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-80 | EM-92 |
EM-98 ![]() |
EM-111 ![]() |
EM-120 |
EM-146 ![]() |
EM-196 | EM-320 |
EM-338 ![]() |
EM-345 ![]() |
EM-437 | EM-650 | EM-659 | EM-684 | EM-698 | EM-831 | EM-838 | EM-841 | EM-849 | EM-1002 | EM-1007 |
EM Short Name
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Agronomic ES and plant traits, Central French Alps | Runoff potential of pesticides, Europe | PATCH, western USA | InVEST water yield, Hood Canal, WA, USA | Landscape importance for habitat diversity, Europe | FORCLIM v2.9, Transect in Western OR, USA | N removal by wetlands, Contiguous USA | Coastal protection, Europe | InVEST crop pollination, California, USA | InVEST habitat quality, Puli Township, Taiwan | InVESTv3.0 Water yield, Guánica Bay, Puerto Rico | Sedge Wren density, CREP, Iowa, USA | LUCI, New Zealand | Beach visitation, Barnstable, MA, USA | Fish species richness, St. Croix, USVI | Northern bobwhite abundance, Piedmont region, USA | Eastern meadowlark abundance, Piedmont region, USA | Brown-headed cowbird abundance, Piedmont, USA | InVEST Coastal Vulnerability | WASP method | N-SPECT land-sea planning submodel |
EM Full Name
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Agronomic ecosystem service estimated from plant functional traits, Central French Alps | Runoff potential of pesticides, Europe | PATCH (Program to Assist in Tracking Critical Habitat), western USA | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) water yield, Hood Canal, WA, USA | Landscape importance for habitat diversity, Europe | FORCLIM (FORests in a changing CLIMate) v2.9, Western OR, USA | Nitrogen removal by wetlands as a function of loading, Contiguous USA | Coastal protection, Europe | InVEST crop pollination, California, USA | InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) habitat quality, Puli Township, Taiwan | InVEST (Integrated Valuation of Environmental Services and Tradeoffs) v3.0 Water yield, Guánica Bay, Puerto Rico, USA | Sedge Wren population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | LUCI (Land Utilisation and Capability Indicator), New Zealand | Beach visitation, Barnstable, Massachusetts, USA | Fish Species Richness, Buck Island, St. Croix , USVI | Northern bobwhite abundance, Piedmont ecoregion, USA | Eastern meadowlark abundance, Piedmont ecoregion, USA | Brown-headed cowbird abundance, Piedmont ecoregion, USA | InVEST Coastal Vulnerability | Water Quality Analysis Simulation Program Model method | A technical guide to the integrated land-sea planning toolkit |
EM Source or Collection
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EU Biodiversity Action 5 | None | US EPA | InVEST | EU Biodiversity Action 5 | US EPA | US EPA | EU Biodiversity Action 5 | InVEST | InVEST | US EPA | InVEST | None | None | US EPA | None | None | None | None | InVEST | None | None |
EM Source Document ID
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260 | 254 | 2 | 205 | 228 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
63 | 296 | 279 | 308 | 338 | 372 |
380 ?Comment:Document 381 is an additional source for this EM. |
386 | 355 | 405 | 405 | 405 | 408 | 472 | 473 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Schriever, C. A., and Liess, M. | Carroll, C, Phillips, M. K. , Lopez-Gonzales, C. A and Schumaker, N. H. | Toft, J. E., Burke, J. L., Carey, M. P., Kim, C. K., Marsik, M., Sutherland, D. A., Arkema, K. K., Guerry, A. D., Levin, P. S., Minello, T. J., Plummer, M., Ruckelshaus, M. H., and Townsend, H. M. | Haines-Young, R., Potschin, M. and Kienast, F. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Jordan, S., Stoffer, J. and Nestlerode, J. | Liquete, C., Zulian, G., Delgado, I., Stips, A., and Maes, J. | Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | Wu, C.-F., Lin, Y.-P., Chiang, L.-C. and Huang, T. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | 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 | Trodahl, M. I., B. M. Jackson, J. R. Deslippe, and A. K. Metherell | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Riffel, S., Scognamillo, D., and L. W. Burger | Riffel, S., Scognamillo, D., and L. W. Burger | Riffel, S., Scognamillo, D., and L. W. Burger | The Natural Capital Project.org | Environmental Protection Agency | Crist, P., Madden, K., Varley, I., Eslinger, D., Walker, D., Anderson, A., Morehead, S. and Dunton, K., |
Document Year
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2011 | 2007 | 2006 | 2013 | 2012 | 2007 | 2011 | 2013 | 2009 | 2014 | 2017 | 2010 | 2017 | 2018 | 2007 | 2008 | 2008 | 2008 | None | 2024 | 2009 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Mapping ecological risk of agricultural pesticide runoff | Defining recovery goals and strategies for endangered species: The wolf as a case study | From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Wetlands as sinks for reactive nitrogen at continental and global scales: A meta-analysis | Assessment of coastal protection as an ecosystem service in Europe | Modelling pollination services across agricultural landscapes | Assessing highway's impacts on landscape patterns and ecosystem services: A case study in Puli Township, Taiwan | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Investigating trade-offs between water quality and agricultural productivity using the Land Utilisation and Capability Indicator (LUCI)-A New Zealand application | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | 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 | InVEST Coastal Vulnerability | Water Quality Assessment Simulation Program | Integrated Land-Sea Planning: A Technical Guide to the Integrated Land-Sea Planning Toolkit. |
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 |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Website users guide | Published EPA report | Published report |
EM ID
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EM-80 | EM-92 |
EM-98 ![]() |
EM-111 ![]() |
EM-120 |
EM-146 ![]() |
EM-196 | EM-320 |
EM-338 ![]() |
EM-345 ![]() |
EM-437 | EM-650 | EM-659 | EM-684 | EM-698 | EM-831 | EM-838 | EM-841 | EM-849 | EM-1002 | EM-1007 |
Not applicable | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable | Not applicable | http://www.naturalcapitalproject.org/models/crop_pollination.html | https://www.naturalcapitalproject.org/invest/ | http://www.naturalcapitalproject.org/invest/ | Not applicable |
info@lucitools.org ?Comment:To obtain LUCI, email us your enquiry at info@lucitools.org with information about: The problem you are seeking to solve or your research question. The country and region you wish to apply LUCI in. What data you have with as much detail as possible about the data sources. Your timeframe or deadlines. |
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://naturalcapitalproject.stanford.edu/software/invest | https://www.epa.gov/hydrowq/wasp8-download | https://repositories.lib.utexas.edu/bitstreams/3dee92a8-9373-4bcc-be25-eda74e81fabf/download | |
Contact Name
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Sandra Lavorel | Carola Alexandra Schriever | Carlos Carroll | J.E. Toft | Marion Potschin | Richard T. Busing | Steve Jordan | Camino Liquete | Eric Lonsdorf |
Yu-Pin Lin ?Comment:Tel.: +886 2 3366 3467; fax: +866 2 2368 6980 |
Susan H. Yee | David Otis | Martha I. Trodahl | Kate K, Mulvaney | Simon Pittman | Sam Riffell | Sam Riffell | Sam Riffell | Not applicable | Environmental Protection Agency |
Patrick Crist ?Comment:No contact information provided |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Helmholtz Centre for Environmental Research - UFZ, Department of System Ecotoxicology, Permoserstrasse 15, 04318 Leipzig, Germany | Klamath Center for Conservation Research, Orleans, CA 95556 | The Natural Capital Project, Stanford University, 371 Serra Mall, Stanford, CA 94305-5020, USA | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | Gulf Ecology Division U.S. Environmental Protection Agency, 1 Sabine Island Drive, Gulf Breeze, Florida 32561 | European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, I-21027 Ispra, VA, Italy | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | Not reported | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | School of Geography, Environment & Earth Sciences, Victoria University of Wellington, New Zealand | Not reported | 1305 East-West Highway, Silver Spring, MD 20910, 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 | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Not applicable | 1200 Pennsylvania Avenue, NW Washington, DC 20460 | None provided |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | carola.schriever@ufz.de | carlos@cklamathconservation.org | jetoft@stanford.edu | marion.potschin@nottingham.ac.uk | rtbusing@aol.com | steve.jordan@epa.gov | camino.liquete@gmail.com | ericlonsdorf@lpzoo.org | yplin@ntu.edu.tw | yee.susan@epa.gov | dotis@iastate.edu | Not reported | Mulvaney.Kate@EPA.gov | simon.pittman@noaa.gov | sriffell@cfr.msstate.edu | sriffell@cfr.msstate.edu | sriffell@cfr.msstate.edu | Not applicable | Google email group | patrick@planitfwd.com |
EM ID
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EM-80 | EM-92 |
EM-98 ![]() |
EM-111 ![]() |
EM-120 |
EM-146 ![]() |
EM-196 | EM-320 |
EM-338 ![]() |
EM-345 ![]() |
EM-437 | EM-650 | EM-659 | EM-684 | EM-698 | EM-831 | EM-838 | EM-841 | EM-849 | EM-1002 | EM-1007 |
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." 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: "The approach is based on the runoff potential (RP) of stream sites, by a spatially explicit calculation based on pesticide use, precipitation, topography, land use and soil characteristics in the near-stream environment. The underlying simplified model complies with the limited availability and resolution of data at larger scales." AUTHOR'S DESCRIPTION: "The RP is based on a mathematical model that describes runoff losses of a compound with generalized properties and which was developed from a proposal by the Organisation for Economic Co-operation and Development (OECD) for estimating dissolved runoff inputs of a pesticide into surface waters (OECD, 1998)...The runoff model underlying RP calculates the dissolved amount of a generic substance that was applied in the near environment of a stream site and that is expected to reach the stream site during one rainfall event. The dissolved amount results from a single application in the near-stream environment (i.e., a two-sided 100-m stream corridor extending for 1500 m upstream of the site) and is the amount of applied substance in the designated corridor reduced due to the influence of the site-specific key environmental factors precipitation, soil characteristics, topography, and plant interception." | **Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** AUTHORS' DESCRIPTION: "PATCH (program to assist in tracking critical habitat), the SEPM used here, is designed for studying territorial vertebrates. It links the survival and fecundity of individual animals to geographic information system (GIS) data on mortality risk and habitat productivity at the scale of an individual or pack territory. Territories are allocated by intersecting the GIS data with an array of hexagonal cells. The different habitat types in the GIS maps are assigned weights based on the relative levels of fecundity and survival expected in those habitat classes. Base survival and reproductive rates, derived from published field studies, are then supplied to the model as a population projection matrix. The model scales these base matrix values using the mean of the habitat weights within each hexagon, with lower means translating into lower survival rates or reproductive output. Each individual in the population is tracked through a yearly cycle of survival, fecundity, and dispersal events. Environmental stochasticity is incorporated by drawing each year’s base population matrix from a randomized set of matrices whose elements were drawn from a beta (survival) or normal (fecundity) distribution. Adult organisms are classified as either territorial or floaters. The movement of territorial individuals is governed by a parameter for site fidelity, but floaters must always search for available breeding sites. As pack size increases, pack members in the model have a greater tendency to disperse and search for new available breeding sites. Movement decisions use a directed random walk that combines varying proportions of randomness, correlation, and attraction to higher-quality habitat (Schumaker 1998)." | InVEST Water Yield and Scarcity Model 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. AUTHOR'S DESCRIPTION: "We modelled discharge and total nitrogen for the 153 perennial sub- watersheds in Hood Canal based on spatial variation in hydrological factors, land and water use, and vegetation.To do this, we reparame-terized a set of fresh water models available in the InVEST tool (Tallis and Polasky, 2009; Kareiva et al., 2011)… We modelled discharge using the InVESTWater Yield and Scarcity model. The model estimates discharge for user-defined subwatersheds based on the average annual precipitation, annual reference evapotranspiration, and a correction factor for vegetation type, soil depth, plant available water content, land use and land cover, root depth, elevation, saturated hydraulic conductivity, and consumptive water use" (2) | 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 … “Habitat diversity” … 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: "The analysis for the regulating service "Habitat Diversity" seeks to identify all the areas with potential to support biodiversity." | 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 compiled published data from wetland studies worldwide to estimate total Nr removal and to evaluate factors that influence removal rates. Over several orders of magnitude in wetland area and Nr loading rates, there is a positive, near-linear relationship between Nr removal and Nr loading. The linear model (null hypothesis) explains the data better than either a model of declining Nr removal efficiency with increasing Nr loading, or a Michaelis–Menten (saturation) model." | ABSTRACT: "Mapping and assessment of ecosystem services is essential to provide scientific support to global and EU biodiversity policy. Coastal protection has been mostly analysed in the frame of coastal vulnerability studies or in local, habitat-specific assessments. This paper provides a conceptual and methodological approach to assess coastal protection as an ecosystem service at different spatial–temporal scales, and applies it to the entire EU coastal zone. The assessment of coastal protection incorporates 14 biophysical and socio-economic variables from both terrestrial and marine datasets. Those variables define three indicators: coastal protection capacity, coastal exposure and human demand for protection. A questionnaire filled by coastal researchers helped assign ranks to categorical parameters and weights to the individual variables. The three indicators are then framed into the ecosystem services cascade model to estimate how coastal ecosystems provide protection, in particular describing the service function, flow and benefit. The results are comparative and aim to support integrated land and marine spatial planning. The main drivers of change for the provision of coastal protection come from the widespread anthropogenic pressures in the European coastal zone, for which a short quantitative analysis is provided." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "Background and Aims: Crop pollination by bees and other animals is an essential ecosystem service. Ensuring the maintenance of the service requires a full understanding of the contributions of landscape elements to pollinator populations and crop pollination. Here, the first quantitative model that predicts pollinator abundance on a landscape is described and tested. Methods: Using information on pollinator nesting resources, floral resources and foraging distances, the model predicts the relative abundance of pollinators within nesting habitats. From these nesting areas, it then predicts relative abundances of pollinators on the farms requiring pollination services. Model outputs are compared with data from coffee in Costa Rica, watermelon and sunflower in California and watermelon in New Jersey–Pennsylvania (NJPA). Key Results: Results from Costa Rica and California, comparing field estimates of pollinator abundance, richness or services with model estimates, are encouraging, explaining up to 80 % of variance among farms. However, the model did not predict observed pollinator abundances on NJPA, so continued model improvement and testing are necessary. The inability of the model to predict pollinator abundances in the NJPA landscape may be due to not accounting for fine-scale floral and nesting resources within the landscapes surrounding farms, rather than the logic of our model. Conclusions: The importance of fine-scale resources for pollinator service delivery was supported by sensitivity analyses indicating that the model's predictions depend largely on estimates of nesting and floral resources within crops. Despite the need for more research at the finer-scale, the approach fills an important gap by providing quantitative and mechanistic model from which to evaluate policy decisions and develop land-use plans that promote pollination conservation and service delivery. " | 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: "...To assess the effects of different land-use scenarios under various agricultural and environmental conservation policy regimes, this study applies an integrated approach to analyze the effects of Highway 6 construction on Puli Township...A habitat quality assessment using the InVEST model indicates that the conservation of agricultural and forested lands improves habitat quality and preserves rare habitats…" AUTHOR'S DESCRIPTION: "In total, three land-use planning scenarios were simulated based on government policies in Taiwan’s Hillside Protection Act and Regulations on Non-Urban Land Utilization Control. The baseline planning scenario, Scenario A, allows land-use development with-out land-use controls (Appendix Fig. S2), meaning that land-use changes can occur anywhere. Scenario B is based on the Regulations on Non-Urban Land Utilization Control and the maintenance of agricultural areas, such that land-use changes cannot occur in agricultural areas. Scenario C protects agricultural land, hillsides, and naturally forested areas from development...The biodiversity evaluation module in the InVEST model assessed the degree of change in habitat quality and habitat rarity under three scenarios. In the InVEST model, habitat quality is primarily threatened by four factors: the relative impact of each threat; the relative sensitivity of each habitat type to each threat; the distance between habitats and sources of threats; as well as the relative degree to which land is legally protected..." Use of other models in conjunction with this model: Land use data for future scenarios modeled in InVEST were derived from a linear regression model of land use change, and the CLUE-S (Conversion of Land Use and its Effects at Small regional extent) model for apportioning those changes to the landscape. | 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. AUTHOR'S DESCRIPTION: "Stakeholders identified an objective of meeting water demands for agriculture and domestic purposes, including irrigation, drinking water, or hydropower production…Geomorphology, climate, and vegetation determine the amount of water runoff from the landscape that could be available for consumptive uses. Long-term average water yield was estimated for each HUC12 sub-watershed as the difference between total precipitation and the amount absorbed by the different land cover classes using a reservoir hydropower production model (InVEST 3.0.0; Tallis et al., 2013)." | 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 Sedge Wren (Cistothorus platensis)... 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: SEWR density = 1-1/1+e^(-0.8015652 + 0.08500569 * grass400) *e^(-0.7982511 + 0.0285891 * bbspath + 0.0105094 *grass400) | ABSTRACT: "...The Land Utilisation & Capability Indicator (LUCI) is a GIS framework that considers impacts of land use on multiple ecosystem services in a holistic and spatially explicit manner. Due to its fine spatial scale and focus on the rural environment, LUCI is well-placed to help both farm and catchment managers to explore and quantify spatially explicit solutions to improve water quality while also maintaining or enhancing other ecosystem service outcomes. LUCI water quality and agricultural productivity models were applied to a catchment in the Bay of Plenty, New Zealand. Nitrogen (N) and phosphorus (P) sources, sinks and pathways in the landscape were identified and trade-offs and synergies between water quality and agricultural productivity were investigated. Results indicate that interventions to improve water quality are likely to come at the expense of agriculturally productive land. Nonetheless, loss of agriculturally productive land can be minimised by using LUCI to identify, at a fine spatial scale, the most appropriate locations for nutrient intervention. Spatially targeted and strategic nutrient source management and pathway interception can improve water quality, while minimising negative financial impacts on farms. Our results provide spatially explicit solutions to optimize agricultural productivity and water quality, which will inform better farm, land and catchment management as well as national and international policy." AUTHOR'S DESCRIPTION (of OVERSEER submodel): "Water quality models within LUCI use an enhanced, spatially representative export co-efficient (EC) approach to model total nitrogen (TN) and total phosphorus (TP) exports to water… Here, ECs for pastoral land cover are calculated by LUCI using algorithms derived from a large ( > 14 000 samples), pastorally based national dataset. The dataset consists of detailed farm nutrient input and management variables that have been entered and run using OVERSEER® to generate nutrient loss predictions, which are also included in the dataset." NOTE: The LUCI model, is a second-generation extension and software implementation of the Polyscape framework, as described in EM-658. https://esml.epa.gov/detail/em/658 | ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "...We needed beach visitation estimates to assess the number of people who would be impacted by beach closures. We modeled visits by combining daily parking counts with other factors that help explain variations in attendance, including weather, day of the week or point within a season, and physical differences in sites (Kreitler et al. 2013). We designed the resulting model to estimate visitation for uncounted days as well as for beaches without counts on a given day. When combined with estimates of value per day, the visitation model can be used to value a lost beach day while accounting for beach size, time of season, and other factors...Since our count data of visitation for all four beaches are relatively large numbers (mean = 490, SD = 440), we used a log-linear regression model as opposed to a count data model. We selected a random effects model to account for time invariant variables such as parking spaces, modeling differences across beaches based on this variable…" Equation 2, page 15, provides the econometric regression. | ABSTRACT: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems." | ABSTRACT:"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. " | Faced with an intensification of human activities and a changing climate, coastal communities need to better understand how modifications of the biological and physical environment (i.e. direct and indirect removal of natural habitats for coastal development) can affect their exposure to storm-induced erosion and flooding (inundation). The InVEST Coastal Vulnerability model produces a qualitative estimate of such exposure in terms of a vulnerability index, which differentiates areas with relatively high or low exposure to erosion and inundation during storms. By coupling these results with global population information, the model can show areas along a given coastline where humans are most vulnerable to storm waves and surge. The model does not take into account coastal processes that are unique to a region, nor does it predict long- or short-term changes in shoreline position or configuration. Model inputs, which serve as proxies for various complex shoreline processes that influence exposure to erosion and inundation, include: a polyline with attributes about local coastal geomorphology along the shoreline, polygons representing the location of natural habitats (e.g., seagrass, kelp, wetlands, etc.), rates of (observed) net sea-level change, a depth contour that can be used as an indicator for surge level (the default contour is the edge of the continental shelf), a digital elevation model (DEM) representing the topography of the coastal area, a point shapefile containing values of observed storm wind speed and wave power, and a raster representing population distribution. Outputs can be used to better understand the relative contributions of these different model variables to coastal exposure and highlight the protective services offered by natural habitats to coastal populations. This information can help coastal managers, planners, landowners and other stakeholders identify regions of greater risk to coastal hazards, which can in turn better inform development strategies and permitting. The results provide a qualitative representation of coastal hazard risks rather than quantifying shoreline retreat or inundation limits. | Web description: " The Water Quality Analysis Simulation Program (WASP) is an enhancement of the original WASP (Di Toro et al., 1983; Connolly and Winfield, 1984; Ambrose, R.B. et al., 1988). This model helps users interpret and predict water quality responses to natural phenomena and manmade pollution for various pollution management decisions. WASP is a dynamic compartment-modeling program for aquatic systems, including both the water column and the underlying benthos. WASP allows the user to investigate 1, 2, and 3 dimensional systems, and a variety of pollutant types. The state variables for the given modules are given in the table below. The time varying processes of advection, dispersion, point and diffuse mass loading and boundary exchange are represented in the model. WASP also can be linked with hydrodynamic and sediment transport models that can provide flows, depths velocities, temperature, salinity and sediment fluxes. This release of WASP contains the inclusion of the sediment diagenesis model linked to the Advanced Eutrophication sub model, which predicted sediment oxygen demand and nutrient fluxes from the underlying sediments " | The Nonpoint-Source Pollution and Erosion Comparison Tool (N-SPECT) is a screening tool developed to help land use planners and mangers understand the potential impacts of land use change decisions on erosion and water quality. The tool runs as an extension within the ESRI ArcGIS software package. It utilizes digital elevation maps, soils and precipitation information from data sets that are available nationally. However, it also lets users take advantage of local higher resolution and/or more accurate data sets when available. For example, the N-SPECT pollution coefficients used are similar to those in the EPA’s BASINS suite of tools and provide a good starting point for quick comparisons between management scenarios, but the coefficients can still be easily customized as users develop more localized data. The real utility of N-SPECT does not lie in the user’s ability to examine the accuracy of any particular run’s results, but in the comparison of runs between different development (or restoration) scenarios. By allowing users to modify multiple land uses and providing the results of those changes in a GIS environment, N-SPECT enables managers to quickly understand the overall consequences of different land use scenarios. The primary role of N-SPECT in this toolkit is to predict sedimentation and pollution changes from different land use scenarios and identify areas that are key contributors of these inputs. |
Specific Policy or Decision Context Cited
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None identified | European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | AUTHOR DESCRIPTION: "Comprehensive habitat and viability assessments. . . [more rigoursly defined] can clarify debate of goals for recovery of large carnivores"; Endangered Species Act and related litigation | Land use change | None identified | None Identified | None identified | Supports global and EU biodiversity policy | None identified | Environmental effects of Highway 6 construction on Puli Township, Taiwan | Meeting water demands | None identified | Land management trade off between agricultural productivity and water quality | To assess the number of people who would be impacted by beach closures. | None provided | None reported | None reported | None reported | None identified | Not applicable | None provided |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Not applicable | Great Plains to Pacific Coast, northern Rocky Mountains, Pacific Northwest | Not additional description provided | No additional description provided | Coastal to montane | Estuarine Emergent; Agricultural; Salt Marsh; Palustrine Emergent; Palustrine Forested | No additional description provided | No additional description provided | 26% of the land area is categorized as plain and the remaining 74% is categorized as hilly with elevations of 380-700 m. Predominant land classes are forested (47.4%), cultivated (31.8%), and built-up (14.5%). Average annual rainfall is 2120 mm, and average annual temperature is 21°C. The soil in the eastern portion of the basin is primarily clay, and primarily loess elsewhere. | No additional description provided | Prairie pothole region of north-central Iowa | Groundwater dominated, volcanic caldera catchment, largely comprised of porous allophanic and pumice soils. | Four separate beaches within the community of Barnstable | Hard and soft benthic habitat types approximately to the 33m isobath | Conservation Reserve Program lands left to go fallow | Conservation Reserve Program lands left to go fallow | Conservation Reserve Program lands left to go fallow | Not applicable | segments of streams modeled | Not applicable |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | Population growth, road development (density) on public vs private land | Future land use and land cover; climate change | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Three scenarios; baseline planning (A, without land-use controls), scenario B based on maintenance of agriculture, scenario C protects agriculture, hillsides and naturally forested areas. | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | N/A | N/A | N/A | Options for future sea level change and population change | n/a | No scenarios presented |
EM ID
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EM-80 | EM-92 |
EM-98 ![]() |
EM-111 ![]() |
EM-120 |
EM-146 ![]() |
EM-196 | EM-320 |
EM-338 ![]() |
EM-345 ![]() |
EM-437 | EM-650 | EM-659 | EM-684 | EM-698 | EM-831 | EM-838 | EM-841 | EM-849 | EM-1002 | EM-1007 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | 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 ?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 | 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 + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method Only | Method Only | Method Only |
New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
Application of existing 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 |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
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EM-80 | EM-92 |
EM-98 ![]() |
EM-111 ![]() |
EM-120 |
EM-146 ![]() |
EM-196 | EM-320 |
EM-338 ![]() |
EM-345 ![]() |
EM-437 | EM-650 | EM-659 | EM-684 | EM-698 | EM-831 | EM-838 | EM-841 | EM-849 | EM-1002 | EM-1007 |
EM Temporal Extent
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Not reported | 2000 | 2000-2025 | 2005-7; 2035-45 | 2000 | 1500 yrs | 2004 | 1992-2010 | 2001-2002 | 2010-2025 | 2006 - 2012 | 1992-2007 | 1930-2013 | 2011 - 2016 | 2000-2005 | 2008 | 2008 | 2008 | Not applicable | Not applicable | Not applicable |
EM Time Dependence
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time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | 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-dependent | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | future time | future time | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable |
EM Time Continuity
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Not applicable | discrete | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
discrete ?Comment:Time frame is modeler dependent |
other or unclear (comment) |
EM Temporal Grain Size Value
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Not applicable | 1 | 1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Day | Year | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable |
EM ID
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EM-80 | EM-92 |
EM-98 ![]() |
EM-111 ![]() |
EM-120 |
EM-146 ![]() |
EM-196 | EM-320 |
EM-338 ![]() |
EM-345 ![]() |
EM-437 | EM-650 | EM-659 | EM-684 | EM-698 | EM-831 | EM-838 | EM-841 | EM-849 | EM-1002 | EM-1007 |
Bounding Type
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Physiographic or Ecological | Geopolitical | Physiographic or ecological | Watershed/Catchment/HUC | Geopolitical | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Geopolitical | Other | Geopolitical | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Not applicable | Not applicable | Not applicable |
Spatial Extent Name
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Central French Alps | EU-15 | Western United States | Hood Canal | The EU-25 plus Switzerland and Norway | Western Oregon transect | Contiguous U.S. | Shoreline of the European Union-27 | Agricultural landscape, Yolo County, Central Valley | Puli Township, Nantou County | Guanica Bay watershed | CREP (Conservation Reserve Enhancement Program) wetland sites | Lake Rotorua catchment | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | SW Puerto Rico, | Piedmont Ecoregion | Piedmont Ecoregion | Piedmont Ecoregion | Not applicable | Not applicable | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 1000-10,000 km^2. | 1-10 km^2 | 100-1000 km^2 | 10-100 ha | 100-1000 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 | Not applicable |
EM ID
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EM-80 | EM-92 |
EM-98 ![]() |
EM-111 ![]() |
EM-120 |
EM-146 ![]() |
EM-196 | EM-320 |
EM-338 ![]() |
EM-345 ![]() |
EM-437 | EM-650 | EM-659 | EM-684 | EM-698 | EM-831 | EM-838 | EM-841 | EM-849 | EM-1002 | EM-1007 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially lumped (in all cases) ?Comment:Computations performed at the area size of 0.08 ha. |
spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | other or unclear (comment) |
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 | area, for pixel or radial feature | Not applicable | 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 | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | Not applicable | Not applicable | Not applicable | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | Not applicable |
Spatial Grain Size
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20 m x 20 m | 10 km x 10 km | 504 km^2 | 30 m x 30 m | 1 km x 1 km | Not applicable | Not applicable | Irregular | 30 m x 30 m | 40 m x 40 m | 30 m x 30 m | multiple, individual, irregular shaped sites | 5m x 5m | by beach site | not reported | Not applicable | Not applicable | Not applicable | user defined | stream segment | Not applicable |
EM ID
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EM-80 | EM-92 |
EM-98 ![]() |
EM-111 ![]() |
EM-120 |
EM-146 ![]() |
EM-196 | EM-320 |
EM-338 ![]() |
EM-345 ![]() |
EM-437 | EM-650 | EM-659 | EM-684 | EM-698 | EM-831 | EM-838 | EM-841 | EM-849 | EM-1002 | EM-1007 |
EM Computational Approach
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Analytic | Analytic | Numeric | Analytic | Logic- or rule-based | Numeric | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Logic- or rule-based | Analytic | Analytic | Analytic | Numeric | Analytic |
EM Determinism
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deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-80 | EM-92 |
EM-98 ![]() |
EM-111 ![]() |
EM-120 |
EM-146 ![]() |
EM-196 | EM-320 |
EM-338 ![]() |
EM-345 ![]() |
EM-437 | EM-650 | EM-659 | EM-684 | EM-698 | EM-831 | EM-838 | EM-841 | EM-849 | EM-1002 | EM-1007 |
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | Unclear | Yes | No | No | Yes | No | Unclear | Unclear | No | Unclear | No | Yes | No | No | Yes | Yes | Not applicable | Unclear | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | No | No | No | No | Yes | No | No | Not applicable | No | No | No | No | Yes | No | No | No | Not applicable | Unclear | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None | None | None | None | None |
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None | None | None | None | None | None | None |
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None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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No | No | No | Yes | Yes | Yes | No | No |
Yes ?Comment:Performed just for "Total pollinator abundance service score". |
Not applicable | No | Unclear | No | No | Yes | No | No | No | Not applicable | Unclear | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | Yes | No | No | No | No | Yes | No | No | No | No | No | No | No | No | No | No | No | Not applicable | Unclear | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | Yes |
Yes ?Comment:No results reported. Just a general statement was made about PATCH sensitivity and that demographic parameters are more sensitive that variation in other parameters such as dispersadistance . Reference made to another publication Carroll et al. 2003. Use of population viability analysis and reserve slelection algorithms in regional conservation plans. Ecol. App. 13:1773-1789. |
Yes | No | No | Yes | No | No | No | No | No | No | Yes | Yes | Yes | Yes | Yes | Not applicable | Unclear | Not applicable |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | No | Unclear | No | Not applicable | Not applicable | Yes | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Unclear | Unclear | Unclear | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-80 | EM-92 |
EM-98 ![]() |
EM-111 ![]() |
EM-120 |
EM-146 ![]() |
EM-196 | EM-320 |
EM-338 ![]() |
EM-345 ![]() |
EM-437 | EM-650 | EM-659 | EM-684 | EM-698 | EM-831 | EM-838 | EM-841 | EM-849 | EM-1002 | EM-1007 |
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None |
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Comment:Taiwan |
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None |
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None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-80 | EM-92 |
EM-98 ![]() |
EM-111 ![]() |
EM-120 |
EM-146 ![]() |
EM-196 | EM-320 |
EM-338 ![]() |
EM-345 ![]() |
EM-437 | EM-650 | EM-659 | EM-684 | EM-698 | EM-831 | EM-838 | EM-841 | EM-849 | EM-1002 | EM-1007 |
None | None | None |
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None | None | None |
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None | None |
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None | None |
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None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-80 | EM-92 |
EM-98 ![]() |
EM-111 ![]() |
EM-120 |
EM-146 ![]() |
EM-196 | EM-320 |
EM-338 ![]() |
EM-345 ![]() |
EM-437 | EM-650 | EM-659 | EM-684 | EM-698 | EM-831 | EM-838 | EM-841 | EM-849 | EM-1002 | EM-1007 |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | 50.01 | 39.88 | 47.8 | 50.53 | 44.13 | -9999 | 48.2 | 38.7 | 23.98 | 17.96 | 42.62 | -38.14 | 41.64 | 17.79 | 36.23 | 36.23 | 36.23 | Not applicable | Not applicable | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
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6.4 | 4.67 | -113.81 | -122.7 | 7.6 | -122.5 | -9999 | 16.35 | -121.8 | 120.96 | -67.02 | -93.84 | 176.25 | -70.29 | -64.62 | -81.9 | -81.9 | -81.9 | Not applicable | Not applicable | Not applicable |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | Not applicable | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Estimated | Estimated | Estimated | Estimated | Provided | Not applicable | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-80 | EM-92 |
EM-98 ![]() |
EM-111 ![]() |
EM-120 |
EM-146 ![]() |
EM-196 | EM-320 |
EM-338 ![]() |
EM-345 ![]() |
EM-437 | EM-650 | EM-659 | EM-684 | EM-698 | EM-831 | EM-838 | EM-841 | EM-849 | EM-1002 | EM-1007 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Forests | Inland Wetlands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Rivers and Streams | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Inland Wetlands | Near Coastal Marine and Estuarine | Open Ocean and Seas | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Inland Wetlands | Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Ground Water | Forests | Agroecosystems | Scrubland/Shrubland | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Grasslands | Grasslands | Grasslands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Not applicable |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows. | Arable lands in near-stream environments | Not reported | glacier-carved saltwater fjord | Not applicable | Primarily conifer forest | Wetlands (multiple types) | Coastal zones | Cropland and surrounding landscape | Predominantly an agricultural area with associated forest land | 13 LULC were used | Grassland buffering inland wetlands set in agricultural land | Largely agricultural, commercial forestry, non-commercial forest and shrubland and urban | Saltwater beach | shallow coral reefs | grasslands | grasslands | grasslands | Coastal environments | Stream segment | None |
EM Ecological Scale
em.detail.ecoScaleHelp
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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 is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 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 | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-80 | EM-92 |
EM-98 ![]() |
EM-111 ![]() |
EM-120 |
EM-146 ![]() |
EM-196 | EM-320 |
EM-338 ![]() |
EM-345 ![]() |
EM-437 | EM-650 | EM-659 | EM-684 | EM-698 | EM-831 | EM-838 | EM-841 | EM-849 | EM-1002 | EM-1007 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Not applicable | Species | Not applicable | Not applicable | Species | Not applicable | Not applicable | Species | Community | Not applicable | Species | Not applicable | Not applicable | Guild or Assemblage | Species | Species | Species | Not applicable | Not applicable | Community |
Taxonomic level and name of organisms or groups identified
EM-80 | EM-92 |
EM-98 ![]() |
EM-111 ![]() |
EM-120 |
EM-146 ![]() |
EM-196 | EM-320 |
EM-338 ![]() |
EM-345 ![]() |
EM-437 | EM-650 | EM-659 | EM-684 | EM-698 | EM-831 | EM-838 | EM-841 | EM-849 | EM-1002 | EM-1007 |
None Available | None Available |
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None Available | None Available |
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None Available | None Available |
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None Available | None Available |
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None Available | None Available |
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None Available | None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-80 | EM-92 |
EM-98 ![]() |
EM-111 ![]() |
EM-120 |
EM-146 ![]() |
EM-196 | EM-320 |
EM-338 ![]() |
EM-345 ![]() |
EM-437 | EM-650 | EM-659 | EM-684 | EM-698 | EM-831 | EM-838 | EM-841 | EM-849 | EM-1002 | EM-1007 |
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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-80 | EM-92 |
EM-98 ![]() |
EM-111 ![]() |
EM-120 |
EM-146 ![]() |
EM-196 | EM-320 |
EM-338 ![]() |
EM-345 ![]() |
EM-437 | EM-650 | EM-659 | EM-684 | EM-698 | EM-831 | EM-838 | EM-841 | EM-849 | EM-1002 | EM-1007 |
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None |
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None | None |
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None |
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None |
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None |