ecological restoration projects to control soil erosion and combat land degradation, especially in the Grain for Green project. By using remote sensing techniques and the Yasso07 model, we simulated the dynamics of soil organic carbon (SOC) stocks in the Yangjuangou catchment of the Loess Plateau. The performance of the model was evaluated by comparing the simulated results with the intensive field measurements in 2006 and 2011 throughout the catchment. SOC stocks and NPP values of all land use types had generally increased during our study period. The average SOC sequestration rate in the upper 30 cm soil from 2006 to 2011 in the Yangjuangou catchment was approximately 44 g C m-2 yr-1, which was comparable to other studies in the Loess Plateau. Forest and grassland showed a more effective accumulation of SOC than the other land use types in our study area. The Yasso07 model performed reasonably well in predicting the overall dynamics of SOC stock for different land use change types at both the site and catchment scales. The assessment of the model performance indicated that the combination of Yasso07 model and remote sensing data could be used for simulating the effect of land use changes on SOC stock at catchment scale in the Loess Plateau." | 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 | Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios. | [ABSTRACT: " "Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders.With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems. | 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: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems." | 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: "...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…In the first year of study, plants in flower and flower visitors were surveyed using the same transects as for the floral resources surveys. The transect was left undisturbed for 20 minutes following the initial plant survey to allow the flower visitors to return. Each transect was surveyed at a rate of approximately 3m/minute for 30 minutes. All insects observed to touch the sexual parts of flowers were either captured using a butterfly net and transferred into individually labeled specimen jars, or directly captured into the jars. After the survey was completed, those insects that could be identified in the field were recorded and released. The flower-visitor surveys were conducted in the morning, within 1 hour of midday, and in the afternoon to sample those insects active at different times. Insects that could not be identified in the field were collected as voucher specimens for later identification. Identifications were verified using reference collections and by taxon specialists. Relatively low capture rates in the first year led to methods being altered in the second year when surveying followed a spiral pattern from a randomly determined point on the sites, at a standard pace of 10 m/minute for 30 minutes, following Nielsen and Bascompte (2007) and Kalikhman (2007). Given a 2-m wide transect, an area of approximately 600m2 was sampled in each |
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
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 | Not applicable | None identified | None identified | None identified | None identified | Supports global and EU biodiversity policy | None identified | None identified | None | None identified | climate change | None | None identified | None identified | None identified | None provided | None identified | None identified | None identified |
Biophysical Context
|
No additional description provided | No additional description provided | Elevation ranges from 1552 to 2442 m, on predominantely south-facing slopes | 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 | Agricultural landuse , 1st-10th order streams | Estuarine intertidal, eelgrass, mudflat, sandflat and low marsh | benthic estuarine | No additional description provided | No additional description provided | No additional description provided | No additional description provided | Estuarine environments and marsh-land interfaces | Agricultural plain, hills, gulleys, forest, grassland, Central China | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Rocky mountain conifer forests | 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 | Hard and soft benthic habitat types approximately to the 33m isobath | Prairie Pothole Region of Iowa | None | No additional description provided |
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
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 | Not applicable | No scenarios presented | No scenarios presented | No scenarios presented | Land Management (3); Climate Change (3) | No scenarios presented | No scenarios presented | Shellfish type; Changes to submerged aquatic vegetation (SAV) | Land use change | No scenarios presented | air temperature, precipitation, Atmospheric CO2 concentrations | N/A | 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 | No scenarios presented | N/A | No scenarios presented |
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-68 | EM-82 | EM-84 | EM-91 | EM-93 | EM-103 | EM-105 | EM-137 |
EM-208 ![]() |
EM-320 |
EM-338 ![]() |
EM-397 ![]() |
EM-469 |
EM-485 ![]() |
EM-593 ![]() |
EM-628 | EM-630 |
EM-686 ![]() |
EM-697 ![]() |
EM-698 | EM-702 | EM-706 |
EM-709 ![]() |
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method Only |
Method + Application (multiple runs exist) View EM Runs ?Comment:Runs differentiated by scenario combination. |
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 |
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 (multiple runs exist) View EM Runs | 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 + Application | Method Only | Method + Application (multiple runs exist) View EM Runs |
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
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 | New or revised 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 | Application of existing model | Application of existing model | Application of existing model | New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing 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
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-68 | EM-82 | EM-84 | EM-91 | EM-93 | EM-103 | EM-105 | EM-137 |
EM-208 ![]() |
EM-320 |
EM-338 ![]() |
EM-397 ![]() |
EM-469 |
EM-485 ![]() |
EM-593 ![]() |
EM-628 | EM-630 |
EM-686 ![]() |
EM-697 ![]() |
EM-698 | EM-702 | EM-706 |
EM-709 ![]() |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
Doc-47 | Doc-313 | Doc-314 ?Comment:Doc 183 is a secondary source for the Evoland model. |
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-269 | Doc-260 |
Doc-272 ?Comment:Doc ID 272 was also used as a source document for this EM |
Doc-123 | Doc-154 | Doc-155 | None | None | Doc-190 | Doc-223 |
Doc-22 | Doc-23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
None | Doc-279 | None | Doc-343 | Doc-342 | Doc-342 | Doc-343 | None | None | None | None | None | Doc-355 | Doc-372 | Doc-373 | None | Doc-389 |
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
EM-333 | EM-369 | None | EM-65 | EM-66 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-83 | None | None | None | None | None | EM-109 | EM-142 | EM-51 | EM-146 | EM-186 | EM-224 | None | EM-340 | EM-339 | EM-604 | EM-603 | EM-466 | EM-467 | EM-480 | EM-485 | EM-466 | EM-467 | EM-469 | EM-480 | EM-598 | EM-629 | EM-626 | None | EM-682 | EM-684 | EM-685 | EM-709 | EM-590 | EM-699 | EM-705 | EM-704 | EM-703 | EM-701 | EM-700 | EM-632 | EM-718 | EM-697 |
EM Modeling Approach
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-68 | EM-82 | EM-84 | EM-91 | EM-93 | EM-103 | EM-105 | EM-137 |
EM-208 ![]() |
EM-320 |
EM-338 ![]() |
EM-397 ![]() |
EM-469 |
EM-485 ![]() |
EM-593 ![]() |
EM-628 | EM-630 |
EM-686 ![]() |
EM-697 ![]() |
EM-698 | EM-702 | EM-706 |
EM-709 ![]() |
EM Temporal Extent
em.detail.tempExtentHelp
?
|
1990-2050 | 2006-2010 | 2007-2009 | Not reported | 1950-1993 | 1987-1997 | 2000-2008 | December 2007 - November 2008 | 1996,1998 | Not applicable | 1990-2050 | 1992-2010 | 2001-2002 | 1950 - 2050 | 1969-2011 | 2000-2010 | 1961-1990 | 2004-2008 | 1950-2071 | Summer 2017 | 2007-2008 | 2000-2005 | 1987-2007 | Not applicable | 2007-2008 |
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-dependent | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
future time | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | future time | past time | future time | both | Not applicable | both | past time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
discrete | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | discrete | discrete | discrete | discrete | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
2 | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Varies by Run | 1 | 1 | 1 | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Year | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Hour | Year | Not applicable | Not applicable | Year | Year | Year | Day | Not applicable | Month | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-68 | EM-82 | EM-84 | EM-91 | EM-93 | EM-103 | EM-105 | EM-137 |
EM-208 ![]() |
EM-320 |
EM-338 ![]() |
EM-397 ![]() |
EM-469 |
EM-485 ![]() |
EM-593 ![]() |
EM-628 | EM-630 |
EM-686 ![]() |
EM-697 ![]() |
EM-698 | EM-702 | EM-706 |
EM-709 ![]() |
Bounding Type
em.detail.boundingTypeHelp
?
|
Geopolitical | Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or Ecological | Not applicable | Watershed/Catchment/HUC | Geopolitical | Other | Physiographic or ecological | Watershed/Catchment/HUC | Geopolitical | Point or points | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Not applicable | Multiple unrelated locations (e.g., meta-analysis) |
Spatial Extent Name
em.detail.extentNameHelp
?
|
Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | counterminous United States | Central French Alps | Central French Alps | South Africa | Upper Mississippi River basin; St. Croix River Watershed | Upper Mississippi, Ohio and Missouri River sub-basins | Yaquina Estuary (intertidal), Oregon, USA | Willapa Bay | Not applicable | South Santiam watershed | Shoreline of the European Union-27 | Agricultural landscape, Yolo County, Central Valley | Gulf of Mexico (estuarine and coastal) | Yangjuangou catchment | Switzerland | Oak Park Research centre | National Park | Santa Basin | Three Bays, Cape Cod | East Midlands | SW Puerto Rico, | CREP (Conservation Reserve Enhancement Program | Not applicable | East Midlands |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
10-100 km^2 | >1,000,000 km^2 | 10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | >1,000,000 km^2 | 1-10 km^2 | 100-1000 km^2 | Not applicable | 100-1000 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. | 10,000-100,000 km^2 | 1-10 km^2 | 10,000-100,000 km^2 | 1-10 ha | 1000-10,000 km^2. | 10,000-100,000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | 100-1000 km^2 | 10,000-100,000 km^2 | Not applicable | 1000-10,000 km^2. |
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-68 | EM-82 | EM-84 | EM-91 | EM-93 | EM-103 | EM-105 | EM-137 |
EM-208 ![]() |
EM-320 |
EM-338 ![]() |
EM-397 ![]() |
EM-469 |
EM-485 ![]() |
EM-593 ![]() |
EM-628 | EM-630 |
EM-686 ![]() |
EM-697 ![]() |
EM-698 | EM-702 | EM-706 |
EM-709 ![]() |
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
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) ?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 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) | 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 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) | 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) |
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
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 | other (specify), for irregular (e.g., stream reach, lake basin) | NHDplus v1 | length, for linear feature (e.g., stream mile) | other (habitat type) | Not applicable | 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 | area, for pixel or radial feature | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | 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) | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
varies | irregular | 20 m x 20 m | 20 m x 20 m | Distributed by catchments with average size of 65,000 ha | NHDplus v1 | 1 km | 0.87-104.29 ha | Not applicable | 30 x 30 m | 0.08 ha | Irregular | 30 m x 30 m | 55.2 km^2 | 30m x 30m | Not applicable | Not applicable | 30m2 | 1 km2 | beach length | multiple unrelated locations | not reported | multiple, individual, irregular sites | not reported | multiple unrelated locations |
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-68 | EM-82 | EM-84 | EM-91 | EM-93 | EM-103 | EM-105 | EM-137 |
EM-208 ![]() |
EM-320 |
EM-338 ![]() |
EM-397 ![]() |
EM-469 |
EM-485 ![]() |
EM-593 ![]() |
EM-628 | EM-630 |
EM-686 ![]() |
EM-697 ![]() |
EM-698 | EM-702 | EM-706 |
EM-709 ![]() |
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Numeric | Numeric | Numeric | Numeric | Numeric | * | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic |
EM Determinism
em.detail.deterStochHelp
?
|
stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
Comment:Agent based modeling results in response indices. |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
None |
|
|
|
|
|
|
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-68 | EM-82 | EM-84 | EM-91 | EM-93 | EM-103 | EM-105 | EM-137 |
EM-208 ![]() |
EM-320 |
EM-338 ![]() |
EM-397 ![]() |
EM-469 |
EM-485 ![]() |
EM-593 ![]() |
EM-628 | EM-630 |
EM-686 ![]() |
EM-697 ![]() |
EM-698 | EM-702 | EM-706 |
EM-709 ![]() |
Model Calibration Reported?
em.detail.calibrationHelp
?
|
Unclear | No | No | No | No | Yes | No | Unclear | Yes | Not applicable | No | No | Unclear | Yes | Yes | No | No | No | No | Yes | Not applicable | No | Unclear | Not applicable | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
No | No | Yes | No | No | Yes | No | No | Yes | Not applicable | No | No | No | No |
Yes ?Comment:For the year 2006 and 2011 |
No |
Yes ?Comment:for N2O fluxes |
Yes | No | No | Not applicable | Yes | No | Not applicable | Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
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
?
|
No | No | Yes | No | No | No | No | No | No | Not applicable | No | No |
Yes ?Comment:Performed just for "Total pollinator abundance service score". |
No | No | Yes | Yes | No | Yes | No | Not applicable | Yes | No | No | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
No | No | No | No | No | No | Yes | No | Yes | Not applicable | No | No | No | No | No | Yes | No | No | No | No | Not applicable | No | No | Not applicable | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
No ?Comment:Sensitivity analysis performed for agent values only. |
No | 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. |
Unclear | No | No | Not applicable | No | No | No | No | No | No | No | No | No | No | Not applicable | Yes | No | Not applicable | Not applicable |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | N/A | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-12 ![]() |
EM-63 | EM-68 | EM-82 | EM-84 | EM-91 | EM-93 | EM-103 | EM-105 | EM-137 |
EM-208 ![]() |
EM-320 |
EM-338 ![]() |
EM-397 ![]() |
EM-469 |
EM-485 ![]() |
EM-593 ![]() |
EM-628 | EM-630 |
EM-686 ![]() |
EM-697 ![]() |
EM-698 | EM-702 | EM-706 |
EM-709 ![]() |
|
|
|
|
|
|
|
|
None | None |
|
|
|
|
|
|
|
|
None | None |
|
None |
|
None |
|
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-12 ![]() |
EM-63 | EM-68 | EM-82 | EM-84 | EM-91 | EM-93 | EM-103 | EM-105 | EM-137 |
EM-208 ![]() |
EM-320 |
EM-338 ![]() |
EM-397 ![]() |
EM-469 |
EM-485 ![]() |
EM-593 ![]() |
EM-628 | EM-630 |
EM-686 ![]() |
EM-697 ![]() |
EM-698 | EM-702 | EM-706 |
EM-709 ![]() |
None | None | None | None | None | None | None |
|
|
None | None |
|
None |
|
None | None | None | None | None |
|
None |
|
None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-68 | EM-82 | EM-84 | EM-91 | EM-93 | EM-103 | EM-105 | EM-137 |
EM-208 ![]() |
EM-320 |
EM-338 ![]() |
EM-397 ![]() |
EM-469 |
EM-485 ![]() |
EM-593 ![]() |
EM-628 | EM-630 |
EM-686 ![]() |
EM-697 ![]() |
EM-698 | EM-702 | EM-706 |
EM-709 ![]() |
Centroid Latitude
em.detail.ddLatHelp
?
|
44.11 | 39.5 | 45.05 | 45.05 | -30 | 42.5 | 36.98 | 44.62 | 46.24 | -9999 | 44.24 | 48.2 | 38.7 | 30.44 | 36.7 | 46.82 | 52.86 | 43.93 | -9.05 | 41.62 | 52.22 | 17.79 | 42.62 | Not applicable | 52.22 |
Centroid Longitude
em.detail.ddLongHelp
?
|
-123.09 | -98.35 | 6.4 | 6.4 | 25 | -90.63 | -89.13 | -124.06 | -124.06 | -9999 | -122.24 | 16.35 | -121.8 | -87.99 | 109.52 | 8.23 | 6.54 | 110.24 | -77.81 | -70.42 | -0.91 | -64.62 | -93.84 | Not applicable | -0.91 |
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 | Not applicable | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Estimated | Estimated | Provided | Provided | Estimated | Estimated | Estimated | Provided | Provided | Not applicable | Provided | Estimated | Estimated | Estimated | Provided | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated |
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-68 | EM-82 | EM-84 | EM-91 | EM-93 | EM-103 | EM-105 | EM-137 |
EM-208 ![]() |
EM-320 |
EM-338 ![]() |
EM-397 ![]() |
EM-469 |
EM-485 ![]() |
EM-593 ![]() |
EM-628 | EM-630 |
EM-686 ![]() |
EM-697 ![]() |
EM-698 | EM-702 | EM-706 |
EM-709 ![]() |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands | 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 | Rivers and Streams | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Rivers and Streams | Ground Water | Created Greenspace | Forests | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Forests | Agroecosystems | Forests | None | Near Coastal Marine and Estuarine | Created Greenspace | Grasslands | Near Coastal Marine and Estuarine | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Created Greenspace | Grasslands |
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Agricultural-urban interface at river junction | Terrestrial | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Not reported | None | Not applicable | Estuarine intertidal | Drowned river valley estuary | Urban watersheds | primarily Conifer Forest | Coastal zones | Cropland and surrounding landscape | Submerged aquatic vegetation in estuaries and coastal lagoons | Loess plain | forests | farm pasture | Montain forest | tropical, coastal to montane | Beaches | restored landfills and grasslands | shallow coral reefs | Wetlands buffered by grassland within agroecosystems | Wetlands | restored landfills and grasslands |
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
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 coarser than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecosystem | 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 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 | 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 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 |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
?
|
EM-12 ![]() |
EM-63 | EM-68 | EM-82 | EM-84 | EM-91 | EM-93 | EM-103 | EM-105 | EM-137 |
EM-208 ![]() |
EM-320 |
EM-338 ![]() |
EM-397 ![]() |
EM-469 |
EM-485 ![]() |
EM-593 ![]() |
EM-628 | EM-630 |
EM-686 ![]() |
EM-697 ![]() |
EM-698 | EM-702 | EM-706 |
EM-709 ![]() |
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Not applicable | Not applicable | Community | Community | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Species | Community | Species | Not applicable | Species | Species | Not applicable | Community | Not applicable | Not applicable | Not applicable | Not applicable | Individual or population, within a species | Guild or Assemblage | Individual or population, within a species | Not applicable | Individual or population, within a species |
Taxonomic level and name of organisms or groups identified
EM-12 ![]() |
EM-63 | EM-68 | EM-82 | EM-84 | EM-91 | EM-93 | EM-103 | EM-105 | EM-137 |
EM-208 ![]() |
EM-320 |
EM-338 ![]() |
EM-397 ![]() |
EM-469 |
EM-485 ![]() |
EM-593 ![]() |
EM-628 | EM-630 |
EM-686 ![]() |
EM-697 ![]() |
EM-698 | EM-702 | EM-706 |
EM-709 ![]() |
|
None Available | None Available | None Available | None Available | None Available | None Available |
|
|
None Available |
|
None Available |
|
|
None Available | None Available | None Available | None Available | None Available | 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-63 | EM-68 | EM-82 | EM-84 | EM-91 | EM-93 | EM-103 | EM-105 | EM-137 |
EM-208 ![]() |
EM-320 |
EM-338 ![]() |
EM-397 ![]() |
EM-469 |
EM-485 ![]() |
EM-593 ![]() |
EM-628 | EM-630 |
EM-686 ![]() |
EM-697 ![]() |
EM-698 | EM-702 | EM-706 |
EM-709 ![]() |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
None |
|
|
|
|
|
|
<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-12 ![]() |
EM-63 | EM-68 | EM-82 | EM-84 | EM-91 | EM-93 | EM-103 | EM-105 | EM-137 |
EM-208 ![]() |
EM-320 |
EM-338 ![]() |
EM-397 ![]() |
EM-469 |
EM-485 ![]() |
EM-593 ![]() |
EM-628 | EM-630 |
EM-686 ![]() |
EM-697 ![]() |
EM-698 | EM-702 | EM-706 |
EM-709 ![]() |
|
|
|
None |
|
None |
|
|
|
|
None |
|
|
|
None | None |
|
|
None |
|
None |
|
|
|
None |