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-70 | EM-71 | EM-131 | EM-148 | EM-184 | EM-195 | EM-317 | EM-333 | EM-338 | EM-359 | EM-374 | EM-415 | EM-450 | EM-456 | EM-458 | EM-652 | EM-668 | EM-862 | EM-863 |
EM Short Name
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Plant species diversity, Central French Alps | Community flowering date, Central French Alps | InVEST marine water quality, Hood Canal, WA, USA | InVEST - Water provision, Francoli River, Spain | ROS (Recreation Opportunity Spectrum), Europe | C Sequestration and De-N, Tampa Bay, FL, USA | ARIES carbon, Puget Sound Region, USA | Evoland v3.5 (unbounded growth), Eugene, OR, USA | InVEST crop pollination, California, USA | InVEST (v1.004) sediment retention, Indonesia | InVEST carbon storage and sequestration (v3.2.0) | Esocid spawning, St. Louis River, MN/WI, USA | Decrease in wave runup, St. Croix, USVI | Reef dive site favorability, St. Croix, USVI | Reef density of P. argus, St. Croix, USVI | Savannah Sparrow density, CREP, Iowa, USA | Fish nutrient cycling , Ohio, USA | Recreational fishery index, USA | SLAMM, Tampa Bay, FL, USA |
EM Full Name
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Plant species diversity, Central French Alps | Community weighted mean flowering date, Central French Alps | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) marine water quality, Hood Canal, WA, USA | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) v2.4.2 - Water provision, Francoli River, Spain | ROS (Recreation Opportunity Spectrum), Europe | Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA | ARIES (Artificial Intelligence for Ecosystem Services) Carbon Storage and Sequestration, Puget Sound Region, Washington, USA | Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA | InVEST crop pollination, California, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs v1.004) sediment retention, Sumatra, Indonesia | InVEST v3.2.0 Carbon storage and sequestration | Esocid spawning, St. Louis River estuary, MN & WI, USA | Decrease in wave runup (by reef), St. Croix, USVI | Dive site favorability (reef), St. Croix, USVI | Relative density of Panulirus argus (on reef), St. Croix, USVI | Savannah Sparrow population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Nutrient Cycling by gizzard shad, Ohio, USA | Recreational fishery index for streams and rivers, USA | SLAMM (sea level affecting marshes model), Tampa Bay, Florida, USA |
EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | InVEST | InVEST | EU Biodiversity Action 5 | US EPA | ARIES | Envision | InVEST | InVEST | InVEST | US EPA | US EPA | US EPA | US EPA | None | None | US EPA | None |
EM Source Document ID
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260 | 260 | 205 | 280 | 293 | 186 | 302 |
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
279 | 309 | 315 | 332 | 335 | 335 | 335 | 372 | 385 | 414 |
415 ?Comment:Secondary sources: Documents 412 and 413. |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | 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. | Marques, M., Bangash, R.F., Kumar, V., Sharp, R., and Schuhmacher, M. | Paracchini, M.L., Zulian, G., Kopperoinen, L., Maes, J., Schägner, J.P., Termansen, M., Zandersen, M., Perez-Soba, M., Scholefield, P.A., and Bidoglio, G. | Russell, M. and Greening, H. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | Bhagabati, N. K., Ricketts, T., Sulistyawan, T. B. S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A., Tallis, H., and Wolney, S. | The Natural Capital Project | Ted R. Angradi, David W. Bolgrien, Jonathon J. Launspach, Brent J. Bellinger, Matthew A. Starry, Joel C. Hoffman, Mike E. Sierszen, Anett S. Trebitz, and Tom P. Hollenhorst | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | 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 | Vanni, M.J., Bowling, A.M., Dickman, E.M., Hale, R.S., Higgins, K.A., Horgan, M.J., Knoll, L.B., Renwick, W.H., and R.A. Stein | Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold | Sherwood, E. T. and H. S. Greening |
Document Year
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2011 | 2011 | 2013 | 2013 | 2014 | 2013 | 2014 | 2008 | 2009 | 2014 | 2015 | 2016 | 2014 | 2014 | 2014 | 2010 | 2006 | 2021 | 2014 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA | The impact of climate change on water provision under a low flow regime: A case study of the ecosystems services in the Francoli river basin | Mapping cultural ecosystem services: A framework to assess the potential for outdoor recreation across the EU | Estimating benefits in a recovering estuary: Tampa Bay, Florida | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Modelling pollination services across agricultural landscapes | Ecosystem services reinforce Sumatran tiger conservation in land use plans | Carbon storage and sequestration - InVEST (v3.2.0) | Mapping ecosystem service indicators of a Great Lakes estuarine Area of Concern | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Nutrient cycling by fish supports relatively more primary production as lake productivity increases | Correspondence between a recreational fishery index and ecological condition for U.S.A. streams and rivers. | Potential impacts and management implications of climate change on Tampa Bay estuary critical coastal habitats |
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 |
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 | Website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-70 | EM-71 | EM-131 | EM-148 | EM-184 | EM-195 | EM-317 | EM-333 | EM-338 | EM-359 | EM-374 | EM-415 | EM-450 | EM-456 | EM-458 | EM-652 | EM-668 | EM-862 | EM-863 |
Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | http://aries.integratedmodelling.org/ | http://evoland.bioe.orst.edu/ | http://www.naturalcapitalproject.org/models/crop_pollination.html | https://www.naturalcapitalproject.org/invest/ | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://warrenpinnacle.com/prof/SLAMM/index.html com/prof/SLAMM/index.html | |
Contact Name
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Sandra Lavorel | Sandra Lavorel | J.E. Toft | Montse Marquès | Maria Luisa Paracchini | M. Russell | Ken Bagstad | Michael R. Guzy | Eric Lonsdorf | Nirmal K. Bhagabati | The Natural Capital Project | Ted R. Angradi | Susan H. Yee | Susan H. Yee | Susan H. Yee | David Otis | Michael Vanni | Gregg Lomnicky | Edward T. Sherwood |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Not reported | Environmental Analysis and Management Group, Department d'Enginyeria Qimica, Universitat Rovira I Virgili, Tarragona, Catalonia, Spain | Joint Research Centre, Institute for Environment and Sustainability, Via E.Fermi, 2749, I-21027 Ispra (VA), Italy | US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA | Geosciences and Environmental Change Science Center, US Geological Survey | Oregon State University, Dept. of Biological and Ecological Engineering | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | 371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | United States Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboraty, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804 USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Dept of Environmental toxocology, C.emson Univ. Pendleton, South Carolina 29670, USA | 200 SW 35th St., Corvallis, OR, 97333 | Tampa Bay Estuary Program, 263 13th Avenue South, St. Petersburg, FL 33701, USA |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | jetoft@stanford.edu | montserrat.marques@fundacio.urv.cat | luisa.paracchini@jrc.ec.europa.eu | Russell.Marc@epamail.epa.gov | kjbagstad@usgs.gov | Not reported | ericlonsdorf@lpzoo.org | nirmal.bhagabati@wwfus.org | invest@naturalcapitalproject.org | angradi.theodore@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | dotis@iastate.edu | vannimj@muohio.edu | lomnicky.gregg@epa.gov | esherwood@tbep.org |
EM ID
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EM-70 | EM-71 | EM-131 | EM-148 | EM-184 | EM-195 | EM-317 | EM-333 | EM-338 | EM-359 | EM-374 | EM-415 | EM-450 | EM-456 | EM-458 | EM-652 | EM-668 | EM-862 | EM-863 |
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: "Simpson species diversity was modelled using the LU + abiotic [land use and all abiotic variables] model given that functional diversity should be a consequence of species diversity rather than the reverse (Lepsˇ et al. 2006)…Species diversity for each pixel was calculated and mapped using model estimates for effects of land use types, and for regression coefficients on abiotic variables. For each pixel these calculations were applied to mapped estimates of 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: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | Marine Water Quality 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 used outputs from the freshwater models as inputs to the marine water quality model.We adapted a box model that has been successfully applied in Puget Sound (Babson et al., 2006; Sutherland et al., 2011) to simulate seasonal and interannual variations in salinity, water temperature, and nitrates in the Canal." (p. 4) | 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: "InVEST 2.4.2 model runs as script tool in the ArcGIS 10 ArcTool-Box on a gridded map at an annual average time step, and its results can be reported in either biophysical or monetary terms, depending on the needs and the availability of information. It is most effectively used within a decision making process that starts with a series of stakeholder consultations to identify questions and services of interest to policy makers, communities, and various interest groups. These questions may concern current service delivery and how services may be affected by new programmes, policies, and conditions in the future. For questions regarding the future, stakeholders develop scenarios of management interventions or natural changes to explore the consequences of potential changes on natural resources [21]. This tool informs managers and policy makers about the impacts of alternative resource management choices on the economy, human well-being, and the environment, in an integrated way [22]. The spatial resolution of analyses is flexible, allowing users to address questions at the local, regional or global scales. | ABSTRACT: "Research on ecosystem services mapping and valuing has increased significantly in recent years. However, compared to provisioning and regulating services, cultural ecosystem services have not yet beenfully integrated into operational frameworks. One reason for this is that transdisciplinarity is required toaddress the issue, since by definition cultural services (encompassing physical, intellectual, spiritual inter-actions with biota) need to be analysed from multiple perspectives (i.e. ecological, social, behavioural).A second reason is the lack of data for large-scale assessments, as detailed surveys are a main sourceof information. Among cultural ecosystem services, assessment of outdoor recreation can be based ona large pool of literature developed mostly in social and medical science, and landscape and ecologystudies. This paper presents a methodology to include recreation in the conceptual framework for EUwide ecosystem assessments (Maes et al., 2013), which couples existing approaches for recreation man-agement at country level with behavioural data derived from surveys and population distribution data.The proposed framework is based on three components: the ecosystem function (recreation potential),the adaptation of the Recreation Opportunity Spectrum framework to characterise the ecosystem serviceand the distribution of potential demand in the EU." | AUTHOR'S DESCRIPTION: "...we examine the change in the production of ecosystem goods produced as a result of restoration efforts and potential relative cost savings for the Tampa Bay community from seagrass expansion (more than 3,100 ha) and coastal marsh and mangrove restoration (∼600 ha), since 1990… The objectives of this article are to explore the roles that ecological processes and resulting ecosystem goods have in maintaining healthy estuarine systems by (1) quantifying the production of specific ecosystem goods in a subtropical estuarine system and (2) determining potential cost savings of improved water quality and increased habitat in a recovering estuary." (pp. 2) | ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "We quantified carbon sequestration and storage in vegetation and soils using Bayesian models (Bagstad et al. 2011) calibrated with Moderate-resolution Imaging Spectroradiometer Net Primary Productivity (MODIS GPP/NPP Project, http://secure.ntsg.umt. edu/projects/index.php/ID/ca2901a0/fuseaction/prohttp://www.whrc.org/ational Bwww.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_053627)vey Geographic Dahttp://www.geomac.gov/index.shtml)wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_053627) soils data, respectively. By overlaying fire boundary polygons from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov/index.shtml) we estimated carbon storage losses caused by wildfire, using fuel consumption coefficients from Spracklen et al. (2009) and carbon pool data from Smith et al. (2006). By incorporating the impacts of land-cover change from urbanization (Bolte and Vache 2010) within carbon models, we quantified resultant changes in carbon storage." | **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." | 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: "...Here we use simple spatial analyses on readily available datasets to compare the distribution of five ecosystem services with tiger habitat in central Sumatra. We assessed services and habitat in 2008 and the changes in these variables under two future scenarios: a conservation-friendly Green Vision, and a Spatial Plan developed by the Indonesian government..." AUTHOR'S DESCRIPTION: "We used a modeling tool, InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004; Tallis et al., 2010), to map and quantify tiger habitat quality and five ecosystem services. InVEST maps ecosystem services and the quality of species habitat as production functions of LULC using simple biophysical models. Models were parameterized using data from regional agencies, literature surveys, global databases, site visits and prior field experience (Table 1)... The sediment retention model is based on the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978). It estimates erosion as ton y^-1 of sediment load, based on the energetic ability of rainfall to move soil, the erodibility of a given soil type, slope, erosion protection provided by vegetated LULC, and land management practices. The model routes sediment originating on each land parcel along its flow path, with vegetated parcels retaining a fraction of sediment with varying efficiencies, and exporting the remainder downstream. ...Although InVEST reports ecosystem services in biophysical units, its simple models are best suited to understanding broad patterns of spatial variation (Tallis and Polasky, 2011), rather than for precise quantification. Additionally, we lacked field measurements against which to calibrate our outputs. Therefore, we focused on relative spatial distribution across the landscape, and relative change to scenarios." | Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. ABSTRACT: "Terrestrial ecosystems, which store more carbon than the atmosphere, are vital to influencing carbon dioxide-driven climate change. The InVEST model uses maps of land use and land cover types and data on wood harvest rates, harvested product degradation rates, and stocks in four carbon pools (aboveground biomass, belowground biomass, soil, dead organic matter) to estimate the amount of carbon currently stored in a landscape or the amount of carbon sequestered over time. Additional data on the market or social value of sequestered carbon and its annual rate of change, and a discount rate can be used in an optional model that estimates the value of this environmental service to society. Limitations of the model include an oversimplified carbon cycle, an assumed linear change in carbon sequestration over time, and potentially inaccurate discounting rates." AUTHOR'S DESCRIPTION: "A fifth optional pool included in the model applies to parcels that produce harvested wood products (HWPs) such as firewood or charcoal or more long-lived products such as house timbers or furniture. Tracking carbon in this pool is useful because it represents the amount of carbon kept from the atmosphere by a given product." | ABSTRACT: "Estuaries provide multiple ecosystem services from which humans benefit…We described an approach, with examples, for assessing how local-scale actions affect the extent and distribution of coastal ecosystem services, using the St. Louis River estuary (SLRE) of western Lake Superior as a case study. We based our approach on simple models applied to spatially explicity biophysical data that allows us to map the providing area of ecosystem services at high resolution (10-m^2 pixel) across aquatic and riparian habitats…Aspects of our approach can be adapted by communities for use in support of local decision-making." AUTHOR'S DESCRIPTION: "We derived the decision criteria used to map the IEGS habitat proxy of esocid spawning from habitat suitability information for two species that have similar but not identical spawning habitat and behavior." | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion, storm damage, or coastal inundation during extreme events...Wave run-up, R, can be estimated as R = H(tan α/(√H/Ho) where H is the wave height nearshore, Ho is the deep water wave height, and α is the angle of the beach slope. R may be corrected by a multiplier depending on the porosity of the shoreline surface...The contribution of each grid cell to reduction in wave run-up would depend on its contribution to wave height attenuation (Eq. (S3))." | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…In lieu of surveys of diver opinion, recreational opportunities can also be estimated by actual field data of coral condition at preferred dive sites. A few studies have directly examined links between coral condition and production of recreational opportunities through field monitoring in an attempt to validate perceptions of recreational quality (Pendleton, 1994; Williams and Polunin, 2002; Leeworthy et al., 2004; Leujakand Ormond, 2007; Uyarraetal., 2009). Uyarraetal. (2009) used surveys to determine reef attributes related to diver perceptions of most and least favorite dive sites. Field data was used to narrow down the suite of potential preferred attributes to those that reflected actual site condition. We combined these attributes to form an index of dive site favorability: Dive site favorability = ΣipiRi where pi is the proportion of respondents indicating each attribute i that affected dive enjoyment positively. Ri is the mean relative magnitude of measured variables used to quantify each descriptive attribute i, including ‘fish abundance’ (pi=0.803), quantified by number of fish schools and fish species richness, and | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j: (1) density of the spiny lobster Panulirus argus" | 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 Savannah Sparrow (Passerculus sandwichensis)... 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: SASP density = e^(-1.581362 + 0.0229603 *bbspath + 0.01024* grass3200 + 0.0255867 * hay3200) | ABSTRACT: "Animals can be important in nutrient cycling in particular ecosystems, but few studies have examined how this importance varies along environmental gradients. In this study we quantified the nutrient cycling role of an abundant detritivorous fish species, the gizzard shad (Dorosoma cepedianum), in reservoir ecosystems along a gradient of ecosystem productivity. Gizzard shad feed mostly on sediment detritus and excrete sediment-derived nutrients into the water column, thereby mediating a cross-habitat translocation of nutrients to phytoplankton. We quantified nitrogen and phosphorus cycling (excretion) rates of gizzard shad, as well as nutrient demand by phytoplankton, in seven lakes over a four-year period (16 lake-years). The lakes span a gradient of watershed land use (the relative amounts of land used for agriculture vs. forest) and productivity. As the watersheds of these lakes became increasingly dominated by agricultural land, primary production rates, lake trophic state indicators (total phosphorus and chlorophyll concentrations), and nutrient flux through gizzard shad populations all increased. Nutrient cycling by gizzard shad supported a substantial proportion of primary production in these ecosystems, and this proportion increased as watershed agriculture (and ecosystem productivity) increased. In the four productive lakes with agricultural watersheds (.78% agricultural land), gizzard shad supported on average 51% of phytoplankton primary production (range 27–67%). In contrast, in the three relatively unproductive lakes in forested or mixed-land-use watersheds (.47% forest, ,52% agricultural land), gizzard shad supported 18% of primary production (range 14–23%). Thus, along a gradient of forested to agricultural landscapes, both watershed nutrient inputs and nutrient translocation by gizzard shad increase, but our data indicate that the importance of nutrient translocation by gizzard shad increases more rapidly. Our results therefore support the hypothesis that watersheds and gizzard shad jointly regulate primary production in reservoir ecosystems " | ABSTRACT: [Sport fishing is an important recreational and economic activity, especially in Australia, Europe and North America, and the condition of sport fish populations is a key ecological indicator of water body condition for millions of anglers and the public. Despite its importance as an ecological indicator representing the status of sport fish populations, an index for measuring this ecosystem service has not been quantified by analyzing actual fish taxa, size and abundance data across the U.S.A. Therefore, we used game fish data collected from 1,561 stream and river sites located throughout the conterminous U.S.A. combined with specific fish species and size dollar weights to calculate site-specific recreational fishery index (RFI) scores. We then regressed those scores against 38 potential site-specific environmental predictor variables, as well as site-specific fish assemblage condition (multimetric index; MMI) scores based on entire fish assemblages, to determine the factors most associated with the RFI scores. We found weak correlations between RFI and MMI scores and weak to moderate correlations with environmental variables, which varied in importance with each of 9 ecoregions. We conclude that the RFI is a useful indicator of a stream ecosystem service, which should be of greater interest to the U.S.A. public and traditional fishery management agencies than are MMIs, which tend to be more useful for ecologists, environmentalists and environmental quality agencies.] | ABSTRACT: "The Tampa Bay estuary is a unique and valued ecosystem that currently thrives between subtropical and temperate climates along Florida’s west-central coast. The watershed is considered urbanized (42 % lands developed); however, a suite of critical coastal habitats still persists. Current management efforts are focused toward restoring the historic balance of these habitat types to a benchmark 1950s period. We have modeled the anticipated changes to a suite of habitats within the Tampa Bay estuary using the sea level affecting marshes model (SLAMM) under various sea level rise (SLR) scenarios. Modeled changes to the distribution and coverage of mangrove habitats within the estuary are expected to dominate the overall proportions of future critical coastal habitats. Modeled losses in salt marsh, salt barren, and coastal freshwater wetlands by 2100 will significantly affect the progress achieved in ‘‘Restoring the Balance’’ of these habitat types over recent periods…" |
Specific Policy or Decision Context Cited
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None identified | None identified | Land use change | None identified | None identified | Restoration of seagrass | 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 | This analysis provided input to government-led spatial planning and strategic environmental assessments in the study area. This region contains some of the last remaining forest habitat of the critically endangered Sumatran tiger, Panthera tigris sumatrae. | None identified | Federal delisting of an area of concern (AOC) | None identified | None identified | None identified | None identified | None identified | None identified | None identified |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | No additional description provided | Mediteranean coastal mountains | No additional description provided | Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove | No additional description provided | No additional description provided | No additional description provided | Six watersheds in central Sumatra covering portions of Riau, Jambi and West Sumatra provinces. The Barisan mountain range comprises the western edge of the watersheds, while peat swamps predominate in the east. | Not applicable | No additional description provided | No additional description provided | No additional description provided | No additional description provided | Prairie pothole region of north-central Iowa | Lakes | None | No additional description provided |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | future land use and land cover; Climate change | IPPC scenarios A2- severe changes in temperature and precipitation, B1 - more moderate variations in temperature and precipitation schemes from the present | No scenarios presented | Habitat loss or restoration in Tampa Bay Estuary | No scenarios presented | Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves. | No scenarios presented | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | Optional future scenarios for changed LULC and wood harvest | The effect of habitat restoration on esocid spawning area was simulated by varying biophysical changes. | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Lake productivity | N/A | Varying sea level rise (baseline - 2m), and two habitat adaption strategies |
EM ID
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EM-70 | EM-71 | EM-131 | EM-148 | EM-184 | EM-195 | EM-317 | EM-333 | EM-338 | EM-359 | EM-374 | EM-415 | EM-450 | EM-456 | EM-458 | EM-652 | EM-668 | EM-862 | EM-863 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application (multiple runs exist) | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs |
New or Pre-existing EM?
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New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
New or revised model | New or revised model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-70 | EM-71 | EM-131 | EM-148 | EM-184 | EM-195 | EM-317 | EM-333 | EM-338 | EM-359 | EM-374 | EM-415 | EM-450 | EM-456 | EM-458 | EM-652 | EM-668 | EM-862 | EM-863 |
Document ID for related EM
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Doc-260 | Doc-260 | Doc-269 | None | Doc-307 | Doc-311 | Doc-338 | Doc-205 | Doc-290 | Doc-291 | Doc-289 | None | Doc-303 | Doc-305 |
Doc-183 | Doc-47 | Doc-313 | Doc-314 ?Comment:Doc 183 is a secondary source for the Evoland model. |
Doc-279 | Doc-338 | Doc-309 | None | Doc-335 | None | None | Doc-372 | None | None | Doc-412 | Doc-413 |
EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | EM-344 | EM-368 | EM-437 | EM-111 | None | None | None | EM-12 | EM-369 | EM-340 | EM-339 | EM-435 | EM-349 | None | EM-447 | None | None | EM-648 | EM-649 | EM-650 | EM-651 | None | None | EM-857 |
EM Modeling Approach
EM ID
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EM-70 | EM-71 | EM-131 | EM-148 | EM-184 | EM-195 | EM-317 | EM-333 | EM-338 | EM-359 | EM-374 | EM-415 | EM-450 | EM-456 | EM-458 | EM-652 | EM-668 | EM-862 | EM-863 |
EM Temporal Extent
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2007-2009 | 2007-2008 | varies by run, see runs for values | 1971-2100 | Not reported | 1982-2010 | 1950-2007 | 1990-2050 | 2001-2002 | 2008-2020 | Not applicable | 2013 | 2006-2007, 2010 | 2006-2007, 2010 | 2006-2007, 2010 | 1992-2007 | 2000-2003 | 2013-2014 | 2002-2100 |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 2 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable |
EM ID
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EM-70 | EM-71 | EM-131 | EM-148 | EM-184 | EM-195 | EM-317 | EM-333 | EM-338 | EM-359 | EM-374 | EM-415 | EM-450 | EM-456 | EM-458 | EM-652 | EM-668 | EM-862 | EM-863 |
Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Physiographic or ecological | Watershed/Catchment/HUC | Geopolitical | Physiographic or Ecological | Physiographic or ecological | Geopolitical | Other | Watershed/Catchment/HUC | Not applicable | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Geopolitical | Watershed/Catchment/HUC |
Spatial Extent Name
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Central French Alps | Central French Alps | Hood Canal | Francoli River | European Union countries | Tampa Bay Estuary | Puget Sound Region | Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Agricultural landscape, Yolo County, Central Valley | central Sumatra | Not applicable | St. Louis River estuary | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | CREP (Conservation Reserve Enhancement Program) wetland sites | Lakes in Ohio | United States | Tampa Bay estuary watershed |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 100-1000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. | 10,000-100,000 km^2 | 10-100 km^2 | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | Not applicable | 10-100 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 1-10 km^2 | 100,000-1,000,000 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. |
EM ID
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EM-70 | EM-71 | EM-131 | EM-148 | EM-184 | EM-195 | EM-317 | EM-333 | EM-338 | EM-359 | EM-374 | EM-415 | EM-450 | EM-456 | EM-458 | EM-652 | EM-668 | EM-862 | EM-863 |
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 distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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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 | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | 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) | Not applicable | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature |
Spatial Grain Size
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20 m x 20 m | 20 m x 20 m | Not reported | 30m x 30m | 100 m x 100 m | 1 ha | 200m x 200m | varies | 30 m x 30 m | 30 m x 30 m | application specific | 10 m x 10 m | 10 m x 10 m | 10 m x 10 m | 10 m x 10 m | multiple, individual, irregular shaped sites | Not applicable | stream reach (site) | 10 x 10 m |
EM ID
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EM-70 | EM-71 | EM-131 | EM-148 | EM-184 | EM-195 | EM-317 | EM-333 | EM-338 | EM-359 | EM-374 | EM-415 | EM-450 | EM-456 | EM-458 | EM-652 | EM-668 | EM-862 | EM-863 |
EM Computational Approach
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Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | stochastic | 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-70 | EM-71 | EM-131 | EM-148 | EM-184 | EM-195 | EM-317 | EM-333 | EM-338 | EM-359 | EM-374 | EM-415 | EM-450 | EM-456 | EM-458 | EM-652 | EM-668 | EM-862 | EM-863 |
Model Calibration Reported?
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No | No | No | No | No | Yes | Yes | Unclear | Unclear | No | Not applicable | No | Yes | Yes | Yes | Unclear |
Yes ?Comment:Nitrogen and Phosphorus excretion rates were calibrated by lake and fish size class. |
No | No |
Model Goodness of Fit Reported?
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Yes | Yes | No | No | No | No | No | No | No | No | Not applicable | No | No | No | No | No | No | No | No |
Goodness of Fit (metric| value | unit)
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None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
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No | No | No |
Yes ?Comment:Used Nash-Sutcliffe model efficiency index |
No | No | No | No |
Yes ?Comment:Performed just for "Total pollinator abundance service score". |
No | Not applicable | No | Yes | Yes | Yes | Unclear | No | No | No |
Model Uncertainty Analysis Reported?
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No | No | No | No | No | No | No | No | No | No | Not applicable | No | No | No | No | No | No | No | No |
Model Sensitivity Analysis Reported?
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No | No | No | No | No | No | No | No | No | No | Not applicable | No | No | No | No | No | No | No | No |
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 | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-70 | EM-71 | EM-131 | EM-148 | EM-184 | EM-195 | EM-317 | EM-333 | EM-338 | EM-359 | EM-374 | EM-415 | EM-450 | EM-456 | EM-458 | EM-652 | EM-668 | EM-862 | EM-863 |
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None | None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-70 | EM-71 | EM-131 | EM-148 | EM-184 | EM-195 | EM-317 | EM-333 | EM-338 | EM-359 | EM-374 | EM-415 | EM-450 | EM-456 | EM-458 | EM-652 | EM-668 | EM-862 | EM-863 |
None | None |
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None | None |
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None | None | None | None | None | None |
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None | None | None |
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Centroid Lat/Long (Decimal Degree)
EM ID
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EM-70 | EM-71 | EM-131 | EM-148 | EM-184 | EM-195 | EM-317 | EM-333 | EM-338 | EM-359 | EM-374 | EM-415 | EM-450 | EM-456 | EM-458 | EM-652 | EM-668 | EM-862 | EM-863 |
Centroid Latitude
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45.05 | 45.05 | 47.8 | 41.26 | 48.2 | 27.95 | 48 | 44.11 | 38.7 | 0 | -9999 | 46.74 | 17.73 | 17.73 | 17.73 | 42.62 | 40.15 | 36.21 | 27.76 |
Centroid Longitude
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6.4 | 6.4 | -122.7 | 1.18 | 16.35 | -82.47 | -123 | -123.09 | -121.8 | 102 | -9999 | -92.14 | -64.77 | -64.77 | -64.77 | -93.84 | -82.95 | -113.76 | -82.54 |
Centroid Datum
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WGS84 | WGS84 | NAD83 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Provided | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Not applicable | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated |
EM ID
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EM-70 | EM-71 | EM-131 | EM-148 | EM-184 | EM-195 | EM-317 | EM-333 | EM-338 | EM-359 | EM-374 | EM-415 | EM-450 | EM-456 | EM-458 | EM-652 | EM-668 | EM-862 | EM-863 |
EM Environmental Sub-Class
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Forests | Atmosphere | Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Not applicable | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Inland Wetlands | Agroecosystems | Grasslands | Lakes and Ponds | Rivers and Streams | Inland Wetlands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | glacier-carver saltwater fjord | Coastal mountains | Not applicable | Subtropical Estuary | Terrestrial environment surrounding a large estuary | Agricultural-urban interface at river junction | Cropland and surrounding landscape | 104 land use land cover classes | Terrestrial environments, but not specified for methods | freshwater estuary | Coral reefs | Coral reefs | Coral reefs | Grassland buffering inland wetlands set in agricultural land | Reservoirs | reach | Esturary and associated urban and terrestrial environment |
EM Ecological Scale
em.detail.ecoScaleHelp
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Not applicable | Not applicable | 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 | Not applicable | 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 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-70 | EM-71 | EM-131 | EM-148 | EM-184 | EM-195 | EM-317 | EM-333 | EM-338 | EM-359 | EM-374 | EM-415 | EM-450 | EM-456 | EM-458 | EM-652 | EM-668 | EM-862 | EM-863 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Community | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Species | Community | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Species | Species | Not applicable | Guild or Assemblage | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-70 | EM-71 | EM-131 | EM-148 | EM-184 | EM-195 | EM-317 | EM-333 | EM-338 | EM-359 | EM-374 | EM-415 | EM-450 | EM-456 | EM-458 | EM-652 | EM-668 | EM-862 | EM-863 |
None Available | None Available | None Available | None Available | None Available | None Available | None Available |
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None Available | None Available |
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None Available | None Available |
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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-70 | EM-71 | EM-131 | EM-148 | EM-184 | EM-195 | EM-317 | EM-333 | EM-338 | EM-359 | EM-374 | EM-415 | EM-450 | EM-456 | EM-458 | EM-652 | EM-668 | EM-862 | EM-863 |
None | 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-70 | EM-71 | EM-131 | EM-148 | EM-184 | EM-195 | EM-317 | EM-333 | EM-338 | EM-359 | EM-374 | EM-415 | EM-450 | EM-456 | EM-458 | EM-652 | EM-668 | EM-862 | EM-863 |
None | None |
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None |
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None | None |
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None |
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