{PAGE NAME} | {WEB AREA NAME}

EM Identity and Description

EM Identification (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

EM ID em.detail.idHelp ?	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
EM Short Name em.detail.shortNameHelp ?	Green biomass production, Central French Alps	Cultural ecosystem services, Bilbao, Spain	Coral taxa and land development, St.Croix, VI, USA	Reef density of P. argus, St. Croix, USVI	P8 UCM	Arthropod flower preference, CA, USA	HWB-home value, Great Lakes, USA	CommunityViz, Albany county, Wyoming
EM Full Name em.detail.fullNameHelp ?	Green biomass production, Central French Alps	Cultural ecosystem services, Bilbao, Spain	Coral taxa richness and land development, St.Croix, Virgin Islands, USA	Relative density of Panulirus argus (on reef), St. Croix, USVI	P8 Urban Catchment model method	Arthropod flower type preference, California, USA	Human well being indicator-home value, Great Lakes waterfront, USA	Wyoming Community Viz TM Partnership Phase I Pilot: Aquifer Protection and Community Viz TM in Albany County, Wyoming.
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	None ? Comment: EU Mapping Studies	US EPA	US EPA	*	None	*	*
EM Source Document ID	260	191	96	335	377 ? Comment: Published to the web. Previously versions prepared for EPA.	399	422 ? Comment: Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers	479 ? Comment: Published as a report by the University of Wyoming, but no record of peer review.
Document Author em.detail.documentAuthorHelp ?	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H.	Oliver, L. M., Lehrter, J. C. and Fisher, W. S.	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Walker, W. Jr., and J.D. Walker	Lundin, O., Ward, K.L., and N.M. Williams	Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick	Lieske, S. N., Mullen, S., Knapp, M., & Hamerlinck, J. D.
Document Year em.detail.documentYearHelp ?	2011	2013	2011	2014	2015	2018	None	2003
Document Title em.detail.sourceIdHelp ?	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning	Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	P8 Urban Catchment Model Version 3.5	Indentifying native plants for coordinated hanbitat manegement of arthroppod pollinators, herbivores and natural enemies	Human well-being and natural capital indictors for Great Lakes waterfront revitalization	Wyoming Community Viz TM Partnership Phase I Pilot: Aquifer Protection and Community Viz TM in Albany County, Wyoming
Document Status em.detail.statusCategoryHelp ?	*	*	*	*	Not peer reviewed but is published (explain in Comment)	Peer reviewed and published	Peer reviewed but unpublished (explain in Comment)	Not peer reviewed but is published (explain in Comment)
Comments on Status em.detail.commentsOnStatusHelp ?	*	*	*	*	Published report	Published journal manuscript	Journal manuscript submitted or in review	Published report

Software and Access (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

EM ID em.detail.idHelp ?	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	*	*	*	*	http://www.wwwalker.net/p8/v35/webhelp/splash.htm	Not applicable	*	https://communityviz.com/
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Izaskun Casado-Arzuaga	Leah Oliver	Susan H. Yee	William Walker Jr., PhD	Ola Lundin	Ted Angradi	Scott Lieske
Contact Address	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain	National Health and Environmental Research Effects Laboratory	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	Concord, Massachusetts	Department of Ecology, Swedish Univ. of Agricultural Sciences, Uppsala, Sweden	USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804	Department of Agricultural & Applied Economics University of Wyoming, Laramie WY 82071
Contact Email	sandra.lavorel@ujf-grenoble.fr	izaskun.casado@ehu.es	leah.oliver@epa.gov	yee.susan@epa.gov	bill@wwwalker.net	ola.lundin@slu.se	tedangradi@gmail.com	lieske@uwyo.edu

EM Description (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

EM ID em.detail.idHelp ?	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
Summary Description em.detail.summaryDescriptionHelp ?	ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)."	ABSTRACT "This paper presents a method to quantify cultural ecosystem services (ES) and their spatial distribution in the landscape based on ecological structure and social evaluation approaches. The method aims to provide quantified assessments of ES to support land use planning decisions. A GIS-based approach was used to estimate and map the provision of recreation and aesthetic services supplied by ecosystems in a peri-urban area located in the Basque Country, northern Spain. Data of two different public participation processes (frequency of visits to 25 different sites within the study area and aesthetic value of different landscape units) were used to validate the maps. Three maps were obtained as results: a map showing the provision of recreation services, an aesthetic value map and a map of the correspondences and differences between both services. The data obtained in the participation processes were found useful for the validation of the maps. A weak spatial correlation was found between aesthetic quality and recreation provision services, with an overlap of the highest values for both services only in 7.2 % of the area. A consultation with decision-makers indicated that the results were considered useful to identify areas that can be targeted for improvement of landscape and recreation management."	AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294)	ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j: (1) density of the spiny lobster Panulirus argus"	Author description: " P8 simulates the generation and transport of stormwater runoff pollutants in urban watersheds. Continuous water-balance and mass-balance calculations are performed on a user-defined drainage system consisting of the following elements: - Watersheds (<= 250 nonpoint source areas) - Devices (<=75 runoff storage/treatment areas or BMP's) - Particles (<= 5 fractions with different settling velocities) - Water Quality Components (<= 10 associated with particles) Simulations are driven by hourly precipitation and daily air temperature time series. Runoff contributions from snowmelt are also simulated. 'P8' abbreviates "Program for Predicting Polluting Particle Passage Thru Pits, Puddles, and Ponds", which more or less captures the basic features and functions of the model. It has been developed for use by engineers and planners in designing and evaluating runoff treatment schemes for existing or proposed urban developments. Design objectives are typically expressed in terms of percentage reduction in suspended solids or other water quality component. Despite its limitations, P8 has been used by state and local regulatory agencies as a consistent framework for evaluating proposed developments. Depending on applications, other models could be either too simple (easily used, but ignoring important factors) or too complex (requiring considerable site-specific data and/or user expertise). P8 attempts to strike a balance to between those extremes. Predicted water quality components include total suspended solids (sum of the individual particle fractions), total phosphorus, total Kjeldahl nitrogen, copper, lead, zinc, and total hydrocarbons. Simulated BMP types include detention ponds (wet, dry, extended), infiltration basins, swales, buffer strips, or other devices with user-specified stage/discharge curves and infiltration rates. A simple water budget algorithm can be used to estimate groundwater storage and stream base flow in watershed-scale applications. Initial calibrations were based upon runoff quality and particle settling velocity data collected under the EPA's Nationwide Urban Runoff Program (Athayede et al., 1983). Calibrations to impervious area runoff parameters for Wisconsin watersheds have been subsequently developed. Inputs are structured in terms which should be familiar to planners and engineers involved in hydrologic evaluation. Several tabular and graphic output formats are provided. "	ABSTRACT: " Plant species differed in attractiveness for each arthropod functional group. Floral area of the focal plant species positively affected honeybee, predator, and parasitic wasp attractiveness. Later bloom period was associated with lower numbers of parasitic wasps. Flower type (actinomorphic, composite, or zygomorphic) predicted attractiveness for honeybees, which preferred actinomorphic over composite flowers and for parasitic wasps, which preferred composite flowers over actinomorphic flowers. 4. Across plant species, herbivore, predator, and parasitic wasp abundances were positively correlated, and honeybee abundance correlated negatively to herbivore abundance. 5. Synthesis and applications. We use data from our common garden experiment to inform evidence-based selection of plants that support pollinators and natural enemies without enhancing potential pests. We recommend selecting plant species with a high floral area per ground area unit, as this metric predicts the abundances of several groups of beneficial arthropods. Multiple correlations between functionally important arthropod groups across plant species stress the importance of a multifunctional approach to arthropod habitat management. " Changes in arthropod abundance were estimated for flower type (entered as separate runs); Actinomorphic, Composite, Zygomorphic. 43 plant species evaluated included Amsinckia intermedia, Calandrinia menziesii, Nemophila maculata, Nemophila menziesii, Phacelia ciliata, Achillea millefolium, Collinsia heterophylla, Fagopyrum esculentum, Lasthenia fremontii, Lasthenia glabrata, Limnanthes alba, Lupinus microcarpus densiflorus, Lupinus succelentus, Phacelia californica, Phacelia campanularia, Phacelia tanacetifolia, Salvia columbariae, Sphaeralcea ambigua, Trifolium fucatum, Trifolium gracilentum, Antirrhinum conutum, Clarkia purpurea, Clarkia unguiculata, Clarkia williamsonii, Eriophyllum lanatum, Eschscholzia californica, Monardella villosa, Scrophularia californica, Asclepia eriocarpa, Asclepia fascicularis, Camissoniopsis Cheiranthifolia, Eriogonum fasciculatum, Gilia capitata, Grindelia camporum, Helianthus annuus, Lupinus formosus, Malacothrix saxatilis, Oenothera elata, Helianthus bolanderi, Helianthus californicus, Madia elegans, Trichostema lanceolatum, Heterotheca grandiflora."	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."	The Wyoming Community VizTM Partnership was established in 2001 to promote the use of geographic information system-based planning support systems and related decision support technologies in community land-use planning and economic development activities in the State of Wyoming. Partnership members include several state agencies, local governments and several nongovernment organizations. Partnership coordination is provided by the Wyoming Rural Development Council. Research and technical support is coordinated by the Wyoming Geographic Information Science Center’s Spatial Decision Support System Research Program at the University of Wyoming. In June 2002, the Partnership initiated a three-phase plan to promote Community VizTM based planning support systems in Wyoming. Phase I of the Partnership plan was a “proof of concept” pilot project set in Albany County in southeastern Wyoming. The goal of the project was to demonstrate the application of Community VizTM to a Wyoming-specific issue (in this case, aquifer protection) and to determine potential challenges for broader adoption in terms of data requirements, computing infrastructure and technological expertise. The results of the Phase I pilot project are detailed in this report. Efforts are currently underway to secure funding for Phase II of the plan, which expands the use of Community VizTM into four additional Wyoming communities. Specific Phase II objectives are to expand the type and number of issues addressed by Community VizTM and increase the use of Community VizTM in the planning process. As a part of Phase II the Partnership will create a technical assistance network aimed at assisting communities with the technical challenges in applying the software to their planning issues. The third phase will expand the program to more communities in the state, maintain the technical assistance network, and monitor the impact of Community VizTM on the planning process.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	Land management, ecosystem management, response to EU 2020 Biodiversity Strategy	Not applicable	None identified	None identified	None reported	None identified	None provided
Biophysical Context	Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes	Northern Spain; Bizkaia region	nearshore; <1.5 km offshore; <12 m depth	No additional description provided	Urban setting	Mediteranean climate	Waterfront districts on south Lake Michigan and south lake Erie	Groundwater recharge area, City of Laramie
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	Not applicable	No scenarios presented	N/A	Arthropod groups	N/A	Continuation of trends

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application	Method Only	Method + Application (multiple runs exist)	Method + Application	Model Run Associated with a Specific EM Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	New or revised model	New or revised model	Continuation of trends

Related EMs (for example, other versions or derivations of this EM) described in ESML

EM ID em.detail.idHelp ?	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260	None	None	None	None	None	Doc-422	Doc-473
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-71 \| EM-79 \| EM-80 \| EM-81 \| EM-82 \| EM-83	None	None	None	None	None	EM-886 \| EM-889 \| EM-890 \| EM-891 \| EM-893 \| EM-894 \| EM-895	None

EM Modeling Approach

EM Relationship to Time (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

EM ID em.detail.idHelp ?	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
EM Temporal Extent em.detail.tempExtentHelp ?	2007-2009	2000 - 2007	2006-2007	2006-2007, 2010	Not applicable	2015-2016	2022	2050
EM Time Dependence em.detail.timeDependencyHelp ?	*	*	*	*	time-dependent	time-stationary	*	*
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	*	*	*	*	*	Not applicable	*	*
EM Time Continuity em.detail.continueDiscreteHelp ?	*	*	*	*	discrete	Not applicable	*	*
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	*	*	*	*	1	Not applicable	*	*
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	*	*	*	*	Hour	Not applicable	*	*

EM spatial extent (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

EM ID em.detail.idHelp ?	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Geopolitical	Physiographic or Ecological	Physiographic or ecological	Not applicable	Point or points	Geopolitical	Watershed/Catchment/HUC
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	Bilbao Metropolitan Greenbelt	St.Croix, U.S. Virgin Islands	Coastal zone surrounding St. Croix	Not applicable	Harry Laidlaw Jr. Honey Bee Research facility	Great Lakes waterfront	Laramie City's aquifer protection area
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	100-1000 km^2	10-100 km^2	100-1000 km^2	Not applicable	<1 ha	1000-10,000 km^2.	10-100 km^2

Spatial Distribution of Computations (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

EM ID em.detail.idHelp ?	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
EM Spatial Distribution em.detail.distributeLumpHelp ?	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)	*	*
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	area, for pixel or radial feature	*	area, for pixel or radial feature	*	Not applicable	*	*
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	2 m x 2 m	*	10 m x 10 m	*	Not applicable	*	*

EM Structure and Computation Approach (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

EM ID em.detail.idHelp ?	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	*	Numeric	*	*
EM Determinism em.detail.deterStochHelp ?	*	*	*	*	*	deterministic	*	*
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	*	None	*	*	*	Conventional (frequentist) statistics	*	*

Model Checking Procedures Used (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

EM ID em.detail.idHelp ?	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	Yes	Yes	Yes	Not applicable	No	Unclear
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	No	Yes	No	*	Not applicable	No	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	Adjusted R \| 69.9 \| Not applicable	*	R squared (taxa richness) \| 0.74 \| Not applicable p-value \| 0.0020 \| unitless	*	*	None	*	*
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	Yes	No	Yes	*	Not applicable	No	Unclear
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	*	*	Yes	*	Not applicable	No	*	Unclear
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	*	*	*	*	Not applicable	No	Yes	Unclear
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	*	*	*	*	*	Not applicable	*	*

EM Locations, Environments, Ecology

Location of EM Application (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])

New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
Europe France	Europe Spain	*	*	*	North America United States California	North America United States Illinois Minnesota Ohio Wisconsin	North America United States Wyoming

Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)

New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
*	*	Tropical Atlantic West Tropical Atlantic Tropical Southwestern Atlantic	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	*	None	*	*

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
Centroid Latitude em.detail.ddLatHelp ?	45.05	43.25	17.75	17.73	Not applicable	38.54	42.26	41.31
Centroid Longitude em.detail.ddLongHelp ?	6.4	-2.92	-64.75	-64.77	Not applicable	-121.79	-87.84	-105.46
Centroid Datum em.detail.datumHelp ?	*	*	NAD83	*	Not applicable	WGS84	*	*
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	*	*	Estimated	Estimated	Not applicable	Provided	Estimated	Estimated

Environments and Scales Modeled (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

EM ID em.detail.idHelp ?	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified) \| Forests \| Agroecosystems \| Created Greenspace \| Grasslands \| Scrubland/Shrubland	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine	Terrestrial Environment (sub-classes not fully specified)	Agroecosystems	Terrestrial Environment (sub-classes not fully specified)	Ground Water \| Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows	none	stony coral reef	Coral reefs	Urban catchments	Agricultural fields	Lake Michigan waterfront	watershed
EM Ecological Scale em.detail.ecoScaleHelp ?	Not applicable	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	*	*

Organisms modeled (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Not applicable	*	Species	Not applicable	Guild or Assemblage	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
*	*	Genus Acropora Porites spp. Diploria spp. Species Mycetophylia lamarckiana Mycetophylia ferox Montastrea cavernosa Dichocoenia stokesii Meandrina meandrites Agaricia tenuifolia Agaricia fragilis Montastrea annularis Agaricia agaricites Dendrogyra cylindricus Millepora complanata Montastrea faveolata Porites astreoides Montastrea franksii	Species Panulirus argus	*	other Honey bees Predators Wild bees Herbivores Parasitic wasps	*	*

EnviroAtlas URL

New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
GAP Ecological Systems	Percent IUCN Status II, Percent GAP Status 1 & 2	None Available	None Available	Average Annual Precipitation, Total Annual Reduced Nitrogen Deposition, The Watershed Boundary Dataset (WBD), Percent Impervious Area, Water supply from NID reservoirs (million gallons)	GAP Ecological Systems	Enabling Conditions	Dasymetric Allocation of Population, Total Annual Reduced Nitrogen Deposition, Employment Rate

EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system

* Note that run information is shown only where run data differ from the "parent" entry shown at left

CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)

New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
*	[Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Physical use of land-/seascapes in different environmental settings Experiential use of plants, animals and land-/seascapes in different environmental settings Intellectual and representative interactions Aesthetic	[Non-ecosystem] Regulation & Maintenance by natural physical structures and processes Maintenance of physical, chemical, abiotic conditions By natural chemical and physical processes Not applicable	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[Ecosystem] Regulation & Maintenance Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal	*	[Non-ecosystem] Regulation & Maintenance by natural physical structures and processes Mediation of waste, toxics and other nuisances By natural chemical and physical processes Not applicable [Ecosystem] Provisioning Materials Water Ground water for non-drinking purposes [Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Physical use of land-/seascapes in different environmental settings Intellectual and representative interactions Aesthetic

National Ecosystem Services Classification System (NESCS) Plus

(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)

New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
Grasslands (23) Flora (5) Green biomass Stock Indicator Agroecosystems (22) Flora (5) Green biomass Stock Indicator	Near Coastal Marine/Estuarine (113) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scapes: views, sounds and scents of land, sea, sky or a combination Site Characteristic Indicator Forests (21) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scapes: views, sounds and scents of land, sea, sky or a combination Site Characteristic Indicator Grasslands (23) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scapes: views, sounds and scents of land, sea, sky or a combination Site Characteristic Indicator Scrubland/Shrubland (24) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scapes: views, sounds and scents of land, sea, sky or a combination Site Characteristic Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Quality Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator	*	Agroecosystems (22) Fauna (4) Pollinators Stock Indicator	*	Groundwater Water (3) Liquid water Flow Indicator

EM Variable Names (and Units)

* Note that for runs, variable name is displayed only where data for that variable differed by run AND those differences were reported in the source document. Where differences occurred but were not reported, the variable is not listed. Click on variable name to view details.

To view run information for variables, select any variable below or view run data for all variables.

Predictor

EM ID em.detail.idHelp ?	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
Driving Variables (and Units) em.detail.drivingVariableHelp ?	Altitude (m) Combination of legacy and current land use (Not applicable)	Climbing sites mapped location (Not applicable) Coastline mapped location (Not applicable) Cycling paths mapped location (Not applicable) Density of mountain summits (km^-2) Elevation (m) Land use/land cover class (Not applicable) Natural protected areas, mapped location (Not applicable) Perceived value of different landscape units (Not applicable) Quality of bathing water (Not applicable) Recreational areas, mapped location (Not applicable) Routes of geological interest (Not applicable)	Land development intensity index (LDI) (Not applicable) Percent of land use classes in watersheds with a land development intensity index =1.0 (%) Station depth (meters)	Habitat type class (Not applicable)	Area (acres) Daily air temperature (Not reported) Device class (Not applicable) Elevation (ft) Hourly precipitation (in./hr) Impervious depression storage ( not reported) Land use (Not applicable) Length (ft) Outflow (exfiltration/overflow) (Not reported) Outlet configuration Roughness (overland flow areas) (acres) Slope (%) Street sweeper efficiency (Not reported) Volume (acre-ft) Watershed boundary (Not applicable) Watershed total area (acres) Width (ft)	None	Average walking distance to park entrance in census tract (m) Impervious surface per capita in census tract (m^2/person) Median census tract household income in 2019 ($) Percent of particulate matter >2.5 microns and < 10 microns removed annually by trees in the census tract (%) Percent tree cover in the census tract (%)	Albany county study area boundary (Not reported) City annual budget data ($) Common land use list (Not reported) Distances from residential parcels to recreational trail access (miles) Existing development (Not reported) Existing land use or zoning data (Not reported) Guidelines for future land use/zoning (Not reported) Number of vehicle trips taken (trips) Population projections (Not reported) Property, sales, and lodging tax data ($) Proportion of land development to open space seen from observation points (NA) Roads/infrastructure (Not reported) Species distribution GIS data (Not reported) Topography (Not reported) Vehicle miles traveled (miles) Water consumption data (millions of gallons) Water quality data (nitrate levels) (ppm)
Constant or Factor Variables (and Units) em.detail.constantFactorVariableHelp ?	Coefficient on altitude for community-weighted mean leaf nitrogen concentration (unitless) Coefficient on altitude for soil water holding capacity (unitless) Coefficient on combination of legacy and current land use for nitrogen nutrition index (unitless) Coefficient on community weighted mean leaf nitrogen concentration for green biomass production (unitless) Coefficient on community weighted mean vegetative height for green biomass production (unitless) Coefficient on land use for community-weighted mean vegetative height (unitless) Coefficient on land use for soil water holding capacity (unitless) Coefficient on nitrogen nutrition index for community-weighted mean vegetative height (unitless) Coefficient on soil water holding capacity for green biomass production (unitless) Coefficient on soil water holding capacity for nitrogen nutrition index (unitless)	None	Coefficient on percent of land use classes with a LDI = 1.0 (Not applicable) Coefficient on station depth (Not applicable)	Relative magnitude of density of Panulirus argus for each habitat type (Not applicable)	Accumulation rate (impervious surface) (lbs./acre day) Copper (ppm) Decay rate - second order (particle) (Not reported) Decay rate -first order (particle) (Not reported) Direct impervious fraction (%) Directly connected impervious coefficient (0-1) (unitless) Directly connected impervious fraction (%) Filtration efficiency (particle removal) (%) Hydrocarbons (ppm) Hydrologic condition (Not applicable) Indirect Impervious fraction (swept) (%) Indirect impervious fraction (not swept) (%) Infiltration outlet (y/n) (Not applicable) Infiltration rate (in./hr) Lead (ppm) Normal outlet (y/n) (Not applicable) Particle decay rate (non runoff process) (Not reported) Percent runoff exponent (particle) (Not reported) Pervious (percent) (%) Pervious area (acres) Pervious runoff concentration (particle) (ppm) Removes particles (y/n) (Not applicable) Runoff curve number (pervious portion) (unitless) Settling velocity (ft/hr) Spillway/overflow (y/n) (Not applicable) Street sweeping frequency (times/week) Sweeper efficiency (particle) (%) Total Kjeldahl nitrogen (ppm) Total phosphorus (ppm) Total suspended solids (ppm) Washoff coefficient (particle) (unitless) Washoff exponent (particle) (unitless) Zinc (ppm)	Flower type (Not applicable)	Average walking distance to park entrance in census tract constant (unitless) Impervious surface per capita in census tract constant (unitless) Median censustract household income in 2019 constant (unitless) Percent of particulate matter >2.5 microns and < 10 microns removed annually by trees in the census tract constant (unitless) Percent tree cover in the census tract constant (unitless)	*

Intermediate

EM ID em.detail.idHelp ?	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
Intermediate (Computed) Variables (and Units) em.detail.intermediateVariableHelp ?	Community-weighted mean leaf nitrogen concentration (mg/g) Community-weighted mean vegetative height (cm) Nitrogen nutrition index (unitless) Soil water holding capacity (%)	None	None	Fraction of area within each grid cell for each habitat type (%)	Particle load removal (ppm/unit time) Runoff (in./hr)	None	None	*

Response

EM ID em.detail.idHelp ?	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	Continuation of trends
Observed Response Variables (and Units) em.detail.observedResponseHelp ?	Green biomass (observed) (g/m^2)	None	Coral taxa richness (No. taxa m^-2)	None	None	Herbivores (No. ind./30 sec.) Honey bees (No. ind./60 sec.) Parasitic wasps (No. ind./30 sec.) Predators (No. ind./30 sec.) Wild bees (No. ind./60 sec.)	None	*
Computed Response Variables (and Units) em.detail.computedResponseHelp ?	Green biomass (modeled) (g/m^2)	Aesthetic quality of the landscape (Not applicable) Recreation provision index (Not applicable)	None	Relative density of Panulirus argus (unitless)	Copper removal (lbs/unit time) Copper removal efficiency (%) Lead removal (lbs/unit time) Lead removal efficiency (%) Total kjeldahl nitrogen removal efficiency (%) Total kjelhahl nitrogen removal (lbs/unit time) Total phosphorus removal (lbs/unit time) Zinc removal (lbs/unit time) Zinc removal efficiency (%)	None	Predicted median censustract home value in 2019 ($)	Cost of community services (millions of dollars) Local tax revenues (millions of dollars) Quantity of water (millions of gallons) Recreation access (Miles to trail) Riparian area impacted (Acres) Traffic impacts (Vehicle miles traveled) Visual impact (Not reported) Water quality (nitrate levels) (ppm) Wildlife habitat (acres)

US EPA

EcoService Models Library (ESML)

View Runs

: Arthropod flower type preference, California, USA (EM-779)

EM Identity and Description

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Related EMs (for example, other versions or derivations of this EM) described in ESML

EM Modeling Approach

EM Locations, Environments, Ecology

Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])

Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)

Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

Taxonomic level and name of organisms or groups identified

EnviroAtlas URL

EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system

* Note that run information is shown only where run data differ from the "parent" entry shown at left

CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)

National Ecosystem Services Classification System (NESCS) Plus

(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)

EM Variable Names (and Units)

* Note that for runs, variable name is displayed only where data for that variable differed by run AND those differences were reported in the source document. Where differences occurred but were not reported, the variable is not listed. Click on variable name to view details.

Predictor

Intermediate

Response