EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
EM Identity and Description
EM ID
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EM-65 | EM-71 | EM-79 | EM-80 | EM-91 |
EM-102 ![]() |
EM-109 ![]() |
EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-593 ![]() |
EM-605 ![]() |
EM-618 ![]() |
EM-632 ![]() |
EM-649 | EM-700 | EM-836 | EM-884 | EM-941 | EM-946 | EM-959 |
EM Short Name
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Green biomass production, Central French Alps | Community flowering date, Central French Alps | Divergence in flowering date, Central French Alps | Agronomic ES and plant traits, Central French Alps | RHyME2, Upper Mississippi River basin, USA | Fish species habitat value, Tampa Bay, FL, USA | UFORE-Hydro, Baltimore, MD, USA | Cultural ecosystem services, Bilbao, Spain | Coral taxa and land development, St.Croix, VI, USA | InVEST (v1.004) sediment retention, Indonesia | EPA H2O, Tampa Bay Region, FL,USA | SAV occurrence, St. Louis River, MN/WI, USA | Reef snorkeling opportunity, St. Croix, USVI | Value of finfish, St. Croix, USVI | DayCent N2O flux simulation, Ireland | VELMA v2.0, Ohio, USA | WaterWorld v2, Santa Basin, Peru | Waterfowl pairs, CREP wetlands, Iowa, USA | Grasshopper Sparrow density, CREP, Iowa, USA | Mallard recruits, CREP wetlands, Iowa, USA | Bird abundance on restored landfills, UK | VELMA v. 2.0 Hydro | ESTIMAP - Pollination potential, Iran | MIKE-SHE Munich, Germany | NC HUC-12 conservation prioritization tool |
EM Full Name
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Green biomass production, Central French Alps | Community weighted mean flowering date, Central French Alps | Functional divergence in flowering date, Central French Alps | Agronomic ecosystem service estimated from plant functional traits, Central French Alps | RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | Fish species habitat value, Tampa Bay, FL, USA |
UFORE-Hydro (Urban Forest Effects - Hydrology) v1, Dead Run Catchment, Baltimore, MD ?Comment:UFORE-Hydro is now incorporated in the i-Tree suite of models as iTree-Hydro. |
Cultural ecosystem services, Bilbao, Spain | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs v1.004) sediment retention, Sumatra, Indonesia | EPA H2O, Tampa Bay Region, FL, USA | Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA | Relative snorkeling opportunity (in reef), St. Croix, USVI | Relative value of finfish (on reef), St. Croix, USVI | DayCent simulation N2O flux and climate change, Ireland | Visualizing Ecosystems for Land Management Assessments (VELMA) v2.0, Shayler Crossing watershed, Ohio, USA | WaterWorld v2, Santa Basin, Peru | Waterfowl pairs, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Grasshopper Sparrow population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Mallard duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Bird abundance on restored landfills compared to paired reference sites, East Midlands, UK | Visualizing Ecosystems for Land Management Assessments (VELMA) Version 2.0 Hydro | ESTIMAP - Pollination potential, Iran | MIKE SHE model, Regulation of urban surface runoff, Munich Germany | NC HUC-12 conservation prioritization tool v. 1.0, North Carolina, USA |
EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | US EPA | US EPA | i-Tree | USDA Forest Service |
None ?Comment:EU Mapping Studies |
US EPA | InVEST | US EPA | US EPA | US EPA | US EPA | None | US EPA | None | None | None | None | None | US EPA | None | None | None |
EM Source Document ID
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260 | 260 | 260 | 260 | 123 | 187 | 190 | 191 | 96 | 309 | 321 | 330 | 335 | 335 | 358 |
359 ?Comment:Document #366 is a supporting document for this EM. McKane et al. 2014, VELMA Version 2.0 User Manual and Technical Documentation. |
368 | 372 | 372 |
372 ?Comment:Document 373 is a secondary source for this EM. |
406 | 366 | 434 | 440 |
443 ?Comment:Doc 444 is an additional source for this EM |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C. | Fulford, R., Yoskowitz, D., Russell, M., Dantin, D., and Rogers, J. | Wang, J., Endreny, T. A. and Nowak, D. J. | Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Bhagabati, N. K., Ricketts, T., Sulistyawan, T. B. S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A., Tallis, H., and Wolney, S. | Ranade, P., Soter, G., Russell, M., Harvey, J., and K. Murphy | Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Hoghooghi, N., H. E. Golden, B. P. Bledsoe, B. L. Barnhart, A. F. Brookes, K. S. Djang, J. J. Halama, R. B. McKane, C. T. Nietch, and P. P. Pettus | Van Soesbergen, A. and M. Mulligan | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Rahman, M. L., S. Tarrant, D. McCollin, and J. Ollerton | McKane, R. B., A. Brookes, K. Djang, M. Stieglitz, A. G. Abdelnour, F. Pan, J. J. Halama, P. B. Pettus and D. L. Phillips | Rahimi, E., Barghjelveh, S., and P. Dong | Zolch,T., Henze, L., Keilholz, P., and S. Pauleit | Warnell, K., I. Golden, and C. Canfield |
Document Year
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2011 | 2011 | 2011 | 2011 | 2013 | 2016 | 2008 | 2013 | 2011 | 2014 | 2015 | 2013 | 2014 | 2014 | 2010 | 2018 | 2018 | 2010 | 2010 | 2010 | 2011 | 2014 | 2020 | 2017 | 2023 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads | Habitat and recreational fishing opportunity in Tampa Bay: Linking ecological and ecosystem services to human beneficiaries | Mechanistic simulation of tree effects in an urban water balance model | Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Ecosystem services reinforce Sumatran tiger conservation in land use plans | EPA H20 User Manual | Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | 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 | Cumulative effects of low impact development on watershed hydrology in a mixed land-cover system | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | The conservation value of restored landfill sites in the East Midlands, UK for supporting bird communities in the East Midlands, UK for supporting bird communities | VELMA Version 2.0 User Manual and Technical Documentation | Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran) | Regulating urban surface runoff through nature-based solutions – An assessment at the micro-scale | Conservation planning tools for NC's people & nature |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | 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 |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published report | Published report | Published journal manuscript | Published EPA report | Published journal manuscript | Published journal manuscript | Webpage |
EM ID
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EM-65 | EM-71 | EM-79 | EM-80 | EM-91 |
EM-102 ![]() |
EM-109 ![]() |
EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-593 ![]() |
EM-605 ![]() |
EM-618 ![]() |
EM-632 ![]() |
EM-649 | EM-700 | EM-836 | EM-884 | EM-941 | EM-946 | EM-959 |
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://www.itreetools.org/ | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | http://www.epa.gov/ged/tbes/EPAH2O | Not applicable | Not applicable | Not applicable | Not applicable | https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | www.policysupport.org/waterworld | Not applicable | Not applicable | Not applicable | Not applicable | https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | Not applicable | Not applicable | https://prioritizationcobenefitstool.users.earthengine.app/view/nc-huc-12-conservation-prioritizer | |
Contact Name
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Sandra Lavorel | Sandra Lavorel | Sandra Lavorel | Sandra Lavorel | Liem Tran | Richard Fulford | Jun Wang | Izaskun Casado-Arzuaga | Leah Oliver | Nirmal K. Bhagabati | Marc J. Russell, Ph.D. | Ted R. Angradi | Susan H. Yee | Susan H. Yee | M. Abdalla | Heather Golden | Arnout van Soesbergen | David Otis | David Otis | David Otis | Lutfor Rahman | Robert B. McKane | Ehsan Rahini | Teresa Zoelch | Katie Warnell |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA | USEPA Gulf Ecology Division, Gulf Breeze, FL 32561 | Environmental Resources and Forest Engineering, Colecge of Environmental Science and Forestry, State University of New York, Syracuse, New York 13210 | Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain | National Health and Environmental Research Effects Laboratory | The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | USEPA GED, One Sabine Island Dr., Gulf Breeze, FL 32561 | U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | National Exposure Research Laboratory, Office of Research and Development, US EPA, Cincinnati, OH 45268, USA | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Landscape and Biodiversity Research Group, School of Science and Technology, The University of Northampton, Avenue Campus, Northampton NN2 6JD, UK | USEPA Office of Research and Development National Health and Environmental Effects Research Laboratory Western Ecology Division Corvallis, Oregon 97333 | Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran | Technical University of Munich, Centre for Urban Ecology and Climate Adaptation (ZSK), Arcisstraße 21, 80333 Munich, Germany | Not reported |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | ltran1@utk.edu | Fulford.Richard@epa.gov | Not reported | izaskun.casado@ehu.es | leah.oliver@epa.gov | nirmal.bhagabati@wwfus.org | russell.marc@epa.gov | angradi.theodore@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | abdallm@tcd.ie | Golden.Heather@epa.gov | arnout.van_soesbergen@kcl.ac.uk | dotis@iastate.edu | dotis@iastate.edu | dotis@iastate.edu | lutfor.rahman@northampton.ac.uk | mckane.bob@epa.gov | ehsanrahimi666@gmail.com | teresa.zoelch@tum.de | katie.warnell@duke.edu |
EM ID
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EM-65 | EM-71 | EM-79 | EM-80 | EM-91 |
EM-102 ![]() |
EM-109 ![]() |
EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-593 ![]() |
EM-605 ![]() |
EM-618 ![]() |
EM-632 ![]() |
EM-649 | EM-700 | EM-836 | EM-884 | EM-941 | EM-946 | EM-959 |
Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production 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, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. 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 ecosystem properties. 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 (see Albert et al. 2010)." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Functional divergence of flowering date was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | ABSTRACT: "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 Agronomic ecosystem service map is a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to agronomic ecosystem services are based on stakeholders’ perceptions, given positive or negative contributions." | 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: "Estimating value of estuarine habitat to human beneficiaries requires that we understand how habitat alteration impacts function through both production and delivery of ecosystem goods and services (EGS). Here we expand on the habitat valuation technique of Bell (1997) with an estimate of recreational angler willingness-to-pay combined with estimates of angler effort, fish population size, and fish and angler distribution. Results suggest species-specific fishery value is impacted by angler interest and stock status, as the most targeted fish (spotted seatrout) did not have the highest specific value (fish−1). Reduced population size and higher size at capture resulted in higher specific value for common snook. Habitat value estimated from recreational fishing value and fish-angler distributions supported an association between seagrass and habitat value, yet this relationship was also impacted by distance to access points. This analysis does not provide complete valuation of habitat as it considers only one service (fishing), but demonstrates a methodology to consider functional equivalency of all habitat features as a part of a habitat mosaic rather than in isolation, as well as how to consider both EGS production and delivery to humans (e.g., anglers) in any habitat valuation, which are critical for a transition to ecosystem management." | ABSTRACT: "A semidistributed, physical-based Urban Forest Effects – Hydrology (UFORE-Hydro) model was created to simulate and study tree effects on urban hydrology and guide management of urban runoff at the catchment scale. The model simulates hydrological processes of precipitation, interception, evaporation, infiltration, and runoff using data inputs of weather, elevation, and land cover along with nine channel, soil, and vegetation parameters. Weather data are pre-processed by UFORE using Penman-Monteith equations to provide potential evaporation terms for open water and vegetation. Canopy interception algorithms modified established routines to better account for variable density urban trees, short vegetation, and seasonal growth phenology. Actual evaporation algorithms allocate potential energy between leaf surface storage and transpiration from soil storage. Infiltration algorithms use a variable rain rate Green-Ampt formulation and handle both infiltration excess and saturation excess ponding and runoff. Stream discharge is the sum of surface runoff and TOPMODEL- based subsurface flow equations. Automated calibration routines that use observed discharge has been coupled to the model." FURTHER DESCRIPTION: UFORE-Hydro was tested in the urban Dead Run catchment of Baltimore, Maryland, USA. | ABSTRACT "This paper presents a method to quantify cultural ecosystem services (ES) and their spatial distribution in the landscape based on ecological structure and social evaluation approaches. The method aims to provide quantified assessments of ES to support land use planning decisions. A GIS-based approach was used to estimate and map the provision of recreation and aesthetic services supplied by ecosystems in a peri-urban area located in the Basque Country, northern Spain. Data of two different public participation processes (frequency of visits to 25 different sites within the study area and aesthetic value of different landscape units) were used to validate the maps. Three maps were obtained as results: a map showing the provision of recreation services, an aesthetic value map and a map of the correspondences and differences between both services. The data obtained in the participation processes were found useful for the validation of the maps. A weak spatial correlation was found between aesthetic quality and recreation provision services, with an overlap of the highest values for both services only in 7.2 % of the area. A consultation with decision-makers indicated that the results were considered useful to identify areas that can be targeted for improvement of landscape and recreation management." | AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "...Here we use simple spatial analyses on readily available datasets to compare the distribution of five ecosystem services with tiger habitat in central Sumatra. We assessed services and habitat in 2008 and the changes in these variables under two future scenarios: a conservation-friendly Green Vision, and a Spatial Plan developed by the Indonesian government..." AUTHOR'S DESCRIPTION: "We used a modeling tool, InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004; Tallis et al., 2010), to map and quantify tiger habitat quality and five ecosystem services. InVEST maps ecosystem services and the quality of species habitat as production functions of LULC using simple biophysical models. Models were parameterized using data from regional agencies, literature surveys, global databases, site visits and prior field experience (Table 1)... The sediment retention model is based on the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978). It estimates erosion as ton y^-1 of sediment load, based on the energetic ability of rainfall to move soil, the erodibility of a given soil type, slope, erosion protection provided by vegetated LULC, and land management practices. The model routes sediment originating on each land parcel along its flow path, with vegetated parcels retaining a fraction of sediment with varying efficiencies, and exporting the remainder downstream. ...Although InVEST reports ecosystem services in biophysical units, its simple models are best suited to understanding broad patterns of spatial variation (Tallis and Polasky, 2011), rather than for precise quantification. Additionally, we lacked field measurements against which to calibrate our outputs. Therefore, we focused on relative spatial distribution across the landscape, and relative change to scenarios." | AUTHORS DESCRIPTION: "EPA H2O is a GIS based demonstration tool for assessing ecosystem goods and services (EGS). It was developed as a preliminary assessment tool in support of research being conducted in the Tampa Bay watershed. It provides information, data, approaches and guidance that communities can use to examine alternative land use scenarios in the context of nature’s benefits to the human community. . . EPA H2O allows users for the Tampa Bay estuary and its watershed to: • Gain a greater understanding of the significance of EGS, • Explore the spatial distribution of EGS and other ecosystem features, • Obtain map and summary statistics of EGS production's potential value, • Analyze and compare potential impacts from predicted development scenarios or user specified changes in land use patterns on EGS production's potential value EPA H2O is designed for analyzing data at neighborhood to regional scales.. . The tool is transportable to other locations if the required data are available. . . . | ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake." | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…Synthesis of scientific literature and expert opinion can be used to estimate the relative potential for recreational opportunities across different benthic habitat types (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to recreational opportunities as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative recreational opportunity j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A.palmata, Montastraea reef, patch reef, and dense or sparse gorgonians), and Mij is the magnitude associated with each habitat for a given metric j: snorkeling opportunity" | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(5) value of finfish," | 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: "Low Impact Development (LID) is an alternative to conventional urban stormwater management practices, which aims at mitigating the impacts of urbanization on water quantity and quality. Plot and local scale studies provide evidence of LID effectiveness; however, little is known about the overall watershed scale influence of LID practices. This is particularly true in watersheds with a land cover that is more diverse than that of urban or suburban classifications alone. We address this watershed-scale gap by assessing the effects of three common LID practices (rain gardens, permeable pavement, and riparian buffers) on the hydrology of a 0.94 km2 mixed land cover watershed. We used a spatially-explicit ecohydrological model, called Visualizing Ecosystems for Land Management Assessments (VELMA), to compare changes in watershed hydrologic responses before and after the implementation of LID practices. For the LID scenarios, we examined different spatial configurations, using 25%, 50%, 75% and 100% implementation extents, to convert sidewalks into rain gardens, and parking lots and driveways into permeable pavement. We further applied 20 m and 40 m riparian buffers along streams that were adjacent to agricultural land cover…" AUTHOR'S DESCRIPTION: "VELMA’s modeling domain is a three-dimensional matrix that includes information regarding surface topography, land use, and four soil layers. VELMA uses a distributed soil column framework to model the lateral and vertical movement of water and nutrients through the four soil layers. A soil water balance is solved for each layer. The soil column model is placed within a watershed framework to create a spatially distributed model applicable to watersheds (Figure 2, shown here with LID practices). Adjacent soil columns interact through down-gradient water transport. Water entering each pixel (via precipitation or flow from an adjacent pixel) can either first infiltrate into the implemented LID and the top soil layer, and then to the downslope pixel, or continue its downslope movement as the lateral surface flow. Surface and subsurface lateral flow are routed using a multiple flow direction method, as described in Abdelnour et al. [21]. A detailed description of the processes and equations can be found in McKane et al. [32], Abdelnour et al. [21], Abdelnour et al. [40]." | 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: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "Number of duck pairs per site was estimated for 6 species of ducks: Mallard (Anas platyrhynchos), Blue-winged Teal (Anas discors), Northern Shoveler (Anas clypeata), Gadwall (Anas strepera), Northern Pintail (Anas acuta), and Wood Duck (Aix sponsa), using models developed by Cowardin et al. (1995). Pair abundance was based on wetland class (i.e., temporary, seasonal, semi-permanent, lake, or river), wetland size, and a set of species specific regression coefficients. All CREP wetlands were considered semi-permanent for this analysis; therefore only coefficients associated with the semipermanent wetland pair model were used in calculations. The general equation used to estimate the pairs per wetland was: Pairs = e (a + bx + α) * p where, e = mathematical constant ≈ 2.718, a = species specific regression coefficient a, b = species specific regression coefficient b, x = the natural log of wetland size, α = species specific alpha value, and p = proportion of the basin containing water (assumed to be 0.90 for this analysis)" | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Grasshopper Sparrow (Ammodramus savannarum)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: GRSP density = e (-2.554612 + 0.0246975 * grass400 – 0.1032461 * trees400) | ABSTRACT: "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). | ABSTRACT: "There has been a rapid decline of grassland bird species in the UK over the last four decades. In order to stem declines in biodiversity such as this, mitigation in the form of newly created habitat and restoration of degraded habitats is advocated in the UK biodiversity action plan. One potential restored habitat that could support a number of bird species is re-created grassland on restored landfill sites. However, this potential largely remains unexplored. In this study, birds were counted using point sampling on nine restored landfill sites in the East Midlands region of the UK during 2007 and 2008. The effects of restoration were investigated by examining bird species composition, richness, and abundance in relation to habitat and landscape structure on the landfill sites in comparison to paired reference sites of existing wildlife value. Twelve bird species were found in total and species richness and abundance on restored landfill sites was found to be higher than that of reference sites. Restored landfill sites support both common grassland bird species and also UK Red List bird species such as skylark Alauda arvensis, grey partridge Perdix perdix, lapwing Vanellus vanellus, tree sparrow, Passer montanus, and starling Sturnus vulgaris. Size of the site, percentage of bare soil and amount of adjacent hedgerow were found to be the most influential habitat quality factors for the distribution of most bird species. Presence of open habitat and crop land in the surrounding landscape were also found to have an effect on bird species composition. Management of restored landfill sites should be targeted towards UK Red List bird species since such sites could potentially play a significant role in biodiversity action planning." AUTHOR'S DESCRIPTION: "Mean number of birds from multiple visits were used for data analysis. To analyse the data generalized linear models (GLMs) were constructed to compare local habitat and landscape parameters affecting different species, and to establish which habitat and landscape characteristics explained significant changes in the frequency of occurrence for each species. To ensure analyses focused on resident species, habitat associations were modelled for those seven bird species which were recorded at least three times in the surveys. The analysis was carried out with the software R (R Development Core Team 2003). Nonsignificant predictors (independent variables) were removed in a stepwise manner (least significant factor first). For distribution pattern of bird species, data were initially analysed using detrended correspondence analysis. Redundancy analysis (RDA) was performed on the same data using CANOCO for Windows version 4.0 (ter Braak and Smilauer 2002)." | ABSTRACT: "VELMA – Visualizing Ecosystems for Land Management Assessments – is a spatially distributed, eco-hydrological model that links a land surface hydrology model with a terrestrial biogeochemistry model for simulating the integrated responses of vegetation, soil, and water resources to interacting stressors. For example, VELMA can simulate how changes in climate and land use interact to affect soil water storage, surface and subsurface runoff, vertical drainage, evapotranspiration, vegetation and soil carbon and nitrogen dynamics, and transport of nitrate, ammonium, and dissolved organic carbon and nitrogen to water bodies. VELMA differs from other existing eco-hydrology models in its simplicity, flexibility, and theoretical foundation. The model has a user-friendly Graphics User Interface (GUI) for easy input of model parameter values. In addition, advanced visualization of simulation results can enhance understanding of results and underlying concepts. VELMA’s visualization and interactivity features are packaged in an open-source, open-platform programming environment (Java / Eclipse). The development team for VELMA version 2.0 includes Dr. Bob McKane and coworkers at the U.S. Environmental Protection Agency’s Western Ecology Division, Dr. Marc Stieglitz and coworkers at the Georgia Institute of Technology, and Dr. Feifei Pan at the University of North Texas." | Abstract: ". ..we used the ESTIMAP model to improve the results of the Lonsdorf model. For this, we included the effects of roads, railways, rivers, wetlands, lakes, altitude, climate, and ecosystem boundaries in the ESTIMAP modeling and compared the results with the Lonsdorf model. The results of the Lonsdorf model showed that the majority of Iran had a very low potential for providing pollination service and only three percent of the northern and western parts of Iran had high potential. However, the results of the ESTIMAP model showed that 16% of Iran had a high potential to provide pollination that covers most of the northern and southern parts of the country. The results of the ESTIMAP model for pollination mapping in Iran showed the Lonsdorf model of estimating pollination service can be improved through considering other relevant factors." | [Enter up to 65000 characters] | ABSTRACT: "Conservation organizations and land trusts in North Carolina are increasingly focused on how their work can contribute to both human and ecosystem resilience and adaptation to climate change, as well as directly mitigate climate change through carbon storage and sequestration. Recent state executive and legislative actions also underscore the importance of natural systems for climate adaptation and mitigation, and may provide additional funding for conservation and restoration for those purposes in the near term. To make it more efficient for conservation organizations working in North Carolina to consider a broad suite of conservation benefits in their work, the Conservation Trust for North Carolina and the Nicholas Institute for Energy, Environment & Sustainability at Duke University have developed two online tools for identifying priority areas for conservation action and estimating benefit metrics for specific properties. The conservation prioritization tool finds the sub-watersheds in North Carolina with the greatest potential to provide a set of user-selected conservation benefits. It allows users to identify priority areas for future conservation work within the entire state or a defined region. This high-level tool allows for quick and easy exploration without the need for spatial analysis expertise." |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified | Not reported | None identifed | None identified | Land management, ecosystem management, response to EU 2020 Biodiversity Strategy | Not applicable | This analysis provided input to government-led spatial planning and strategic environmental assessments in the study area. This region contains some of the last remaining forest habitat of the critically endangered Sumatran tiger, Panthera tigris sumatrae. | None reported | None identified | None identified | None identified | climate change | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None reported | None | Allows users to prioritize HUCs within their area of interest based on their conservation goals. |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | No additional description provided | shallow bay (mean 3.7m), transition zone between warm temperate and tropical biogeographic provinces. Highly urbanized watershed | No additional description provided | Northern Spain; Bizkaia region | nearshore; <1.5 km offshore; <12 m depth | Six watersheds in central Sumatra covering portions of Riau, Jambi and West Sumatra provinces. The Barisan mountain range comprises the western edge of the watersheds, while peat swamps predominate in the east. | Not applicable | submerged aquatic vegetation | No additional description provided | No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | The Shayler Crossing (SHC) watershed is a subwatershed of the East Fork Little Miami River Watershed in southwest Ohio, USA and falls within the Till Plains region of the Central Lowland physiographic province. The Till Plains region is a topographically young and extensive flat plain, with many areas remaining undissected by even the smallest stream. The bedrock is buried under a mantle of glacial drift 3–15 m thick. The Digital Elevation Model (DEM) has a maximum value of ~269 m (North American_1983 datum) within the watershed boundary (Figure 1). The soils are primarily the Avonburg and Rossmoyne series, with high silty clay loam content and poor to moderate infiltration. Average annual precipitation for the period, 1990 through 2011, was 1097.4 _ 173.5 mm. Average annual air temperature for the same period was 12 _C Mixed land cover suburban watershed. The primary land uses consist of 64.1% urban or developed area (including 37% lawn, 12% building, 6.5% street, 6.4% sidewalk, and 2.1% parking lot and driveway), 23% agriculture, and 13% deciduous forest. Total imperviousness covers approximately 27% of the watershed area. | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | Prairie pothole region of north-central Iowa | Prairie pothole region of north-central Iowa | Prairie Pothole Region of Iowa | The study area covered mainly Northamptonshire and parts of Bedfordshire, Buckinghamshire and Warwickshire, ranging from 51o58’44.74” N to 52o26’42.18” N and 0o27’49.94” W to 1o19’57.67” W. This region has countryside of low, undulating hills separated by valleys and lies entirely within the great belt of scarplands formed by rocks of Jurassic age which stretch across England from Yorkshire to Dorset (Beaver 1943; Sutherland 1995; Wilson 1995). | No additional description provided | None additional | None | No additional description provided |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented |
Base case; increase pervious area tree cover to 40%; increase impervious area tree cover to 40%; double impervious area to 60%; halve pervious area tree cover to 6%; double pervious area tree cover to 24% and increase pervious area tree cover to 20%. ?Comment:Base case is existing conditions. |
No scenarios presented | Not applicable | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | Land Use, EGS algorithm values, | No scenarios presented | No scenarios presented | No scenarios presented | air temperature, precipitation, Atmospheric CO2 concentrations | Three types of Low Impact Development (LID) practices (rain gardens, permeable pavements, forested riparian buffers) applied a different conversion levels. | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | N/A | None | No scenarios presented |
EM ID
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EM-65 | EM-71 | EM-79 | EM-80 | EM-91 |
EM-102 ![]() |
EM-109 ![]() |
EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-593 ![]() |
EM-605 ![]() |
EM-618 ![]() |
EM-632 ![]() |
EM-649 | EM-700 | EM-836 | EM-884 | EM-941 | EM-946 | EM-959 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method Only | Method Only | Method + Application | None | Method Only |
New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | New or revised model | Application of existing model | New or revised model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
New or revised model | New or revised model | New or revised model | Application of existing model | None | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
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EM-65 | EM-71 | EM-79 | EM-80 | EM-91 |
EM-102 ![]() |
EM-109 ![]() |
EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-593 ![]() |
EM-605 ![]() |
EM-618 ![]() |
EM-632 ![]() |
EM-649 | EM-700 | EM-836 | EM-884 | EM-941 | EM-946 | EM-959 |
EM Temporal Extent
em.detail.tempExtentHelp
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2007-2009 | 2007-2008 | 2007-2008 | Not reported | 1987-1997 | 2006-2011 | 2000 | 2000 - 2007 | 2006-2007 | 2008-2020 | Not applicable | 2010 - 2012 | 2006-2007, 2010 | 2006-2007, 2010 | 1961-1990 | Jan 1, 2009 to Dec 31, 2011 | 1950-2071 | 2002-2007 | 2002-2007 | 1987-2007 | Not applicable |
Not applicable ?Comment:User defined model duration. |
2020 | None | Not applicable |
EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | None | time-stationary |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | both | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | both | past time | both | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | None | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | None | Not applicable |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | None | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Hour | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Day | Day | Month | Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable | None | Not applicable |
EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-79 | EM-80 | EM-91 |
EM-102 ![]() |
EM-109 ![]() |
EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-593 ![]() |
EM-605 ![]() |
EM-618 ![]() |
EM-632 ![]() |
EM-649 | EM-700 | EM-836 | EM-884 | EM-941 | EM-946 | EM-959 |
Bounding Type
em.detail.boundingTypeHelp
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Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Watershed/Catchment/HUC | Physiographic or Ecological | Watershed/Catchment/HUC | Geopolitical | Physiographic or Ecological | Watershed/Catchment/HUC |
Geopolitical ?Comment:Extent was Tampa Bay area in example, but boundary can be geopolitical or watershed derived. |
Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Point or points | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Not applicable | Geopolitical | None | Not applicable |
Spatial Extent Name
em.detail.extentNameHelp
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Central French Alps | Central French Alps | Central French Alps | Central French Alps | Upper Mississippi River basin; St. Croix River Watershed | Tampa Bay | Dead Run Catchement, Baltimore, MD | Bilbao Metropolitan Greenbelt | St.Croix, U.S. Virgin Islands | central Sumatra | Tampa Bay region | St. Louis River Estuary | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Oak Park Research centre | Shayler Crossing watershed, a subwatershed of the East Fork Little Miami River Watershed | Santa Basin | CREP (Conservation Reserve Enhancement Program) wetland sites | CREP (Conservation Reserve Enhancement Program) wetland sites | CREP (Conservation Reserve Enhancement Program | East Midland | Not applicable | Iran | None | Not applicable |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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10-100 km^2 | 10-100 km^2 | 10-100 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | 10-100 km^2 | 100-1000 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | 10-100 km^2 | 100-1000 km^2 | 100-1000 km^2 | 1-10 ha | 10-100 ha | 10,000-100,000 km^2 | 1-10 km^2 | 1-10 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | Not applicable | >1,000,000 km^2 | None | Not applicable |
EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-79 | EM-80 | EM-91 |
EM-102 ![]() |
EM-109 ![]() |
EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-593 ![]() |
EM-605 ![]() |
EM-618 ![]() |
EM-632 ![]() |
EM-649 | EM-700 | EM-836 | EM-884 | EM-941 | EM-946 | EM-959 |
EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | 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) ?Comment:BH: Each individual transect?s data was parceled into location reports, and that each report?s ?quadrat? area was dependent upon the angle of the hydroacoustic sampling beam. The spatial grain is 0.07 m^2, 0.20 m^2 and 0.70 m^2 for depths of 1 meter, 2 meters and 3 meters, respectively. |
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) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:User defined scale, from plot to basin size. |
spatially distributed (in at least some cases) ?Comment:Varies by inputs, but results are for areas of country |
None | spatially distributed (in at least some cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | NHDplus v1 | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | 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 | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | None | map scale, for cartographic feature |
Spatial Grain Size
em.detail.spGrainSizeHelp
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20 m x 20 m | 20 m x 20 m | 20 m x 20 m | 20 m x 20 m | NHDplus v1 | 1 km^2 | irregular topographically delineated similar units | 2 m x 2 m | Not applicable | 30 m x 30 m | 30m x 30m | 0.07 m^2 to 0.70 m^2 | 10 m x 10 m | 10 m x 10 m | Not applicable | 10m x 10m | 1 km2 | multiple, individual, irregular shaped sites | multiple, individual, irregular shaped sites | multiple, individual, irregular sites | multiple unrelated sites | user defined | ha^2 | None | HUC 12 |
EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-79 | EM-80 | EM-91 |
EM-102 ![]() |
EM-109 ![]() |
EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-593 ![]() |
EM-605 ![]() |
EM-618 ![]() |
EM-632 ![]() |
EM-649 | EM-700 | EM-836 | EM-884 | EM-941 | EM-946 | EM-959 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Numeric | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | * | Other or unclear (comment) |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | None | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-79 | EM-80 | EM-91 |
EM-102 ![]() |
EM-109 ![]() |
EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-593 ![]() |
EM-605 ![]() |
EM-618 ![]() |
EM-632 ![]() |
EM-649 | EM-700 | EM-836 | EM-884 | EM-941 | EM-946 | EM-959 |
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | No | No | Yes | No | Yes | No | Yes | No | No | Yes | Yes | Yes | No | Yes | No | Unclear | Unclear | Unclear | Not applicable | Not applicable | No | None | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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Yes | Yes | Yes | No | Yes | No | Yes | No | Yes | No | No | Yes | No | No |
Yes ?Comment:for N2O fluxes |
Yes ?Comment:Goodness of fit for calibrated (2009-2010) and observed streamflow. |
No | No | No | No | Not applicable | Not applicable | No | None | Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None |
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None | None |
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None | None |
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None | None | None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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Yes | No | No | No | No | No | Yes | Yes | No | No | No | Yes | Yes | Yes | Yes | Yes | Yes | Unclear | Unclear | No | Not applicable | Not applicable | No | None | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | No | No | Unclear | No | Yes | No | No | No | No | No | No | No | No | No | No | No | Not applicable | Not applicable | No | None | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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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. |
No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | Not applicable | Not applicable | No | None | Not applicable |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | None | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-65 | EM-71 | EM-79 | EM-80 | EM-91 |
EM-102 ![]() |
EM-109 ![]() |
EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-593 ![]() |
EM-605 ![]() |
EM-618 ![]() |
EM-632 ![]() |
EM-649 | EM-700 | EM-836 | EM-884 | EM-941 | EM-946 | EM-959 |
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None |
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None | None |
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None |
Comment:Model for Iran - no form preset id for country |
None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-65 | EM-71 | EM-79 | EM-80 | EM-91 |
EM-102 ![]() |
EM-109 ![]() |
EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-593 ![]() |
EM-605 ![]() |
EM-618 ![]() |
EM-632 ![]() |
EM-649 | EM-700 | EM-836 | EM-884 | EM-941 | EM-946 | EM-959 |
None | None | None | None | None |
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None | None |
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None | None | None |
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None | None | None | None | None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-79 | EM-80 | EM-91 |
EM-102 ![]() |
EM-109 ![]() |
EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-593 ![]() |
EM-605 ![]() |
EM-618 ![]() |
EM-632 ![]() |
EM-649 | EM-700 | EM-836 | EM-884 | EM-941 | EM-946 | EM-959 |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | 45.05 | 45.05 | 45.05 | 42.5 | 27.74 | 39.31 | 43.25 | 17.75 | 0 | 28.05 | 46.72 | 17.73 | 17.73 | 52.86 | 39.19 | -9.05 | 42.62 | 42.62 | 42.62 | 52.22 | Not applicable | 32.29 | None | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
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6.4 | 6.4 | 6.4 | 6.4 | -90.63 | -82.57 | -76.74 | -2.92 | -64.75 | 102 | -82.52 | -96.13 | -64.77 | -64.77 | 6.54 | -84.29 | -77.81 | -93.84 | -93.84 | -93.84 | -0.91 | Not applicable | 53.68 | None | Not applicable |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | NAD83 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | None | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Provided | Provided | Provided | Estimated | Estimated | Provided | Provided | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Provided | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | None | Not applicable |
EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-79 | EM-80 | EM-91 |
EM-102 ![]() |
EM-109 ![]() |
EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-593 ![]() |
EM-605 ![]() |
EM-618 ![]() |
EM-632 ![]() |
EM-649 | EM-700 | EM-836 | EM-884 | EM-941 | EM-946 | EM-959 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Near Coastal Marine and Estuarine | Rivers and Streams | Ground Water | Created Greenspace | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Near Coastal Marine and Estuarine | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Agroecosystems | Rivers and Streams | Ground Water | Forests | Agroecosystems | Created Greenspace | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Created Greenspace | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | None | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | None | Habitat Zones (Low, Med, High, Optimal) around seagrass and emergent marsh | Urban watershed | none | stony coral reef | 104 land use land cover classes | All terestrial landcover and waterbodies | Freshwater estuarine system | Coral reefs | Coral reefs | farm pasture | Mixed land cover suburban watershed | tropical, coastal to montane | Wetlands buffered by grassland set in agricultural land | Grassland buffering inland wetlands set in agricultural land | Wetlands buffered by grassland within agroecosystems | restored landfills and conserved grasslands | Terrestrial | terrestrial land types | None | Terrestrial and freshwater aquatic |
EM Ecological Scale
em.detail.ecoScaleHelp
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Not applicable | Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecosystem | Zone within an ecosystem | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 |
Other or unclear (comment) ?Comment:Variable data was derived from multiple climate data stations distrubuted across the study area. The location and distribution of the data stations was not provided. |
Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | None | Ecological scale is coarser than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-79 | EM-80 | EM-91 |
EM-102 ![]() |
EM-109 ![]() |
EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-593 ![]() |
EM-605 ![]() |
EM-618 ![]() |
EM-632 ![]() |
EM-649 | EM-700 | EM-836 | EM-884 | EM-941 | EM-946 | EM-959 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Community | Community | Community | Not applicable | Species | Community | Not applicable | Guild or Assemblage | Community | Not applicable | Not applicable | Guild or Assemblage | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Species | Species | Individual or population, within a species | Individual or population, within a species | Not applicable | Not applicable | None | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-65 | EM-71 | EM-79 | EM-80 | EM-91 |
EM-102 ![]() |
EM-109 ![]() |
EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-593 ![]() |
EM-605 ![]() |
EM-618 ![]() |
EM-632 ![]() |
EM-649 | EM-700 | EM-836 | EM-884 | EM-941 | EM-946 | EM-959 |
None Available | None Available | None Available | None Available | None Available |
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None Available | None Available |
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None Available | None Available | None Available | None Available |
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None Available | None Available | None Available |
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None Available |
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None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-65 | EM-71 | EM-79 | EM-80 | EM-91 |
EM-102 ![]() |
EM-109 ![]() |
EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-593 ![]() |
EM-605 ![]() |
EM-618 ![]() |
EM-632 ![]() |
EM-649 | EM-700 | EM-836 | EM-884 | EM-941 | EM-946 | EM-959 |
None | None | None |
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None | 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-65 | EM-71 | EM-79 | EM-80 | EM-91 |
EM-102 ![]() |
EM-109 ![]() |
EM-193 | EM-260 |
EM-359 ![]() |
EM-392 | EM-414 | EM-454 | EM-462 |
EM-593 ![]() |
EM-605 ![]() |
EM-618 ![]() |
EM-632 ![]() |
EM-649 | EM-700 | EM-836 | EM-884 | EM-941 | EM-946 | EM-959 |
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None | None |
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None |
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None |
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None | None |
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None | None |
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None | None |