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-79 | EM-113 | EM-132 | EM-184 | EM-303 |
EM-349 ![]() |
EM-375 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-430 | EM-454 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-838 | EM-890 | EM-943 | EM-1006 | EM-1009 |
EM-1054 ![]() |
EM Short Name
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Divergence in flowering date, Central French Alps | Wetland conservation for birds, Midwestern USA | Flood regulation capacity, Etropole, Bulgaria | ROS (Recreation Opportunity Spectrum), Europe | Biological pest control, Uppland Province, Sweden | InVEST (v1.004) Carbon, Indonesia | VELMA hydro, Oregon, USA | Wetland shellfish production, Gulf of Mexico, USA | ARIES viewsheds, Puget Sound Region, USA | HexSim v2.4, San Joaquin kit fox, CA, USA | Carbon sequestration, Guánica Bay, Puerto Rico | Reef snorkeling opportunity, St. Croix, USVI | RUM: Valuing fishing quality, Michigan, USA | Alewife derived nutrients, Connecticut, USA | WESP Method | Eastern meadowlark abundance, Piedmont region, USA | HWB Blood pressure, Great Lakes waterfront, USA | Visitation to natural areas, New England, USA | Vista land-sea planning submodel | ETDOT carbon contaminant removal | Aquatox, chemical pollutant model, China |
EM Full Name
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Functional divergence in flowering date, Central French Alps | Prioritizing wetland conservation for birds, Midwestern USA | Flood regulation capacity of landscapes, Municipality of Etropole, Bulgaria | ROS (Recreation Opportunity Spectrum), Europe | Biological control of agricultural pests by natural predators, Uppland Province, Sweden | InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004) carbon storage and sequestration, Sumatra, Indonesia | VELMA (visualizing ecosystems for land management assessments) hydro, Oregon, USA | Wetland shellfish production, Gulf of Mexico, USA | ARIES (Artificial Intelligence for Ecosystem Services) Scenic viewsheds for homeowners, Puget Sound Region, Washington, USA | HexSim v2.4, San Joaquin kit fox rodenticide exposure, California, USA | Carbon sequestration, Guánica Bay, Puerto Rico, USA | Relative snorkeling opportunity (in reef), St. Croix, USVI | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | Alewife derived nutrients in stream food web, Connecticut, USA | Method for the Wetland Ecosystem Services Protocol (WESP) | Eastern meadowlark abundance, Piedmont ecoregion, USA | Human well being indicator- Blood pressure, Great Lakes waterfront, USA | Estimating natural area use with cell phone data, Narragansett Beach, New England, USA | A technical guide to the integrated land-sea planning toolkit | nvironmental Technologies Design Option Tool for contaminant removal | Plankton response to sudden polychlorinated biphenyls pollution on a Reservoir of North China based on AQUATOX model |
EM Source or Collection
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EU Biodiversity Action 5 | None | EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | InVEST | US EPA |
US EPA ?Comment:Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science |
ARIES | US EPA | US EPA | US EPA | None | None | None | None | None | US EPA | None | US EPA | None |
EM Source Document ID
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260 | 122 | 248 | 293 | 299 | 309 | 13 | 324 | 302 |
337 ?Comment:The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships. |
338 | 335 |
382 ?Comment:Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010. |
384 | 390 | 405 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
436 | 473 | 475 | 501 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Thogmartin, W. A., Potter, B. A. and Soulliere, G. J. | Nedkov, S., Burkhard, B. | 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. | Jonsson, M., Bommarco, R., Ekbom, B., Smith, H.G., Bengtsson, J., Caballero-Lopez, B., Winqvist, C., and Olsson, O. | 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. | Abdelnour, A., Stieglitz, M., Pan, F. and McKane, R. B. | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Nogeire, T. M., J. J. Lawler, N. H. Schumaker, B. L. Cypher, and S. E. Phillips | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | Walters, A. W., R. T. Barnes, and D. M. Post | Adamus, P. R. | Riffel, S., Scognamillo, D., and L. W. Burger | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | Merrill, N.H., Atkinson, S.F., Mulvaney, K.K., Mazzotta, K.K., and J. Bousquin | Crist, P., Madden, K., Varley, I., Eslinger, D., Walker, D., Anderson, A., Morehead, S. and Dunton, K., | National Center for Clean Industrial and Treatment Technologies at Michigan Technological University (MTU) | Yan, J., Li, H., Liu, C., Liu, C. and Xing, T. |
Document Year
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2011 | 2011 | 2012 | 2014 | 2014 | 2014 | 2011 | 2012 | 2014 | 2015 | 2017 | 2014 | 2014 | 2009 | 2016 | 2008 | None | 2020 | 2009 | 2019 | 2025 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Bridging the conservation design and delivery gap for wetland bird habitat maintenance and restoration in the midwestern United States | Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria | Mapping cultural ecosystem services: A framework to assess the potential for outdoor recreation across the EU | Ecological production functions for biological control services in agricultural landscapes | Ecosystem services reinforce Sumatran tiger conservation in land use plans | Catchment hydrological responses to forest harvest amount and spatial pattern | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Land use as a driver of patterns of rodenticide exposure in modeled kit fox populations | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Valuing recreational fishing quality at rivers and streams | Anadromous alewives (Alosa pseudoharengus) contribute marine-derived nutrients to coastal stream food webs | Manual for the Wetland Ecosystem Services Protocol (WESP) v. 1.3. | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | Using data derived from cellular phone locations to estimate visitation to natural areas: An application to water recreation in New England, USA | Integrated Land-Sea Planning: A Technical Guide to the Integrated Land-Sea Planning Toolkit. | Environmental Technologies Design Option Tool | Plankton response to sudden polychlorinated biphenyls pollution on a Reservoir of North China based on AQUATOX model |
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 but unpublished (explain in Comment) | 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 journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Journal manuscript submitted or in review | Published journal manuscript | Published report | Published report | Published journal manuscript |
EM ID
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EM-79 | EM-113 | EM-132 | EM-184 | EM-303 |
EM-349 ![]() |
EM-375 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-430 | EM-454 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-838 | EM-890 | EM-943 | EM-1006 | EM-1009 |
EM-1054 ![]() |
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable | http://aries.integratedmodelling.org/ | http://www.hexsim.net/ | Not applicable | Not applicable | Not applicable | Not applicable |
http://people.oregonstate.edu/~adamusp/WESP/ ?Comment:This is an Excel spreadsheet calculator |
Not applicable | Not applicable | https://github.com/USEPA/Recreation_Benefits.git | https://repositories.lib.utexas.edu/bitstreams/3dee92a8-9373-4bcc-be25-eda74e81fabf/download | https://github.com/USEPA/Environmental-Technologies-Design-Option-Tool | https://www.epa.gov/hydrowq/aquatox-32-download-page | |
Contact Name
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Sandra Lavorel | Wayne Thogmartin, USGS | Stoyan Nedkov | Maria Luisa Paracchini | Mattias Jonsson | Nirmal K. Bhagabati | A. Abdelnour | Stephen J. Jordan | Ken Bagstad | Theresa M. Nogeire | Susan H. Yee | Susan H. Yee | Richard Melstrom | Annika W. Walters | Paul R. Adamus | Sam Riffell | Ted Angradi | Nathaniel Merrill |
Patrick Crist ?Comment:No contact information provided |
David Hokanson | Jinxia Yan |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Upper Midwest Environmental Sciences Center, 2630 Fanta Reed Road, La Crosse, WI 54603 | National Institute of Geophysics, Geodesy and Geography, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, bl.3, 1113 Sofia, Bulgaria | Joint Research Centre, Institute for Environment and Sustainability, Via E.Fermi, 2749, I-21027 Ispra (VA), Italy | Department of Ecology, Swedish University of Agricultural Sciences, PO Box 7044, SE-750 07 Uppsala, Sweden | The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018 | Dept. of Civil and Environmental Engineering, Goergia Institute of Technology, Atlanta, GA 30332-0335, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA | Geosciences and Environmental Change Science Center, US Geological Survey | School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | Dept. of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511, USA | 6028 NW Burgundy Dr. Corvallis, OR 97330 | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | Atlantic Coastal Environmental Sciences Division, U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Narragansett, Rhode Island, United States of America, | None provided | 224 N. Fair Oaks Ave., Floor 2 Pasadena, CA. 91103 | Henan Key Laboratory of Water Environment Simulation and Treatment, School of Environmental and Municipal Engineering, North China University of Water Resources and Electric Power, 450046 Zhengzhou, China |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | wthogmartin@usgs.gov | snedkov@abv.bg | luisa.paracchini@jrc.ec.europa.eu | mattias.jonsson@slu.se | nirmal.bhagabati@wwfus.org | abdelnouralex@gmail.com | jordan.steve@epa.gov | kjbagstad@usgs.gov | tnogeire@gmail.com | yee.susan@epa.gov | yee.susan@epa.gov | melstrom@okstate.edu | annika.walters@yale.edu | adamus7@comcast.net | sriffell@cfr.msstate.edu | tedangradi@gmail.com | merrill.nathaniel@epa.gov | patrick@planitfwd.com | administrator@trusselltech.com | yanjinxia@ncwu.edu.cn |
EM ID
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EM-79 | EM-113 | EM-132 | EM-184 | EM-303 |
EM-349 ![]() |
EM-375 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-430 | EM-454 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-838 | EM-890 | EM-943 | EM-1006 | EM-1009 |
EM-1054 ![]() |
Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Functional divergence of flowering date was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | ABSTRACT: "The U.S. Fish and Wildlife Service’s adoption of Strategic Habitat Conservation is intended to increase the effectiveness and efficiency of conservation delivery by targeting effort in areas where biological benefits are greatest. Conservation funding has not often been allocated in accordance with explicit biological endpoints, and the gap between conservation design (the identification of conservation priority areas) and delivery needs to be bridged to better meet conservation goals for multiple species and landscapes. We introduce a regional prioritization scheme for North American Wetlands Conservation Act funding which explicitly addresses Midwest regional goals for wetland-dependent birds. We developed decision-support maps to guide conservation of breeding and non-breeding wetland bird habitat. This exercise suggested ~55% of the Midwest consists of potential wetland bird habitat, and areas suited for maintenance (protection) were distinguished from those most suited to restoration. Areas with greater maintenance focus were identified for central Minnesota, southeastern Wisconsin, the Upper Mississippi and Illinois rivers, and the shore of western Lake Erie and Saginaw Bay. The shores of Lakes Michigan and Superior accommodated fewer waterbird species overall, but were also important for wetland bird habitat maintenance. Abundant areas suited for wetland restoration occurred in agricultural regions of central Illinois, western Iowa, and northern Indiana and Ohio. Use of this prioritization scheme can increase effectiveness, efficiency, transparency, and credibility to land and water conservation efforts for wetland birds in the Midwestern United States." | ABSTRACT: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. In this study, the capacities of different ecosystems to regulate floods were assessed through investigations of water retention functions of the vegetation and soil cover. Based on spatial land cover units originating from CORINE and further data sets, these regulating ecosystem services were quantified and mapped. Resulting maps show the ecosystems’ flood regulating service capacities in the case study area of the Malki Iskar river basin above the town of Etropole in the northern part of Bulgaria...The resulting map of flood regulation supply capacities shows that the Etropole municipality’s area has relatively high capacities for flood regulation. Areas of high and very high relevant capacities cover about 34% of the study area." AUTHOR'S DESCRIPTION: "The capacities of the identified spatial units were assessed on a relative scale ranging from 0 to 5 (after Burkhard et al., 2009). A 0-value indicates that there is no relevant capacity to supply flood regulating services and a 5-value indicates the highest relevant capacity for the supply of these services in the case study region. Values of 2, 3 and 4 represent respective intermediate supply capacities. Of course it depends on the observer’s estimation and knowledge which function–service relations in general are supposed to be relevant. But, this scale offers an alternative relative evaluation scheme, avoiding the presentation of monetary or normative value-transfer results. The 0–5 capacity values’ classifications for the different land cover types were based on the spatial analyses of different biogeophysical and land use data combined with hydrological modeling as described before…The supply capacities of the land cover classes and soil types in the study area were assigned to every unit in their databases. GIS map layers, containing information about the capacity to supply flood regulation for every polygon, were created. The map of supply capacities of flood regulating ecosystem services was elaborated by overlaying the GIS map layers of the land cover and the soils’ capacities." | ABSTRACT: "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." | ABSTRACT: "We develop a novel, mechanistic landscape model for biological control of cereal aphids, explicitly accounting for the influence of landscape composition on natural enemies varying in mobility, feeding rates and other life history traits. Finally, we use the model to map biological control services across cereal fields in a Swedish agricultural region with varying landscape complexity. The model predicted that biological control would reduce crop damage by 45–70% and that the biological control effect would be higher in complex landscapes. In a validation with independent data, the model performed well and predicted a significant proportion of biological control variation in cereal fields. However, much variability remains to be explained, and we propose that the model could be improved by refining the mechanistic understanding of predator dynamics and accounting for variation in aphid colonization." | 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)... We mapped biomass carbon by assigning carbon values (in ton ha_1) for aboveground, belowground, and dead organic matter to each LULC class based on values from literature, as described in Tallis et al. (2010). We mapped soil carbon separately, as large quantities of carbon are stored in peat soil (Page et al., 2011). We estimated total losses in peat carbon over 50 years into the future scenarios, using reported annual emission rates for specific LULC transitions on peat (Uryu et al., 2008)...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." | AUTHOR'S DESCRIPTION: "VELMA uses a distributed soil column framework to simulate the movement of water and nutrients (NH4, NO3, DON, DOC) within the soil, between the soil and the vegetation, and between the soil surface and vegetation to the atmosphere. The soil column model consists of three coupled submodels: (1) a hydrological model that simulates vertical and lateral movement of water within soil, losses of water from soil and vegetation to the atmosphere, and the growth and ablation of the seasonal snowpack, (2) a soil temperature model that simulates daily soil layer temperatures from surface air temperature and snow depth, and (3) a plant-soil model that simulates C and N dynamics. (Note: for the purposes of this paper we describe only the hydrologic aspects of the model.) Each soil column consists of n soil layers. Soil water balance is solved for each layer (equations (A1)–(A6)). We employ a simple logistic function that is based on the degree of saturation to capture the breakthrough characteristics of soil water drainage (equations (A7)–(A9)). Evapotranspiration increases exponentially with increasing soil water storage and asymptotically approaches the potential evapotranspiration rate (PET) as water storage reaches saturation [Davies and Allen, 1973; Federer, 1979, 1982; Spittlehouse and Black, 1981] (equation (A12)). PET is estimated using a simple temperature-based method [Hamon, 1963] (equation (A13)). An evapotranspiration recovery function is used to account for the effects of changes in stand-level transpiration rates during succession, e.g., after fire or harvest (equation (B2)). Snowmelt is estimated using the degree-day approach [Rango and Martinec, 1995] and accounts for the effects of rain on snow [Harr, 1981] (equation (A10)). [15] The soil column model is placed within a catchment framework to create a spatially distributed model applicable to watersheds and landscapes. Adjacent soil columns interact with each other through the downslope lateral transport of water (Figures A1 and A2). Surface and subsurface lateral flow are routed using a multiple flow direction method [Freeman, 1991; Quinn et al., 1991]. As with vertical drainage of soil water, lateral subsurface downslope flow is modeled using a simple logistic function multiplied by a factor to account for the local topographic slope angle (equation (A16))… The model is forced with daily temperature and precipitation. Daily observed streamflow data is used to calibrate and validate simulated discharge." "Model calibration is needed to accurately capture the pre- and postharvest dynamics at WS10. This model calibration consists of two simulations: an old-growth simulation for the period 1969-1974 and a post-harvest simulation for the period 1975-2008." Two additional sets of VELMA simulations examining changes in streamflow are presented in the paper, but not included here. Twenty simulations were conducted varying the location across the watershed of a 20% har | ABSTRACT: "We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for … commercial blue crab Callinectes sapidus and penaeid shrimp fisheries in the Gulf of Mexico." | ABSTRACT: "...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: "Within a given viewshed, our models quantified the contribution of viewshed source features such as mountains and water bodies and sinks that detract from view quality, including obstructions or visual blight such as industrial or commercial development. Source, sink, and use locations were linked by a flow model that computed visibility along lines of sight from use locations to scenic viewshed features. The model includes a distance decay function that accounts for changes with distance in the value of views. We then computed the ratio of actual to theoretical provision of scenic views to compare the values accruing to homeowners relative to those for the entire landscape." | ABSTRACT: "...Here, we use an individual-based population model to assess potential population-wide effects of rodenticide exposures on the endangered San Joaquin kit fox (Vulpes macrotis mutica). We estimate likelihood of rodenticide exposure across the species range for each land cover type based on a database of reported pesticide use and literature…" AUTHOR'S DESCRIPTION: "We simulated individual kit foxes across their range using HexSim [33], a computer modeling platform for constructing spatially explicit population models. Our model integrated life history traits, repeated exposures to rodenticides, and spatial data layers describing habitat and locations of likely exposures. We modeled female kit foxes using yearly time steps in which each individual had the potential to disperse, establish a home range, acquire resources from their habitat, reproduce, accumulate rodenticide exposures, and die." "Simulated kit foxes assembled home ranges based on local habitat suitability, with range size inversely related to habitat suitability [34,35]. Kit foxes aimed to acquire a home range with a target score corresponding to the observed 544 ha home range size in the most suitable habitat [26]. Modeled home ranges varied in size from 170 ha to 1000 ha. Kit foxes were assigned to a resource class depending on the quality of the habitat in their acquired home range. The resource class then influenced rates of kit fox survival," "Juveniles and adults without ranges searched for a home range across 30 km2 outside of their natal range, using HexSim’s ‘adaptive’ exploration algorithm [33]." | AUTHOR'S DESCRIPTION: "In addition to affecting water quality, the ecosystem services of nitrogen retention, phosphorous retention, and sediment retention were also considered to contribute to stakeholder goals of maintaining the productivity of agricultural land and reducing soil loss. Two additional metrics, nitrogen fixation and rates of carbon sequestration into soil and sediment, were also calculated as potential measures of soil quality and agricultural productivity. Carbon sequestration and nitrogen fixation rates were assigned to each land cover class, applying the mean of rates for natural sub-tropical ecosystems obtained from the literature." | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…Synthesis of scientific literature and expert opinion can be used to estimate the relative potential for recreational opportunities across different benthic habitat types (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to recreational opportunities as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative recreational opportunity j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A.palmata, Montastraea reef, patch reef, and dense or sparse gorgonians), and Mij is the magnitude associated with each habitat for a given metric j: snorkeling opportunity" | ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. " | ABSTRACT: "Diadromous fish are an important link between marine and freshwater food webs. Pacific salmon (Oncorhynchus spp.) strongly impact nutrient dynamics in inland waters and anadromous alewife (Alosa pseudoharengus) may play a similar ecological role along the Atlantic coast. The annual spawning migration of anadromous alewife contributes, on average, 1050 g of nitrogen and 120 g of phosphorus to Bride Brook, Connecticut, USA, through excretion and mortality each year... There was no significant effect of this nutrient influx on water chemistry, leaf decomposition, or periphyton accrual. Dam removal and fish ladder construction will allow anadromous alewife to regain access to historical freshwater spawning habitats, potentially impacting food web dynamics and nutrient cycling in coastal freshwater systems." | Author Description: " The Wetland Ecosystem Services Protocol (WESP) is a standardized template for creating regionalized methods which then can be used to rapid assess ecosystem services (functions and values) of all wetland types throughout a focal region. To date, regionalized versions of WESP have been developed (or are ongoing) for government agencies or NGOs in Oregon, Alaska, Alberta, New Brunswick, and Nova Scotia. WESP also may be used directly in its current condition to assess these services at the scale of an individual wetland, but without providing a regional context for interpreting that information. Nonetheless, WESP takes into account many landscape factors, especially as they relate to the potential or actual benefits of a wetland’s functions. A WESP assessment requires completing a single three-part data form, taking about 1-3 hours. Responses to questions on that form are based on review of aerial imagery and observations during a single site visit; GIS is not required. After data are entered in an Excel spreadsheet, the spreadsheet uses science-based logic models to automatically generate scores intended to reflect a wetland’s ability to support the following functions: Water Storage and Delay, Stream Flow Support, Water Cooling, Sediment Retention and Stabilization, Phosphorus Retention, Nitrate Removal and Retention, Carbon Sequestration, Organic Nutrient Export, Aquatic Invertebrate Habitat, Anadromous Fish Habitat, Non-anadromous Fish Habitat, Amphibian & Reptile Habitat, Waterbird Feeding Habitat, Waterbird Nesting Habitat, Songbird, Raptor and Mammal Habitat, Pollinator Habitat, and Native Plant Habitat. For all but two of these functions, scores are given for both components of an ecosystem service: function and benefit. In addition, wetland Ecological Condition (Integrity), Public Use and Recognition, Wetland Sensitivity, and Stressors are scored. Scores generated by WESP may be used to (a) estimate a wetland’s relative ecological condition, stress, and sensitivity, (b) compare relative levels of ecosystem services among different wetland types, or (c) compare those in a single wetland before and after restoration, enhancement, or loss."] | ABSTRACT:"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: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context." | ABSTRACT: "We introduce and validate the use of commercially available human mobility datasets based on cell phone locations to estimate visitation to natural areas. By combining this data with on-the-ground observations of visitation to water recreation areas in New England, we fit a model to estimate daily visitation for four months to more than 500 sites. The results show the potential for this new big data source of human mobility to overcome limitations in traditional methods of estimating visitation and to provide consistent information at policy-relevant scales. However, the data providers’ opaque and rapidly developing methods for processing locational information required a calibration and validation against data collected by traditional means to confidently reproduce the desired estimates of visitation. We found that with this calibration, the high-resolution information in both space and time provided by cell phone location-derived data creates opportunities for developing next-generation models of human interactions with the natural environment. " | NatureServe Vista is a broad assessment and planning decision support tool focused on conservation of specific mapped features or “conservation elements.” It facilitates capturing spatial and non-spatial information and conservation requirements for elements, defining scenarios of land use, management, conservation, disturbance, etc., and evaluating the impacts of scenarios on the elements. Vista also contains powerful internal tools and interoperability with outside tools to facilitate mitigating site-level conflicts, offsite mitigation, and development of alternative scenarios. The primary objective (though not exclusive application) of the tool is to develop/mitigate alternative scenarios such that they meet explicit conservation goals for the elements. Vista can also support goal seeking for competing land uses while preventing development of scenarios that attempt to meet goals for conflicting things in the same place. The primary role of NatureServe Vista in this toolkit is to evaluate the impacts of land use scenarios on conservation elements in terrestrial, freshwater, and marine ecosystems. It does this through direct evaluation of land use scenarios from CommunityViz (augmented with other use, management, disturbance data) and interoperating with N-SPECT to evaluate water quality impacts on aquatic/marine elements. | Authors description: "The Environmental Technologies Design Option Tool (ETDOT) is a suite of software models that provides engineers with the capability to evaluate and design systems that use granular activated carbon or ion exchange resins for the removal of contaminants, including PFAS, from drinking water and wastewater. Information generated from ETDOT models will provide states and utilities with a better understanding of the fundamentals of carbon adsorption and what that means to the operation, performance, and costs associated with this technology. Even though carbon adsorption can be an effective treatment technology for removing organic compounds, such as PFAS, from water, it can be expensive or may not achieve desired removal objectives if improperly designed. Proper full-scale design of this adsorption process typically results from carefully controlled pilot-scale studies that are used to determine important design variables, such as the type of adsorbent, empty bed contact time, and bed configuration. However, these studies can be time consuming and expensive if they are not properly planned. Information generated from the ETDOT models can be used to help design pilot treatment systems and provide a first-cut prediction of full-scale results.] | Emergency management research on sudden organic pollution accidents in urban water sources is of great significance. In the study, AQUATOX, coupled water quality and water quantity based on food web, was implemented to simulate and predict the effects of sudden Polychlorinated biphenyls (PCBs) pollution under different concentrations scenario on water quality and dominant biological populations in Panshitou Reservoir, China. The model was used to quantify how the biomass changes of the modelled taxa in the reservoir food web deviated from natural conditions due to varying concentration inputs of the PCBs. Also, no observed effect concentrations (NOECs) were derived using AQUATOX model. The results showed that the contents of DO, TN, TP and NH4+-N increased slightly with the concentration increase of PCBs, and the comprehensive water quality indexes (WQI) decreased little. The biomass of dominant biological populations did not show significant changes in the previous week, but the changes gradually increased, and the ecological risk rose correspondingly. The NOEC levels of PCBs for primary producers, such as diatom, green algae, blue-green algae and cryptoalgae, were about 1.89μg/L, 0.66μg/L, 0.81μg/L and 0.45μg/L, respectively; consumers such as water fleas, planktonic predators, and oligochaetes were around 3.4μg/L, 2.6 μg/L, and 0.81 μg/L, respectively. Compared with benchmarks of NOEC from the PCB Residue Effects Database of USEPA, threshold concentration computed using AQUATOX model were generally of the same order of magnitude in Panshitou Reservoir ecosystem. Our findings indicated that ecosystem models could be a useful tool in the assessment of organic chemical sudden impact on reservoir ecosystems as a whole. |
Specific Policy or Decision Context Cited
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None identified | Strategic habitat conservation by USFW for Wetland Conservation Act funding | None identified | None identified | 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 | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None reported | None identified | None identified | None provided | Not Applicable | None identified |
Biophysical Context
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Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Boreal Hardwood Transition, Eastern Tallgrass Prairie, Prairie Hardwood Transition, Central Hardwoods | Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak. | No additional description provided | Spring-sown cereal croplands, where the bird chearry-oat aphid is a key aphid pest. The aphid colonizes the crop during late May and early June, depending on weather and location. The colonization phase is followed by a brief phase of rapid exponential population growth by wingless aphids, continuing until about the time of crop heading, in late June or early July. After heading, aphid populations usually decline rapidly in the crop due to decreased plant quality and migration to grasslands. The aphids are attacked by a complex of arthropod natural enemies, but parasitism is not important in the region and therefore not modelled here. | 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. | Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter. Mean annual precipitation is 2300 mm and falls primarily as rain between October and April. Total rainfall during June– September averages 200 mm. Snow rarely persists longer than a couple of weeks and usually melts within 1 to 2 days. Average annual streamflow is 1600 mm, which is approximately 70% of annual precipitation. Soils are of the Frissel series, classified as Typic Dystrochrepts with fine loamy to loamy-skeletal texture that are generally deep and well drained. These soils quickly transmit subsurface water to the stream. Prior to the 1975 100% clearcut, WS10 was a 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). The dominant vegetation of WS10 today is a 35 year old mixed Douglasfir and western hemlock stand. | Estuarine environments and marsh-land interfaces | No additional description provided | No additional description provided | No additional description provided | No additional description provided | stream and river reaches of Michigan | Alewife spawning runs typically occur Mid March - May. | None | Conservation Reserve Program lands left to go fallow | Waterfront districts on south Lake Michigan and south lake Erie | Natural area water bodies | Not applicable | Not applicable | Panshitou Reservoir, Qihe Riverin Haihe River Basin |
EM Scenario Drivers
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No scenarios presented | Conservation efforts for: marsh-wetland breeding birds, regional marsh and open-water for non-breeding birds, mudflat/shallows for birds during non-breeding period. | No scenarios presented | No scenarios presented | No scenarios presented | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | Stand age; old-growth (pre-harvest), and harvested (postharvest) | Shellfish type; Changes to submerged aquatic vegetation (SAV) | No scenarios presented | Rodenticide exposure level, and rodenticide exposure on low intensity development land cover class | No scenarios presented | No scenarios presented | targeted sport fish biomass | No scenarios presented | N/A | N/A | N/A | N/A | No scenarios presented | Not applicable | PCB concentration |
EM ID
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EM-79 | EM-113 | EM-132 | EM-184 | EM-303 |
EM-349 ![]() |
EM-375 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-430 | EM-454 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-838 | EM-890 | EM-943 | EM-1006 | EM-1009 |
EM-1054 ![]() |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs |
Method + Application (multiple runs exist) View EM Runs ?Comment:Ten runs; blue crab and penaeid shrimp, each combined with five different submerged aquatic vegetation habitat areas. |
Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships. |
Method + Application | Method + Application | 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 Only | Method Only | 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 | 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 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
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EM-79 | EM-113 | EM-132 | EM-184 | EM-303 |
EM-349 ![]() |
EM-375 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-430 | EM-454 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-838 | EM-890 | EM-943 | EM-1006 | EM-1009 |
EM-1054 ![]() |
EM Temporal Extent
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2007-2008 | 2007 | Not reported | Not reported | 2009 | 2008-2020 | 1969-2008 | 1950 - 2050 | 1992-2006 | 60 yr | 1978 - 2013 | 2006-2007, 2010 | 2008-2010 | 1979-2009 | Not applicable | 2008 | 2022 | 2017 | Not applicable | Not applicable | 2018 |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | Not applicable | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | future time | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable | past time |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | other or unclear (comment) | Not applicable | discrete |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Varies by Run | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Day | Year | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Day |
EM ID
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EM-79 | EM-113 | EM-132 | EM-184 | EM-303 |
EM-349 ![]() |
EM-375 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-430 | EM-454 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-838 | EM-890 | EM-943 | EM-1006 | EM-1009 |
EM-1054 ![]() |
Bounding Type
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Physiographic or Ecological | Physiographic or ecological | Geopolitical | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Not applicable | Physiographic or ecological | Geopolitical | Point or points | Not applicable | Not applicable | Watershed/Catchment/HUC |
Spatial Extent Name
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Central French Alps | Upper Mississippi River and Great Lakes Region | Municipality of Etropole | European Union countries | Uppland province | central Sumatra | H. J. Andrews LTER WS10 | Gulf of Mexico (estuarine and coastal) | Puget Sound Region | San Joaquin Valley, CA | Guanica Bay watershed | Coastal zone surrounding St. Croix | HUCS in Michigan | Bride Brook | Not applicable | Piedmont Ecoregion | Great Lakes waterfront | Cape Cod | Not applicable | Not applicable | Panshitou Reservoir |
Spatial Extent Area (Magnitude)
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10-100 km^2 | >1,000,000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 10,000-100,000 km^2 | 100,000-1,000,000 km^2 | 10-100 ha | 10,000-100,000 km^2 | 10,000-100,000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 100,000-1,000,000 km^2 | 1-10 ha | Not applicable | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | Not applicable | Not applicable | 100-1000 km^2 |
EM ID
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EM-79 | EM-113 | EM-132 | EM-184 | EM-303 |
EM-349 ![]() |
EM-375 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-430 | EM-454 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-838 | EM-890 | EM-943 | EM-1006 | EM-1009 |
EM-1054 ![]() |
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) ?Comment:Computations at this pixel scale pertain to certain variables specific to Mobile Bay. |
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 lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | other or unclear (comment) | spatially lumped (in all 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 | 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 | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | Not applicable | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
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20 m x 20 m | 1 ha | Distributed by land cover and soil type polygons | 100 m x 100 m | 25 m x 25 m | 30 m x 30 m | 30 m x 30 m surface pixel and 2-m depth soil column | 55.2 km^2 | 200m x 200m | 14 ha | 30 m x 30 m | 10 m x 10 m | reach in HUC | Not applicable | not reported | Not applicable | Not applicable | water feature edge (beach) | Not applicable | Not applicable | Irregular segments of resrvoir |
EM ID
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EM-79 | EM-113 | EM-132 | EM-184 | EM-303 |
EM-349 ![]() |
EM-375 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-430 | EM-454 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-838 | EM-890 | EM-943 | EM-1006 | EM-1009 |
EM-1054 ![]() |
EM Computational Approach
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Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Numeric | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | 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-79 | EM-113 | EM-132 | EM-184 | EM-303 |
EM-349 ![]() |
EM-375 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-430 | EM-454 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-838 | EM-890 | EM-943 | EM-1006 | EM-1009 |
EM-1054 ![]() |
Model Calibration Reported?
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No | No | No | No | No | No | Yes | Yes | No | Unclear | No | Yes | No |
Yes ?Comment:The fish counter (for alewife numbers) was calibrated. |
Not applicable | Yes | No | Yes | Not applicable | Not applicable | Yes |
Model Goodness of Fit Reported?
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Yes | No | No | No | No | No | Yes | No | No | No | No | No | Yes | No | Not applicable | No | No |
Yes ?Comment:Random forest model performance statistics |
Not applicable | Not applicable | Yes |
Goodness of Fit (metric| value | unit)
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None | None | None | None | None |
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None | None | None | None | None |
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None | None | None | None |
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None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | No | No | No | Yes | No | No | No | No | No | No | Yes | No | No | No | No | No | Yes | Not applicable | Unclear | Yes |
Model Uncertainty Analysis Reported?
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No | No | No | No | No | No | No | No | No | No | No | No | No | No | Not applicable | No | No | Unclear | Not applicable | Not applicable | Unclear |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | No |
Yes ?Comment:AUTHOR'S NOTE: "Varying aphid fecundity, overall predator abundances and attack rates affected the biological control effect, but had little influence on the relative differences between landscapes with high and low levels of biological control. The model predictions were more sensitive to changing the predators' landscape relations, but, with few exceptions, did not dramatically alter the overall patterns generated by the model." |
No | No | No | No | Yes | No | No | No | No | Not applicable | Yes | Yes | Yes | Not applicable | Not applicable | Unclear |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Unclear | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-79 | EM-113 | EM-132 | EM-184 | EM-303 |
EM-349 ![]() |
EM-375 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-430 | EM-454 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-838 | EM-890 | EM-943 | EM-1006 | EM-1009 |
EM-1054 ![]() |
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None |
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None |
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None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-79 | EM-113 | EM-132 | EM-184 | EM-303 |
EM-349 ![]() |
EM-375 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-430 | EM-454 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-838 | EM-890 | EM-943 | EM-1006 | EM-1009 |
EM-1054 ![]() |
None | None | None | None | None | None | None |
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None |
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None |
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None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-79 | EM-113 | EM-132 | EM-184 | EM-303 |
EM-349 ![]() |
EM-375 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-430 | EM-454 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-838 | EM-890 | EM-943 | EM-1006 | EM-1009 |
EM-1054 ![]() |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | 42.05 | 42.8 | 48.2 | 59.52 | 0 | 44.15 | 30.44 | 48 | 36.13 | 17.96 | 17.73 | 45.12 | 41.32 | Not applicable | 36.23 | 42.26 | 41.72 | Not applicable | Not applicable | 39.49 |
Centroid Longitude
em.detail.ddLongHelp
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6.4 | -88.6 | 24 | 16.35 | 17.9 | 102 | -122.2 | -87.99 | -123 | -120 | -67.02 | -64.77 | 85.18 | -72.24 | Not applicable | -81.9 | -87.84 | -70.29 | Not applicable | Not applicable | 113.85 |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | Not applicable | Not applicable | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Estimated | Estimated | Estimated | Estimated | Provided | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Not applicable | Estimated | Estimated | Estimated | Not applicable | Not applicable | Estimated |
EM ID
em.detail.idHelp
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EM-79 | EM-113 | EM-132 | EM-184 | EM-303 |
EM-349 ![]() |
EM-375 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-430 | EM-454 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-838 | EM-890 | EM-943 | EM-1006 | EM-1009 |
EM-1054 ![]() |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Inland Wetlands | Rivers and Streams | Lakes and Ponds | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Rivers and Streams | Ground Water | Forests | Near Coastal Marine and Estuarine | Lakes and Ponds | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Rivers and Streams | Rivers and Streams | Inland Wetlands | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Lakes and Ponds | Near Coastal Marine and Estuarine | Not applicable | Not applicable | Lakes and Ponds |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows | Not reported | Mountainous flood-prone region | Not applicable | Spring-sown cereal croplands and surrounding grassland and non-arable land | 104 land use land cover classes | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | Submerged aquatic vegetation in estuaries and coastal lagoons | Terrestrial environment surrounding a large estuary | Agricultural region (converted desert) and terrestrial perimeter | 13 LULC were used | Coral reefs | stream reaches | Coastal stream | Wetlands | grasslands | Lake Michigan & Lake Erie waterfront | beaches | None | Not applicable | Tributary reservoir |
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 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 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 corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Not applicable | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-79 | EM-113 | EM-132 | EM-184 | EM-303 |
EM-349 ![]() |
EM-375 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-430 | EM-454 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-838 | EM-890 | EM-943 | EM-1006 | EM-1009 |
EM-1054 ![]() |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Species | Not applicable | Not applicable | Individual or population, within a species | Community | Not applicable | Species | Not applicable | Individual or population, within a species | Not applicable | Guild or Assemblage | Not applicable | Individual or population, within a species | Not applicable | Species | Not applicable | Not applicable | Community | Not applicable | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
EM-79 | EM-113 | EM-132 | EM-184 | EM-303 |
EM-349 ![]() |
EM-375 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-430 | EM-454 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-838 | EM-890 | EM-943 | EM-1006 | EM-1009 |
EM-1054 ![]() |
None Available |
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None Available | None Available |
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None Available | None Available |
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None Available |
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None Available | None Available |
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None Available |
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None Available | None Available | None Available | None Available |
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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-79 | EM-113 | EM-132 | EM-184 | EM-303 |
EM-349 ![]() |
EM-375 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-430 | EM-454 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-838 | EM-890 | EM-943 | EM-1006 | EM-1009 |
EM-1054 ![]() |
None |
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None |
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None |
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None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-79 | EM-113 | EM-132 | EM-184 | EM-303 |
EM-349 ![]() |
EM-375 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-430 | EM-454 |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-838 | EM-890 | EM-943 | EM-1006 | EM-1009 |
EM-1054 ![]() |
None |
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None | None |
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None | None | None |
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None |
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None | None |
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