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-12 ![]() |
EM-59 ![]() |
EM-66 | EM-79 | EM-80 | EM-86 | EM-99 | EM-121 | EM-132 |
EM-146 ![]() |
EM-321 ![]() |
EM-333 ![]() |
EM-414 |
EM-672 ![]() |
EM-686 ![]() |
EM-845 | EM-846 | EM-847 | EM-884 | EM-892 |
EM Short Name
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Evoland v3.5 (bounded growth), Eugene, OR, USA | EnviroAtlas-Air pollutant removal | Litter biomass production, Central French Alps | Divergence in flowering date, Central French Alps | Agronomic ES and plant traits, Central French Alps | Area and hotspots of soil retention, South Africa | Landscape importance for crops, Europe | Landscape importance for recreation, Europe | Flood regulation capacity, Etropole, Bulgaria | FORCLIM v2.9, Transect in Western OR, USA | Erosion prevention by vegetation, Portel, Portugal | Evoland v3.5 (unbounded growth), Eugene, OR, USA | SAV occurrence, St. Louis River, MN/WI, USA | Alewife nutrients in stream food web, CT, USA | Estuary recreational use, Cape Cod, MA | Red-winged blackbird abun, Piedmont region, USA | Indigo bunting abund, Piedmont region, USA | Eastern kingbird abundance, Piedmont region, USA | VELMA v. 2.0 Hydro | VELMA v. 2.1 contaminant modeling |
EM Full Name
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Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA | US EPA EnviroAtlas - Pollutants (air) removed annually by tree cover; Example is shown for Durham NC and vicinity, USA | Litter biomass production, Central French Alps | Functional divergence in flowering date, Central French Alps | Agronomic ecosystem service estimated from plant functional traits, Central French Alps | Area and hotspots of soil retention, South Africa | Landscape importance for crop-based production, Europe | Landscape importance for recreation, Europe | Flood regulation capacity of landscapes, Municipality of Etropole, Bulgaria | FORCLIM (FORests in a changing CLIMate) v2.9, Western OR, USA | Soil erosion prevention provided by vegetation cover, Portel municipality, Portugal | Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA | Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA | Alewife derived nutrients in stream food web, Connecticut, USA | Estuary recreational use, Cape Cod, MA | Red-winged blackbird abundance, Piedmont ecoregion, USA | Indigo bunting abundance, Piedmont ecoregion, USA | Eastern kingbird abundance, Piedmont ecoregion, USA | Visualizing Ecosystems for Land Management Assessments (VELMA) Version 2.0 Hydro | VELMA (Visualizing Ecosystem Land Management Assessments) v. 2.1 contaminant modeling |
EM Source or Collection
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Envision |
US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Eco model. |
EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | US EPA | EU Biodiversity Action 5 | Envision | US EPA | None | US EPA | None | None | None | US EPA | US EPA |
EM Source Document ID
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47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
223 | 260 | 260 | 260 | 271 | 228 | 228 | 248 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
281 |
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
330 | 384 | 387 | 405 | 405 | 405 | 366 |
423 ?Comment:Document #430 is an additional source for this EM. Document #423 has been imcorporated into the more recently published document #430. |
Document Author
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Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | US EPA Office of Research and Development - National Exposure Research Laboratory | 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. | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Haines-Young, R., Potschin, M. and Kienast, F. | Haines-Young, R., Potschin, M. and Kienast, F. | Nedkov, S., Burkhard, B. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Guerra, C.A., Pinto-Correia, T., Metzger, M.J. | Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke | Walters, A. W., R. T. Barnes, and D. M. Post | Mulvaney, K K., Atkinson, S.F., Merrill, N.H., Twichell, J.H., and M.J. Mazzotta | Riffel, S., Scognamillo, D., and L. W. Burger | Riffel, S., Scognamillo, D., and L. W. Burger | Riffel, S., Scognamillo, D., and L. W. Burger | McKane, R. B., A. Brookes, K. Djang, M. Stieglitz, A. G. Abdelnour, F. Pan, J. J. Halama, P. B. Pettus and D. L. Phillips | McKane |
Document Year
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2008 | 2013 | 2011 | 2011 | 2011 | 2008 | 2012 | 2012 | 2012 | 2007 | 2014 | 2008 | 2013 | 2009 | 2019 | 2008 | 2008 | 2008 | 2014 | None |
Document Title
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Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | EnviroAtlas - Featured Community | 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 | Mapping ecosystem services for planning and management | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Mapping soil erosion prevention using an ecosystem service modeling framework for integrated land management and policy | Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary | Anadromous alewives (Alosa pseudoharengus) contribute marine-derived nutrients to coastal stream food webs | Quantifying Recreational Use of an Estuary: A case study of three bays, Cape Cod, USA | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | VELMA Version 2.0 User Manual and Technical Documentation | Tutorial A.1 – Contaminant Fate and Transport Modeling Concepts; VELMA 2.1 “How To” Documentation |
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 but unpublished (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Draft manuscript-work progressing | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report | Published EPA report |
EM ID
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EM-12 ![]() |
EM-59 ![]() |
EM-66 | EM-79 | EM-80 | EM-86 | EM-99 | EM-121 | EM-132 |
EM-146 ![]() |
EM-321 ![]() |
EM-333 ![]() |
EM-414 |
EM-672 ![]() |
EM-686 ![]() |
EM-845 | EM-846 | EM-847 | EM-884 | EM-892 |
http://evoland.bioe.orst.edu/ ?Comment:Software is likely available. |
https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://evoland.bioe.orst.edu/ | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=354355 | |
Contact Name
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Michael R. Guzy | EnviroAtlas Team | Sandra Lavorel | Sandra Lavorel | Sandra Lavorel | Benis Egoh | Marion Potschin | Marion Potschin | Stoyan Nedkov | Richard T. Busing | Carlos A. Guerra | Michael R. Guzy | Ted R. Angradi | Annika W. Walters | Mulvaney, Kate | Sam Riffell | Sam Riffell | Sam Riffell | Robert B. McKane | Robert B. McKane |
Contact Address
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Oregon State University, Dept. of Biological and Ecological Engineering | Not reported | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | National Institute of Geophysics, Geodesy and Geography, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, bl.3, 1113 Sofia, Bulgaria | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | Instituto de Ciências Agrárias e Ambientais Mediterrânicas, Universidade de Évora, Pólo da Mitra, Apartado 94, 7002-554 Évora, Portugal | Oregon State University, Dept. of Biological and Ecological Engineering | 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 | Dept. of Ecology and Evolutionary Biology, Yale University, New Haven CT 06511 | US EPA, ORD, NHEERL, Atlantic Ecology Division, Narragansett, RI | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | USEPA Office of Research and Development National Health and Environmental Effects Research Laboratory Western Ecology Division Corvallis, Oregon 97333 | US EPA, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon 97333 |
Contact Email
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Not reported | enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | Not reported | marion.potschin@nottingham.ac.uk | marion.potschin@nottingham.ac.uk | snedkov@abv.bg | rtbusing@aol.com | cguerra@uevora.pt | Not reported | angradi.theodore@epa.gov | annika.walters@yale.edu | Mulvaney.Kate@epa.gov | sriffell@cfr.msstate.edu | sriffell@cfr.msstate.edu | sriffell@cfr.msstate.edu | mckane.bob@epa.gov | mckane.bob@epa.gov |
EM ID
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EM-12 ![]() |
EM-59 ![]() |
EM-66 | EM-79 | EM-80 | EM-86 | EM-99 | EM-121 | EM-132 |
EM-146 ![]() |
EM-321 ![]() |
EM-333 ![]() |
EM-414 |
EM-672 ![]() |
EM-686 ![]() |
EM-845 | EM-846 | EM-847 | EM-884 | EM-892 |
Summary Description
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**Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | The Air Pollutant Removal model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina. ... pollution removal ... are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA: The maps, estimate and illustrate the variation in the amount of six airborne pollutants, carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter (PM10), and particulate matter (PM2.5), removed by trees. PM10 is for particulate matter greater than 2.5 microns and less than 10 microns. DATA FACT SHEET: "The data for this map are based on the land cover derived for each EnviroAtlas community and the pollution removal models in i-Tree, a toolkit developed by the USDA Forest Service. The land cover data were created from aerial photography through remote sensing methods; tree cover was then summarized as the percentage of each census block group. The i-Tree pollution removal module uses the tree cover data by block group, the closest hourly meteorological monitoring data for the community, and the closest pollution monitoring data... hourly estimates of pollution removal by trees were combined with atmospheric data to estimate hourly percent air quality improvement due to pollution removal for each pollutant." | 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., litter biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in litter 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…Litter biomass production for each pixel was calculated and mapped using model estimates...This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on litter mass. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. 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." | AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil retention was modelled as a function of vegetation or litter cover and soil erosion potential. Schoeman et al. (2002) modelled soil erosion potential and derived eight erosion classes, ranging from low to severe erosion potential for South Africa. The vegetation cover was mapped by ranking vegetation types using expert knowledge of their ability to curb erosion. We used Schulze (2004) index of litter cover which estimates the soil surface covered by litter based on observations in a range of grasslands, woodlands and natural forests. According to Quinton et al. (1997) and Fowler and Rockstrom (2001) soil erosion is slightly reduced with about 30%, significantly reduced with about 70% vegetation cover. The range of soil retention was mapped by selecting all areas that had vegetation or litter cover of more than 30% for both the expert classified vegetation types and litter accumulation index within areas with moderate to severe erosion potential. The hotspot was mapped as areas with severe erosion potential and vegetation/litter cover of at least 70% where maintaining the cover is essential to prevent erosion. An assumption was made that the potential for this service is relatively low in areas with little natural vegetation or litter cover." | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Crop-based production” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain." AUTHOR'S DESCRIPTION: "The analysis for "Crop-based production" maps all the areas that are important for food crops produced through commercial agriculture." | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Recreation” ... The potential to deliver services is assumed to be influenced by land-use ... and bioclimatic and landscape properties such as mountainous terrain, adjacency to coastal and wetland ecosystems, as well as adjacency to landscape protection zones." AUTHOR'S DESCRIPTION: "Recreation… is broadly defined as all areas where landscape properties are favourable for active recreation purposes." | ABSTRACT: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. In this study, the capacities of different ecosystems to regulate floods were assessed through investigations of water retention functions of the vegetation and soil cover. Based on spatial land cover units originating from CORINE and further data sets, these regulating ecosystem services were quantified and mapped. Resulting maps show the ecosystems’ flood regulating service capacities in the case study area of the Malki Iskar river basin above the town of Etropole in the northern part of Bulgaria...The resulting map of flood regulation supply capacities shows that the Etropole municipality’s area has relatively high capacities for flood regulation. Areas of high and very high relevant capacities cover about 34% of the study area." AUTHOR'S DESCRIPTION: "The capacities of the identified spatial units were assessed on a relative scale ranging from 0 to 5 (after Burkhard et al., 2009). A 0-value indicates that there is no relevant capacity to supply flood regulating services and a 5-value indicates the highest relevant capacity for the supply of these services in the case study region. Values of 2, 3 and 4 represent respective intermediate supply capacities. Of course it depends on the observer’s estimation and knowledge which function–service relations in general are supposed to be relevant. But, this scale offers an alternative relative evaluation scheme, avoiding the presentation of monetary or normative value-transfer results. The 0–5 capacity values’ classifications for the different land cover types were based on the spatial analyses of different biogeophysical and land use data combined with hydrological modeling as described before…The supply capacities of the land cover classes and soil types in the study area were assigned to every unit in their databases. GIS map layers, containing information about the capacity to supply flood regulation for every polygon, were created. The map of supply capacities of flood regulating ecosystem services was elaborated by overlaying the GIS map layers of the land cover and the soils’ capacities." | ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA." Author's Description: "The first set of tests involved eight sites on western Oregon transect from west to east… Individual sites were chosen to represent a particular type of potential natural vegetation as described by Franklin and Dyrness (1988)." | ABSTRACT: "We present an integrative conceptual framework to estimate the provision of soil erosion prevention (SEP) by combining the structural impact of soil erosion and the social–ecological processes that allow for its mitigation. The framework was tested and illustrated in the Portel municipality in Southern Portugal, a Mediterranean silvo-pastoral system that is prone to desertification and soil degradation. The results show a clear difference in the spatial and temporal distribution of the capacity for ecosystem service provision and the actual ecosystem service provision." AUTHOR'S DESCRIPTION: "To begin assessing the contribution of SEP we need to identify the structural impact of soil erosion, that is, the erosion that would occur when vegetation is absent and therefore no ES is provided. It determines the potential soil erosion in a given place and time and is related to rainfall erosivity (that is, the erosive potential of rainfall), soil erodibility (as a characteristic of the soil type) and local topography. Although external drivers can have an effect on these variables (for example, climate change), they are less prone to be changed directly by human action. The actual ES provision reduces the total amount of structural impact, and we define the remaining impact as the ES mitigated impact. We can then define the capacity for ES provision as a key component to determine the fraction of the structural impact that is mitigated…Following the conceptual outline, we will estimate the SEP provided by vegetation cover using an adaptation of the Universal Soil Loss Equation (USLE)." | **Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | 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: "Diadromous fish are an important link between marine and freshwater food webs. Pacific salmon (Oncorhynchus spp.) strongly impact nutrient dynamics in inland waters and anadromous alewife (Alosa pseudoharengus) may play a similar ecological role along the Atlantic coast. The annual spawning migration of anadromous alewife contributes, on average, 1050 g of nitrogen and 120 g of phosphorus to Bride Brook, Connecticut, USA, through excretion and mortality each year. Natural abundance stable isotope analyses indicate that this influx of marine-derived nitrogen is rapidly incorporated into the stream food web. An enriched d15N signal, indicative of a marine origin, is present at all stream trophic levels with the greatest level of enrichment coincident with the timing of the anadromous alewife spawning migration. There was no significant effect of this nutrient influx on water chemistry, leaf decomposition, or periphyton accrual. Dam removal and fish ladder construction will allow anadromous alewife to regain access to historical freshwater spawning habitats, potentially impacting food web dynamics and nutrient cycling in coastal freshwater systems." AUTHOR'S DESCRIPTION: "Here, we examine the effect of alewife-contributed marine- derived nutrients to coastal stream ecosystems in southern New England. We take a comparative approach examining streams with and without anadromous alewife runs. We use natural abundance stable isotope analyses to assess the incorporation of marine-derived nitrogen and carbon into stream food webs." | [ABSTRACT: "Estimates of the types and number of recreational users visiting an estuary are critical data for quantifying the value of recreation and how that value might change with variations in water quality or other management decisions. However, estimates of recreational use are minimal and conventional intercept surveys methods are often infeasible for widespread application to estuaries. Therefore, a practical observational sampling approach was developed to quantify the recreational use of an estuary without the use of surveys. Designed to be simple and fast to allow for replication, the methods involved the use of periodic instantaneous car counts multiplied by extrapolation factors derived from all-day counts. This simple sampling approach can be used to estimate visitation to diverse types of access points on an estuary in a single day as well as across multiple days. Evaluation of this method showed that when periodic counts were taken within a preferred time window (from 11am-4:30pm), the estimates were within 44 percent of actual daily visitation. These methods were applied to the Three Bays estuary system on Cape Cod, USA. The estimated combined use across all its public access sites is similar to the use at a mid-sized coastal beach, demonstrating the value of estuarine systems. Further, this study is the first to quantify the variety and magnitude of recreational uses at several different types of access points throughout the estuary using observational methods. This model focused on the various use by access point type (beaches, landings and way to water, boat use). This work can be transferred to the many small coastal access points used for recreation across New England and beyond." ] | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds." | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds." | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. " | 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: "This document describes the conceptual framework underpinning the use of VELMA 2.1 to model fate and transport of organic contaminants within watersheds. We review how VELMA 2.1 simulates contaminant fate and transport within soils and hillslopes as a function of two processes: (1) the partitioning of the total amount of a contaminant between sorbed (immobile) and aqueous (mobile) phases; and (2) the vertical and lateral transport of the contaminant’s aqueous phase within surface and subsurface waters." |
Specific Policy or Decision Context Cited
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Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None Identified | None identified | Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None identified | Nutrients and water quality related to anadromous alewife restoration efforts | None identified | None reported | None reported | None reported | None identified | None identified |
Biophysical Context
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No additional description provided | No additional description provided | Elevation ranges from 1552 to 2442 m, on predominately 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 | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | No additional description provided | No additional description provided | Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak. | Coastal to montane | Open savannah-like forest of cork (Quercus suber) and holm (Quercus ilex) oaks, with trees of different ages randomly dispersed in changing densities, and pastures in the under cover. The pastures are mostly natural in a mosaic with patches of shrubs, which differ in size and the distribution depends mainly on the grazing intensity. Shallow, poor soils are prone to erosion, especially in areas with high grazing pressure. | No additional description provided | submerged aquatic vegetation | No additional description provided | None identified | Conservation Reserve Program lands left to go fallow | Conservation Reserve Program lands left to go fallow | Conservation Reserve Program lands left to go fallow | No additional description provided | No additional description provided |
EM Scenario Drivers
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Five scenarios that include urban growth boundaries and various combinations of unconstrainted development, fish conservation, agriculture and forest reserves. ?Comment:Additional alternatives included adding agricultural and forest reserves, and adding or removing urban growth boundaries to the three main scenarios. |
No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Different land management practices as represented by the comparison of different grazing intensities (i.e., livestock densities) in the whole study area and in three Civil Parishes within the study area | Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves. | No scenarios presented | No scenarios presented | N/A | N/A | N/A | N/A | No scenarios presented | No scenarios presented |
EM ID
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EM-12 ![]() |
EM-59 ![]() |
EM-66 | EM-79 | EM-80 | EM-86 | EM-99 | EM-121 | EM-132 |
EM-146 ![]() |
EM-321 ![]() |
EM-333 ![]() |
EM-414 |
EM-672 ![]() |
EM-686 ![]() |
EM-845 | EM-846 | EM-847 | EM-884 | EM-892 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Each of the seven runs represents a different site (ecoregion) along a west to east Oregon transect. An eighth site was not forested and its results were not included. |
Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method Only | Method Only |
New or Pre-existing EM?
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New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | 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 |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
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EM-12 ![]() |
EM-59 ![]() |
EM-66 | EM-79 | EM-80 | EM-86 | EM-99 | EM-121 | EM-132 |
EM-146 ![]() |
EM-321 ![]() |
EM-333 ![]() |
EM-414 |
EM-672 ![]() |
EM-686 ![]() |
EM-845 | EM-846 | EM-847 | EM-884 | EM-892 |
EM Temporal Extent
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1990-2050 | 2008-2010 | Not reported | 2007-2008 | Not reported | Not reported | 2000 | 2000 | Not reported | 1500 yrs | January to December 2003 | 1990-2050 | 2010 - 2012 | 2005-2006 (March-July) | Summer 2017 | 2008 | 2008 | 2008 |
Not applicable ?Comment:User defined model duration. |
Not applicable |
EM Time Dependence
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time-dependent | 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-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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future time | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | future time | future time | Not applicable | past time | past time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
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discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | discrete | Not applicable |
other or unclear (comment) ?Comment:Sampling conducted at discrete time periods during Alewife migration. Three sampling periods are presented in this entry. |
discrete | Not applicable | Not applicable | Not applicable | discrete | discrete |
EM Temporal Grain Size Value
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2 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 1 | 2 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | 1 | 1 |
EM Temporal Grain Size Unit
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Year | Hour | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Month | Year | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable | Day | Day |
EM ID
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EM-12 ![]() |
EM-59 ![]() |
EM-66 | EM-79 | EM-80 | EM-86 | EM-99 | EM-121 | EM-132 |
EM-146 ![]() |
EM-321 ![]() |
EM-333 ![]() |
EM-414 |
EM-672 ![]() |
EM-686 ![]() |
EM-845 | EM-846 | EM-847 | EM-884 | EM-892 |
Bounding Type
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Geopolitical | Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Geopolitical | Geopolitical | Geopolitical | Physiographic or ecological | Geopolitical | Geopolitical | Physiographic or ecological | Geopolitical | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Not applicable | Not applicable |
Spatial Extent Name
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Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Durham NC and vicinity | Central French Alps | Central French Alps | Central French Alps | South Africa | The EU-25 plus Switzerland and Norway | The EU-25 plus Switzerland and Norway | Municipality of Etropole | Western Oregon transect | Portel municipality | Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | St. Louis River Estuary | New London County, Connecticut, USA | Three Bays, Cape Cod | Piedmont Ecoregion | Piedmont Ecoregion | Piedmont Ecoregion | Not applicable | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 100-1000 km^2 | 10-100 km^2 | 10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 10-100 km^2 | 10-100 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | Not applicable | Not applicable |
EM ID
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EM-12 ![]() |
EM-59 ![]() |
EM-66 | EM-79 | EM-80 | EM-86 | EM-99 | EM-121 | EM-132 |
EM-146 ![]() |
EM-321 ![]() |
EM-333 ![]() |
EM-414 |
EM-672 ![]() |
EM-686 ![]() |
EM-845 | EM-846 | EM-847 | EM-884 | EM-892 |
EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. |
spatially distributed (in at least some cases) ?Comment:Spatial grain type is census block group. |
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) ?Comment:Computations performed at the area size of 0.08 ha. |
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 lumped (in all cases) | spatially lumped (in all cases) |
spatially distributed (in at least some cases) ?Comment:User defined scale, from plot to basin size. |
spatially distributed (in at least some cases) |
Spatial Grain Type
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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) | Not applicable | Not applicable | Not applicable | area, for pixel or radial feature | volume, for 3-D feature |
Spatial Grain Size
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varies | irregular | 20 m x 20 m | 20 m x 20 m | 20 m x 20 m | Distributed across catchments with average size of 65,000 ha | 1 km x 1 km | 1 km x 1 km | Distributed by land cover and soil type polygons | Not applicable | 250 m x 250 m | varies | 0.07 m^2 to 0.70 m^2 | variable stream lengths | beach length | Not applicable | Not applicable | Not applicable | user defined | user defined |
EM ID
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EM-12 ![]() |
EM-59 ![]() |
EM-66 | EM-79 | EM-80 | EM-86 | EM-99 | EM-121 | EM-132 |
EM-146 ![]() |
EM-321 ![]() |
EM-333 ![]() |
EM-414 |
EM-672 ![]() |
EM-686 ![]() |
EM-845 | EM-846 | EM-847 | EM-884 | EM-892 |
EM Computational Approach
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Numeric | Numeric | Analytic | Analytic | Analytic | Analytic | Logic- or rule-based | Logic- or rule-based | Analytic | Numeric | Analytic | Numeric | Analytic | Not applicable | Numeric | Analytic | Analytic | Logic- or rule-based | Numeric | Analytic |
EM Determinism
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stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | Not applicable | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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Comment:Agent based modeling results in response indices. |
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EM ID
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EM-12 ![]() |
EM-59 ![]() |
EM-66 | EM-79 | EM-80 | EM-86 | EM-99 | EM-121 | EM-132 |
EM-146 ![]() |
EM-321 ![]() |
EM-333 ![]() |
EM-414 |
EM-672 ![]() |
EM-686 ![]() |
EM-845 | EM-846 | EM-847 | EM-884 | EM-892 |
Model Calibration Reported?
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Unclear | Unclear | No | No | No | No | No | No | No | No | No | Unclear | Yes | Not applicable | Yes | Yes | Yes | No | Not applicable | Not applicable |
Model Goodness of Fit Reported?
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No | No | Yes | Yes | No | No | No | No | No | No | No | No | Yes | Not applicable | No | No | No | No | Not applicable | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None |
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None | None | None | None | None | None | None | None |
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None | None | None | None | None | None | None |
Model Operational Validation Reported?
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No | No | Yes | No | No | No | Yes | Yes | No | Yes | No | No | Yes | Not applicable | No | No | No | No | Not applicable | Not applicable |
Model Uncertainty Analysis Reported?
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No | No | No | No | No | No | No | No | No | No | No | No | No | Not applicable | No | No | No | No | Not applicable | Not applicable |
Model Sensitivity Analysis Reported?
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No ?Comment:Sensitivity analysis performed for agent values only. |
No | No | No | No | No | No | No | No | No | No | No | No | Not applicable | No | Yes | Yes | Yes | Not applicable | Not applicable |
Model Sensitivity Analysis Include Interactions?
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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 | Unclear | Not applicable | Unclear | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-12 ![]() |
EM-59 ![]() |
EM-66 | EM-79 | EM-80 | EM-86 | EM-99 | EM-121 | EM-132 |
EM-146 ![]() |
EM-321 ![]() |
EM-333 ![]() |
EM-414 |
EM-672 ![]() |
EM-686 ![]() |
EM-845 | EM-846 | EM-847 | EM-884 | EM-892 |
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None |
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None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-12 ![]() |
EM-59 ![]() |
EM-66 | EM-79 | EM-80 | EM-86 | EM-99 | EM-121 | EM-132 |
EM-146 ![]() |
EM-321 ![]() |
EM-333 ![]() |
EM-414 |
EM-672 ![]() |
EM-686 ![]() |
EM-845 | EM-846 | EM-847 | EM-884 | EM-892 |
None | None | None | None | None | None | None | None | None | None | None | None | None |
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None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-12 ![]() |
EM-59 ![]() |
EM-66 | EM-79 | EM-80 | EM-86 | EM-99 | EM-121 | EM-132 |
EM-146 ![]() |
EM-321 ![]() |
EM-333 ![]() |
EM-414 |
EM-672 ![]() |
EM-686 ![]() |
EM-845 | EM-846 | EM-847 | EM-884 | EM-892 |
Centroid Latitude
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44.11 | 35.99 | 45.05 | 45.05 | 45.05 | -30 | 50.53 | 50.53 | 42.8 | 44.13 | 38.3 | 44.11 | 46.72 | 41.78 | 41.62 | 36.23 | 36.23 | 36.23 | Not applicable | Not applicable |
Centroid Longitude
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-123.09 | -78.96 | 6.4 | 6.4 | 6.4 | 25 | 7.6 | 7.6 | 24 | -122.5 | -7.7 | -123.09 | -96.13 | -72.17 | -70.42 | -81.9 | -81.9 | -81.9 | Not applicable | Not applicable |
Centroid Datum
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WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | Not applicable |
Centroid Coordinates Status
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Estimated | Estimated | Provided | Provided | Provided | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-59 ![]() |
EM-66 | EM-79 | EM-80 | EM-86 | EM-99 | EM-121 | EM-132 |
EM-146 ![]() |
EM-321 ![]() |
EM-333 ![]() |
EM-414 |
EM-672 ![]() |
EM-686 ![]() |
EM-845 | EM-846 | EM-847 | EM-884 | EM-892 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Created Greenspace | Atmosphere | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Lakes and Ponds | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Forests | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Scrubland/Shrubland | Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Rivers and Streams | Near Coastal Marine and Estuarine | Grasslands | Grasslands | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Agricultural-urban interface at river junction | Urban and vicinity | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Not reported | Not applicable | Not applicable | Mountainous flood-prone region | Primarily conifer forest | Silvo-pastoral system | Agricultural-urban interface at river junction | Freshwater estuarine system | Coastal streams | Beaches | grasslands | grasslands | grasslands | Terrestrial | Terrestrial environment |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is coarser 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 corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-12 ![]() |
EM-59 ![]() |
EM-66 | EM-79 | EM-80 | EM-86 | EM-99 | EM-121 | EM-132 |
EM-146 ![]() |
EM-321 ![]() |
EM-333 ![]() |
EM-414 |
EM-672 ![]() |
EM-686 ![]() |
EM-845 | EM-846 | EM-847 | EM-884 | EM-892 |
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Community | Community | Community | Not applicable | Not applicable | Not applicable | Not applicable | Species | Not applicable | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Species | Species | Species | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-12 ![]() |
EM-59 ![]() |
EM-66 | EM-79 | EM-80 | EM-86 | EM-99 | EM-121 | EM-132 |
EM-146 ![]() |
EM-321 ![]() |
EM-333 ![]() |
EM-414 |
EM-672 ![]() |
EM-686 ![]() |
EM-845 | EM-846 | EM-847 | EM-884 | EM-892 |
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None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available |
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None Available |
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None Available |
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None Available |
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None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-12 ![]() |
EM-59 ![]() |
EM-66 | EM-79 | EM-80 | EM-86 | EM-99 | EM-121 | EM-132 |
EM-146 ![]() |
EM-321 ![]() |
EM-333 ![]() |
EM-414 |
EM-672 ![]() |
EM-686 ![]() |
EM-845 | EM-846 | EM-847 | EM-884 | EM-892 |
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None | None |
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None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-12 ![]() |
EM-59 ![]() |
EM-66 | EM-79 | EM-80 | EM-86 | EM-99 | EM-121 | EM-132 |
EM-146 ![]() |
EM-321 ![]() |
EM-333 ![]() |
EM-414 |
EM-672 ![]() |
EM-686 ![]() |
EM-845 | EM-846 | EM-847 | EM-884 | EM-892 |
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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 |