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-63 | EM-84 | EM-113 | EM-178 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-943 | EM-970 | EM-971 | EM-999 | EM-1006 |
EM Short Name
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EnviroAtlas - Natural biological nitrogen fixation | ACRU, South Africa | Wetland conservation for birds, Midwestern USA | Natural attenuation by soil, The Netherlands | Biological pest control, Uppland Province, Sweden | InVEST (v1.004) Carbon, Indonesia | Wetland shellfish production, Gulf of Mexico, USA | ARIES viewsheds, Puget Sound Region, USA | HexSim v2.4, San Joaquin kit fox, CA, USA | RUM: Valuing fishing quality, Michigan, USA | Alewife derived nutrients, Connecticut, USA | WESP Method | HWB Blood pressure, Great Lakes waterfront, USA | Visitation to natural areas, New England, USA | Air quality regulation, Lisbon | Stormwater pollutant loads, Finland | B-INTACT biodiversity tool | Vista land-sea planning submodel |
EM Full Name
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US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | ACRU (Agricultural Catchments Research Unit), South Africa | Prioritizing wetland conservation for birds, Midwestern USA | Natural attenuation capacity of the soil, The Netherlands | 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 | 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 | 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) | Human well being indicator- Blood pressure, Great Lakes waterfront, USA | Estimating natural area use with cell phone data, Narragansett Beach, New England, USA | Modeling the provision of air-quality regulation ecosystem service provided by urban green spaces using lichens as ecological indicators | Assessment of stormwater pollutant loads and source area contributions with storm water management model (SWMM) | Biodiversity integrated assessment and computation tool | B-INTACT | A technical guide to the integrated land-sea planning toolkit |
EM Source or Collection
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US EPA | EnviroAtlas | None | None | None | None | InVEST |
US EPA ?Comment:Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science |
ARIES | US EPA | None | None | None | None | US EPA | None | None | None | None |
EM Source Document ID
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262 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
271 | 122 | 287 | 299 | 309 | 324 | 302 |
337 ?Comment:The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships. |
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 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
436 | 454 | 455 | 469 | 473 |
Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Thogmartin, W. A., Potter, B. A. and Soulliere, G. J. | van Wijnen, H.J., Rutgers, M., Schouten, A.J., Mulder, C., de Zwart, D., and Breure, A.M. | 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. | 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 | 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. | 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 | Matos, P., Vieira, J., Rocha, B., Branquinho, C., & Pinho, P. | Tuomela, C., N., Sillanpaa, and H. Koivusalo | FAO | Crist, P., Madden, K., Varley, I., Eslinger, D., Walker, D., Anderson, A., Morehead, S. and Dunton, K., |
Document Year
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2013 | 2008 | 2011 | 2012 | 2014 | 2014 | 2012 | 2014 | 2015 | 2014 | 2009 | 2016 | None | 2020 | 2019 | 2019 | 2021 | 2009 |
Document Title
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EnviroAtlas - National | Mapping ecosystem services for planning and management | Bridging the conservation design and delivery gap for wetland bird habitat maintenance and restoration in the midwestern United States | How to calculate the spatial distribution of ecosystem services - Natural attenuation as example from the Netherlands | Ecological production functions for biological control services in agricultural landscapes | Ecosystem services reinforce Sumatran tiger conservation in land use plans | 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 | 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. | 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 | Modeling the provision of air-quality regulation ecosystem service provided by urban green spaces using lichens as ecological indicators | Assessment of stormwater pollutant loads and source area contributions with storm water management model (SWMM) | Biodiversity integrated assessment and computational tool | B-INTACT | Integrated Land-Sea Planning: A Technical Guide to the Integrated Land-Sea Planning Toolkit. |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed but unpublished (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Journal manuscript submitted or in review | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published report |
EM ID
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EM-63 | EM-84 | EM-113 | EM-178 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-943 | EM-970 | EM-971 | EM-999 | EM-1006 |
https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | http://aries.integratedmodelling.org/ | http://www.hexsim.net/ | Not applicable | Not applicable |
http://people.oregonstate.edu/~adamusp/WESP/ ?Comment:This is an Excel spreadsheet calculator |
Not applicable | https://github.com/USEPA/Recreation_Benefits.git | Not applicable | https://www.epa.gov/water-research/storm-water-management-model-swmm | https://openknowledge.fao.org/items/6822f818-949b-43fb-8e5f-c75fc1018af1 | https://repositories.lib.utexas.edu/bitstreams/3dee92a8-9373-4bcc-be25-eda74e81fabf/download | |
Contact Name
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EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Roland E Schulze | Wayne Thogmartin, USGS | H.J. van Wijnen | Mattias Jonsson | Nirmal K. Bhagabati | Stephen J. Jordan | Ken Bagstad | Theresa M. Nogeire | Richard Melstrom | Annika W. Walters | Paul R. Adamus | Ted Angradi | Nathaniel Merrill | Pedro Pinho | Camilla Tuomela | FAO |
Patrick Crist ?Comment:No contact information provided |
Contact Address
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Not reported | School of Bioresources Engineering and Environmental Hydrology, University of Natal, South Africa | Upper Midwest Environmental Sciences Center, 2630 Fanta Reed Road, La Crosse, WI 54603 | National Institute for Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, The Netherlands | 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 | 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 | 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 | 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, | N/A | Department of Built Environment, Aalto University School of Engineering, P.O. Box 15200, FI- 00076, Aalto, Finland | Rome, Italy | None provided |
Contact Email
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enviroatlas@epa.gov | schulzeR@nu.ac.za | wthogmartin@usgs.gov | harm.van.wijnen@rivm.nl | mattias.jonsson@slu.se | nirmal.bhagabati@wwfus.org | jordan.steve@epa.gov | kjbagstad@usgs.gov | tnogeire@gmail.com | melstrom@okstate.edu | annika.walters@yale.edu | adamus7@comcast.net | tedangradi@gmail.com | merrill.nathaniel@epa.gov | ppinho@fc.ul.pt | camilla.tuomela@aalto.fi | None provided | patrick@planitfwd.com |
EM ID
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EM-63 | EM-84 | EM-113 | EM-178 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-943 | EM-970 | EM-971 | EM-999 | EM-1006 |
Summary Description
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DATA FACT SHEET: "This EnviroAtlas national map displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM." | AUTHOR'S DESCRIPTION (Doc ID 272): "ACRU is a daily timestep, physical conceptual and multipurpose model structured to simulate impacts of land cover/ use change. The model can output, inter alia, components of runoff, irrigation supply and demand, reservoir water budgets as well as sediment and crop yields." AUTHOR'S DESCRIPTION (Doc ID 271): "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…The total benefit to people of water supply is a function of both the quantity and quality with the ecosystem playing a key role in the latter. However, due to the lack of suitable national scale data on water quality for quantifying the service, runoff was used as an estimate of the benefit where runoff is the total water yield from a watershed including surface and subsurface flow. This assumes that runoff is positively correlated with quality, which is the case in South Africa (Allanson et al., 1990)…In South Africa, water resources are mapped in water management areas called catchments (vs. watersheds) where a catchment is defined as the area of land that is drained by a single river system, including its tributaries (DWAF, 2004). There are 1946 quaternary (4th order) catchments in South Africa, the smallest is 4800 ha and the average size is 65,000 ha. Schulze (1997) modelled annual runoff for each quaternary catchment. During modelling of runoff, he used rainfall data collected over a period of more than 30 years, as well as data on other climatic factors, soil characteristics and grassland as the land cover. In this study, median annual simulated runoff was used as a measure of surface water supply. The volume of runoff per quaternary catchment was calculated for surface water supply. The range (areas with runoff of 30 million m^3 or more) and hotspots (areas with runoff of 70 million m^3 or more) were defined using a combination of statistics and expert inputs due to a lack of published thresholds in the literature." | 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: "Maps play an important role during the entire process of spatial planning and bring ecosystem services to the attention of stakeholders' negotiation more easily. As example we show the quantification of the ecosystem service ‘natural attenuation of pollutants’, which is a service necessary to keep the soil clean for production of safe food and provision of drinking water, and to provide a healthy habitat for soil organisms to support other ecosystem services. A method was developed to plot the relative measure of the natural attenuation capacity of the soil in a map. Several properties of Dutch soils were related to property-specific reference values and subsequently combined into one proxy for the natural attenuation of pollutants." AUTHOR'S DESCRIPTION: "The natural attenuation capacity that is modeled in this study must be seen as a measure that describes the ‘biodegradation capacity’ of the soil, including biodegradation of all types of contaminants" | 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." | 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]." | 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: "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. " | The UN Sustainable Development Goals states that urban air pollution must be tackled to create more inclusive, safe, resilient and sustainable cities. Urban green infrastructures can mitigate air pollution, but a crucial step to use this knowledge into urban management is to quantify how much air-quality regulation can green spaces provide and to understand how the provision of this ecosystem service is affected by other environmental factors. Considering the insufficient number of air quality monitoring stations in cities to monitor the wide range of natural and anthropic sources of pollution with high spatial resolution, ecological indicators of air quality are an alternative cost-effective tool. The aim of this work was to model the supply of air-quality regulation based on urban green spaces characteristics and other environmental factors. For that, we sampled lichen diversity in the centroids of 42 urban green spaces in Lisbon, Portugal. Species richness was the best biodiversity metric responding to air pollution, considering its simplicity and its significative response to the air pollutants concentration data measured in the existent air quality monitoring stations. Using that metric, we then created a model to estimate the supply of air quality regulation provided by green spaces in all green spaces of Lisbon based on the response to the following environmental drivers: the urban green spaces size and its vegetation density. We also used the unexplained variance of this model to map the background air pollution. Overall, we suggest that management should target the smallest urban green spaces by increasing green space size or tree density. The use of ecological indicators, very flexible in space, allow the understanding and the modeling of the provision of air-quality regulation by urban green spaces, and how urban green spaces can be managed to improve air quality and thus improve human well-being and cities resilience. | Decentralized urban runoff management requires detailed information about pollutant sources and pathways. However, scarce data of local water quality compel simplified approaches in water quality modelling. This study investigated the use of constant source concentrations in modelling pollutant loads. The source area contributions of total suspended solids, total phosphorus, total nitrogen, lead, copper and zinc were modelled with SWMM based on literature event mean concentrations (EMCs) for different land cover types and on-site rainfall and discharge data for a residential area in southern Finland. The simulated pollutant loads were compared with loads measured at the catchment outlet. Large differences were evident in the modelled catchment-scale and land cover specific loads, depending on the EMC data source. The simulated loads exceeded the measured loads especially during wet conditions, which was explained by the dilution effect of large stormwater volumes on measured EMCs. In addition, the mismatch was explained by the lack of local data for the source area EMCs and by the unaccountability of the mechanisms affecting loads along the pollutant pathways from source areas to sewer outlet. The spatial simulation of stormwater pollutant loads enabled the assessment of source area contributions at the catchment scale, as well as the pollutant pathways and the total diffuse pollution load. For a single pollutant, one or two important pollutant sources contributed the majority of the catchment load, which provides useful information for stormwater management. However, for a group of pollutants, no single land cover type dominated the pollutant loads, reflecting the challenges in decentralized water quality management in the scale of a residential area. Overall, the results emphasize that the widely used stormwater quality modelling with constant EMCs is uncertain even when on-site water quality and rainfall-runoff data from a catchment outlet are available. | As a timely response, the EX-ACT team from the Food and Agriculture Organization (FAO) of the United Nations has developed the Biodiversity Integrated Assessment and Computation Tool (B-INTACT). B-INTACT uniquely seeks to extend the scope of environmental assessments to capture biodiversity concerns, which are not accounted for in conventional carbon pricing. The tool is designed for users ranging from national investment banks, international financial institutions and policy decision-makers, and allows for a thorough biodiversity assessment of project-level activities in the Agriculture, Forestry and Land Use (AFOLU) sector while maintaining the logic of the EX-ACT model. The biodiversity assessment in the tool takes on a quantitative and qualitative approach. The quantitative approach considers a set of relationships for anthropogenic impacts on biodiversity from land use changes, habitat fragmentation, infrastructure and human encroachment. Biodiversity responses are quantified in the mean species abundance (MSA) metric, which expresses the mean abundance of original species in disturbed conditions relative to their abundance in an undisturbed habitat (where MSA = 1 highlights an entirely intact ecosystem and MSA = 0 highlights a fully destroyed ecosystem). Nonquantifiable impacts to biodiversity from project activities are assessed with a qualitative appraisal of the biodiversity sensitivity, management activities and agrobiodiversity practices, to complement the quantitative assessment. | 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. |
Specific Policy or Decision Context Cited
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None Identified | None identified | Strategic habitat conservation by USFW for Wetland Conservation Act funding | 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 | N/A | None identified | None provided |
Biophysical Context
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No additional description provided | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | Boreal Hardwood Transition, Eastern Tallgrass Prairie, Prairie Hardwood Transition, Central Hardwoods | Five soil types including Löss, Fluvial clay, Peat, Sand, and Silty Loam. Five land-use types including Pasture, Arable farming, Semi-natural grassland, Heathland, and Forest. | 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. | Estuarine environments and marsh-land interfaces | No additional description provided | No additional description provided | stream and river reaches of Michigan | Alewife spawning runs typically occur Mid March - May. | None | Waterfront districts on south Lake Michigan and south lake Erie | Natural area water bodies | Green spaces in Lisbon, Portugal | Urban residential catchment area, Espoo, Finland | N/A | Not applicable |
EM Scenario Drivers
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No scenarios presented | 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 | Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan | Shellfish type; Changes to submerged aquatic vegetation (SAV) | No scenarios presented | Rodenticide exposure level, and rodenticide exposure on low intensity development land cover class | targeted sport fish biomass | No scenarios presented | N/A | N/A | N/A | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented |
EM ID
em.detail.idHelp
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EM-63 | EM-84 | EM-113 | EM-178 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-943 | EM-970 | EM-971 | EM-999 | EM-1006 |
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 ?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 (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 Only | Method Only |
New or Pre-existing EM?
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New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-63 | EM-84 | EM-113 | EM-178 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-943 | EM-970 | EM-971 | EM-999 | EM-1006 |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-346 | Doc-347 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
Doc-272 ?Comment:Doc ID 272 was also used as a source document for this EM |
Doc-169 | Doc-170 | Doc-171 | Doc-172 | Doc-173 | Doc-174 | Doc-175 | Doc-288 | None | Doc-315 | None | Doc-303 | Doc-305 | Doc-328 | Doc-327 | Doc-2 | None | Doc-383 | None | Doc-422 | None | None | Doc-452 | None | Doc-473 |
EM ID for related EM
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None | None | None | None | None | EM-374 | EM-604 | EM-603 | None | EM-403 | EM-98 | None | EM-661 | EM-665 | EM-666 | EM-672 | EM-674 | EM-673 | EM-718 | EM-886 | EM-888 | EM-889 | EM-891 | EM-893 | EM-894 | EM-895 | None | None | EM-968 | None | EM-1003 | EM-1005 | EM-1007 | EM-1008 |
EM Modeling Approach
EM ID
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EM-63 | EM-84 | EM-113 | EM-178 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-943 | EM-970 | EM-971 | EM-999 | EM-1006 |
EM Temporal Extent
em.detail.tempExtentHelp
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2006-2010 | 1950-1993 | 2007 | 1999-2005 | 2009 | 2008-2020 | 1950 - 2050 | 1992-2006 | 60 yr | 2008-2010 | 1979-2009 | Not applicable | 2022 | 2017 | 2015-2018 | 2005-2006 | Not applicable | Not applicable |
EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-dependent |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
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Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | past time | Not applicable | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | other or unclear (comment) | Not applicable | other or unclear (comment) |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Varies by Run | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-63 | EM-84 | EM-113 | EM-178 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-943 | EM-970 | EM-971 | EM-999 | EM-1006 |
Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Geopolitical | Physiographic or ecological | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Not applicable | Geopolitical | Point or points | Physiographic or ecological | Watershed/Catchment/HUC | Not applicable | Not applicable |
Spatial Extent Name
em.detail.extentNameHelp
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counterminous United States | South Africa | Upper Mississippi River and Great Lakes Region | The Netherlands | Uppland province | central Sumatra | Gulf of Mexico (estuarine and coastal) | Puget Sound Region | San Joaquin Valley, CA | HUCS in Michigan | Bride Brook | Not applicable | Great Lakes waterfront | Cape Cod | Urban green spaces in Lisbon | Vallikallio | Not applicable | Not applicable |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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>1,000,000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 10,000-100,000 km^2 | 10,000-100,000 km^2 | 100,000-1,000,000 km^2 | 10,000-100,000 km^2 | 10,000-100,000 km^2 | 10,000-100,000 km^2 | 100,000-1,000,000 km^2 | 1-10 ha | Not applicable | 1000-10,000 km^2. | 1000-10,000 km^2. | 100-1000 km^2 | 1-10 km^2 | Not applicable | Not applicable |
EM ID
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EM-63 | EM-84 | EM-113 | EM-178 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-943 | EM-970 | EM-971 | EM-999 | EM-1006 |
EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) ?Comment:Watersheds (12-digit HUCs). |
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 lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | other or unclear (comment) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | 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 | other (specify), for irregular (e.g., stream reach, lake basin) | map scale, for cartographic feature | Not applicable | map scale, for cartographic feature | Not applicable |
Spatial Grain Size
em.detail.spGrainSizeHelp
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irregular | Distributed by catchments with average size of 65,000 ha | 1 ha | 100 m x 100 m | 25 m x 25 m | 30 m x 30 m | 55.2 km^2 | 200m x 200m | 14 ha | reach in HUC | Not applicable | not reported | Not applicable | water feature edge (beach) | N/A | Not applicable | Not applicable | Not applicable |
EM ID
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EM-63 | EM-84 | EM-113 | EM-178 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-943 | EM-970 | EM-971 | EM-999 | EM-1006 |
EM Computational Approach
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Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Numeric | Numeric | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | 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-63 | EM-84 | EM-113 | EM-178 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-943 | EM-970 | EM-971 | EM-999 | EM-1006 |
Model Calibration Reported?
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No | No | No | No | No | No | Yes | No | Unclear | No |
Yes ?Comment:The fish counter (for alewife numbers) was calibrated. |
Not applicable | No | Yes | Yes | No | Not applicable | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | No | No | No | No | No | No | No | Yes | No | Not applicable | No |
Yes ?Comment:Random forest model performance statistics |
Yes | No | Not applicable | Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None | None | None | None | None |
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None | None | None |
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None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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No | No | No | No | Yes | No | No | No | No | No | No | No | No | Yes | No | Yes | Unclear | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | No | No | No | No | No | No | No | Not applicable | No | Unclear | No | Unclear | Not applicable | Not applicable |
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 | Yes | No | No | Not applicable | Yes | Yes | Unclear | Unclear | Not applicable | Not applicable |
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 | No | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | 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-63 | EM-84 | EM-113 | EM-178 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-943 | EM-970 | EM-971 | EM-999 | EM-1006 |
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None |
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None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-63 | EM-84 | EM-113 | EM-178 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-943 | EM-970 | EM-971 | EM-999 | EM-1006 |
None | None | None | None | None | None |
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None | 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-63 | EM-84 | EM-113 | EM-178 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-943 | EM-970 | EM-971 | EM-999 | EM-1006 |
Centroid Latitude
em.detail.ddLatHelp
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39.5 | -30 | 42.05 | 52.37 | 59.52 | 0 | 30.44 | 48 | 36.13 | 45.12 | 41.32 | Not applicable | 42.26 | 41.72 | 38.75 | 60.23 | Not applicable | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
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-98.35 | 25 | -88.6 | 4.88 | 17.9 | 102 | -87.99 | -123 | -120 | 85.18 | -72.24 | Not applicable | -87.84 | -70.29 | 9.8 | 24.82 | Not applicable | Not applicable |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | None provided | WGS84 | Not applicable | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Provided | Not applicable | Estimated | Estimated | Estimated | Estimated | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-63 | EM-84 | EM-113 | EM-178 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-943 | EM-970 | EM-971 | EM-999 | EM-1006 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Agroecosystems | Grasslands | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | 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) | Rivers and Streams | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Lakes and Ponds | Near Coastal Marine and Estuarine | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Not applicable |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Terrestrial | Not reported | Not reported | Not applicable | Spring-sown cereal croplands and surrounding grassland and non-arable land | 104 land use land cover classes | Submerged aquatic vegetation in estuaries and coastal lagoons | Terrestrial environment surrounding a large estuary | Agricultural region (converted desert) and terrestrial perimeter | stream reaches | Coastal stream | Wetlands | Lake Michigan & Lake Erie waterfront | beaches | Green spaces in Lisbon, Portugal | Urben residential stormwater catchment | Terrestrial | None |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 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 | Other or unclear (comment) | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-63 | EM-84 | EM-113 | EM-178 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-943 | EM-970 | EM-971 | EM-999 | EM-1006 |
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Species | Not applicable | Individual or population, within a species | Community | Species | Not applicable | Individual or population, within a species | Not applicable | Individual or population, within a species | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Community | Community |
Taxonomic level and name of organisms or groups identified
EM-63 | EM-84 | EM-113 | EM-178 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-943 | EM-970 | EM-971 | EM-999 | EM-1006 |
None Available | None Available |
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None Available |
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None Available |
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None Available |
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None Available | None Available | None Available |
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None Available | None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-63 | EM-84 | EM-113 | EM-178 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-943 | EM-970 | EM-971 | EM-999 | EM-1006 |
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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-63 | EM-84 | EM-113 | EM-178 | EM-303 |
EM-349 ![]() |
EM-397 ![]() |
EM-419 |
EM-422 ![]() |
EM-660 ![]() |
EM-667 ![]() |
EM-706 | EM-890 | EM-943 | EM-970 | EM-971 | EM-999 | EM-1006 |
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None |
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None |
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None |
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None |
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None | None |