{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	Application of existing model	New or revised model	EM-779	New or revised model	New or revised model	Continuation of trends
EM Short Name em.detail.shortNameHelp ?	Cultural ecosystem services, Bilbao, Spain	C Sequestration and De-N, Tampa Bay, FL, USA	Reef density of P. argus, St. Croix, USVI	P8 UCM	Arthropod flower preference, CA, USA	HWB-home value, Great Lakes, USA	EcoSim II - method	CommunityViz, Albany county, Wyoming
EM Full Name em.detail.fullNameHelp ?	Cultural ecosystem services, Bilbao, Spain	Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, 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	EcoSim II - method	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 ?	None ? Comment: EU Mapping Studies	US EPA	US EPA	*	None	*	None	*
EM Source Document ID	191	186	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	448	479 ? Comment: Published as a report by the University of Wyoming, but no record of peer review.
Document Author em.detail.documentAuthorHelp ?	Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H.	Russell, M. and Greening, H.	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	Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell	Lieske, S. N., Mullen, S., Knapp, M., & Hamerlinck, J. D.
Document Year em.detail.documentYearHelp ?	2013	2013	2014	2015	2018	None	2000	2003
Document Title em.detail.sourceIdHelp ?	Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning	Estimating benefits in a recovering estuary: Tampa Bay, Florida	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	Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II	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	Application of existing model	New or revised model	EM-779	New or revised model	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://ecopath.org/downloads/	https://communityviz.com/
Contact Name em.detail.contactNameHelp ?	Izaskun Casado-Arzuaga	M. Russell	Susan H. Yee	William Walker Jr., PhD	Ola Lundin	Ted Angradi	Carl Walters	Scott Lieske
Contact Address	Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain	US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA	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	Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4	Department of Agricultural & Applied Economics University of Wyoming, Laramie WY 82071
Contact Email	izaskun.casado@ehu.es	Russell.Marc@epamail.epa.gov	yee.susan@epa.gov	bill@wwwalker.net	ola.lundin@slu.se	tedangradi@gmail.com	c.walters@oceans.ubc.ca	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	Application of existing model	New or revised model	EM-779	New or revised model	New or revised model	Continuation of trends
Summary Description em.detail.summaryDescriptionHelp ?	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: "...we examine the change in the production of ecosystem goods produced as a result of restoration efforts and potential relative cost savings for the Tampa Bay community from seagrass expansion (more than 3,100 ha) and coastal marsh and mangrove restoration (∼600 ha), since 1990… The objectives of this article are to explore the roles that ecological processes and resulting ecosystem goods have in maintaining healthy estuarine systems by (1) quantifying the production of specific ecosystem goods in a subtropical estuarine system and (2) determining potential cost savings of improved water quality and increased habitat in a recovering estuary." (pp. 2)	ABSTRACT: "...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."	ABSTRACT: " EcoSim II uses results from the Ecopath procedure for trophic mass-balance analysis to define biomass dynamics models for predicting temporal change in exploited ecosystems. Key populations can be repre- sented in further detail by using delay-difference models to account for both biomass and numbers dynamics. A major problem revealed by linking the population and biomass dynamics models is in representation of population responses to changes in food supply; simple proportional growth and reproductive responses lead to unrealistic predic- tions of changes in mean body size with changes in fishing mortality. EcoSim II allows users to specify life history mechanisms to avoid such unrealistic predictions: animals may translate changes in feed- ing rate into changes in reproductive rather than growth rates, or they may translate changes in food availability into changes in foraging time that in turn affects predation risk. These options, along with model relationships for limits on prey availabil- ity caused by predation avoidance tactics, tend to cause strong compensatory responses in modeled populations. It is likely that such compensatory responses are responsible for our inability to find obvious correlations between interacting trophic components in fisheries time-series data. But Eco- sim II does not just predict strong compensatory responses: it also suggests that large piscivores may be vulnerable to delayed recruitment collapses caused by increases in prey species that are in turn competitors/predators of juvenile piscivores "	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 ?	Land management, ecosystem management, response to EU 2020 Biodiversity Strategy	Restoration of seagrass	None identified	None identified	None reported	None identified	None	None provided
Biophysical Context	Northern Spain; Bizkaia region	Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove	No additional description provided	Urban setting	Mediteranean climate	Waterfront districts on south Lake Michigan and south lake Erie	None, Ocean ecosystems	Groundwater recharge area, City of Laramie
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	Habitat loss or restoration in Tampa Bay Estuary	No scenarios presented	N/A	Arthropod groups	N/A	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	Application of existing model	New or revised model	EM-779	New or revised model	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 Only	Method + Application (multiple runs exist)	Method + Application	Method Only	Model Run Associated with a Specific EM Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	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	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	Application of existing model	New or revised model	EM-779	New or revised model	New or revised model	Continuation of trends
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	None	None	None	None	None	Doc-422	None	Doc-473
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	None	None	None	None	EM-886 \| EM-889 \| EM-890 \| EM-891 \| EM-893 \| EM-894 \| EM-895	EM-1055	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	Application of existing model	New or revised model	EM-779	New or revised model	New or revised model	Continuation of trends
EM Temporal Extent em.detail.tempExtentHelp ?	2000 - 2007	1982-2010	2006-2007, 2010	Not applicable	2015-2016	2022	Not applicable	2050
EM Time Dependence em.detail.timeDependencyHelp ?	*	*	*	time-dependent	time-stationary	*	time-dependent	*
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	*	*	*	*	Not applicable	*	both	*
EM Time Continuity em.detail.continueDiscreteHelp ?	*	*	*	discrete	Not applicable	*	discrete ? Comment: Modeller dependent	*
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	*	*	*	1	Not applicable	*	1	*
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	*	*	*	Hour	Not applicable	*	Day	*

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	Application of existing model	New or revised model	EM-779	New or revised model	New or revised model	Continuation of trends
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or Ecological	Physiographic or ecological	Not applicable	Point or points	Geopolitical	Other	Watershed/Catchment/HUC
Spatial Extent Name em.detail.extentNameHelp ?	Bilbao Metropolitan Greenbelt	Tampa Bay Estuary	Coastal zone surrounding St. Croix	Not applicable	Harry Laidlaw Jr. Honey Bee Research facility	Great Lakes waterfront	Not applicable	Laramie City's aquifer protection area
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	100-1000 km^2	1000-10,000 km^2.	100-1000 km^2	Not applicable	<1 ha	1000-10,000 km^2.	Not applicable	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	Application of existing model	New or revised model	EM-779	New or revised model	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 ?	2 m x 2 m	1 ha	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	Application of existing model	New or revised model	EM-779	New or revised model	New or revised model	Continuation of trends
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	*	Numeric	*	Analytic	*
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	Application of existing model	New or revised model	EM-779	New or revised model	New or revised model	Continuation of trends
Model Calibration Reported? em.detail.calibrationHelp ?	No	Yes	Yes	Yes	Not applicable	No	No	Unclear
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	No	*	Not applicable	No	No	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	*	*	*	*	None	*	*	*
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	No	Yes	*	Not applicable	No	*	Unclear
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	*	*	*	Not applicable	No	*	Not applicable	Unclear
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	*	*	*	Not applicable	No	Yes	Not applicable	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	Application of existing model	New or revised model	EM-779	New or revised model	New or revised model	Continuation of trends
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	Application of existing model	New or revised model	EM-779	New or revised model	New or revised model	Continuation of trends
*	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Floridian	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	Application of existing model	New or revised model	EM-779	New or revised model	New or revised model	Continuation of trends
Centroid Latitude em.detail.ddLatHelp ?	43.25	27.95	17.73	Not applicable	38.54	42.26	Not applicable	41.31
Centroid Longitude em.detail.ddLongHelp ?	-2.92	-82.47	-64.77	Not applicable	-121.79	-87.84	Not applicable	-105.46
Centroid Datum em.detail.datumHelp ?	*	*	*	Not applicable	WGS84	*	Not applicable	*
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	*	Estimated	Estimated	Not applicable	Provided	Estimated	Not applicable	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	Application of existing model	New or revised model	EM-779	New or revised model	New or revised model	Continuation of trends
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	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)	Open Ocean and Seas	Ground Water \| Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	none	Subtropical Estuary	Coral reefs	Urban catchments	Agricultural fields	Lake Michigan waterfront	Pelagic	watershed
EM Ecological Scale em.detail.ecoScaleHelp ?	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	Application of existing model	New or revised model	EM-779	New or revised model	New or revised model	Continuation of trends
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Species	Not applicable	Guild or Assemblage	Not applicable	Other (Comment) ? Comment: Varied levels of taxonomic order	Not applicable

Taxonomic level and name of organisms or groups identified

New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	New or revised model	Continuation of trends
*	*	Species Panulirus argus	*	other Honey bees Predators Wild bees Herbivores Parasitic wasps	*	other Whiting shrimp Whales Seals Cod Phytoplankton Mackerel Benthos Zooplankton Anchovy	*

EnviroAtlas URL

New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	New or revised model	Continuation of trends
Percent IUCN Status II, Percent GAP Status 1 & 2	Carbon Storage by Tree Biomass	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	Big game hunting recreation demand	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	Application of existing model	New or revised model	EM-779	New or revised model	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	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations Mediation of waste, toxics and other nuisances Mediation by biota Bio-remediation by micro-organisms, algae, plants, and animals Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals	[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	*	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs [Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[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	Application of existing model	New or revised model	EM-779	New or revised model	New or revised model	Continuation of trends
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) Water (3) Liquid water Quality Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator	*	Agroecosystems (22) Fauna (4) Pollinators Stock Indicator	*	Open Oceans and Seas (114) Fauna (4) Fish Flow 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	Application of existing model	New or revised model	EM-779	New or revised model	New or revised model	Continuation of trends
Driving Variables (and Units) em.detail.drivingVariableHelp ?	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)	Habitat type (Not applicable)	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 ($) Percent of particulate matter >2.5 microns and < 10 microns removed annually by trees in the census tract (%) Percent tree cover in the census tract (%)	Adult class (Not applicable) Adult food consumption (Not reported) Biomass immigration rate (Not reported) Emigration rate (Not reported) Fishing mortality rate (Not reported) Fleet size dynamics (unitless) Group type (Not applicable) Juvenile body weight (Not reported) Juvenile class (unitless) Net growth efficiency (Not reported) Non predation mortality/metabolic rate (Not reported) Number of adults (No.) Number of juveniles age a (No.) Number of juveniles at age 0 produced at month t (No.) Number of juveniles at the start of month (No.) Predator biomass j (Not reported) Predator effective search rate (unitless) Prey biomass I (Not reported) Total food consumption by juveniles (Not reported)	Albany county study area boundary (Not reported) City annual budget data ($) 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 ($) Proportion of land development to open space seen from observation points (NA) Roads/infrastructure (Not reported) Species distribution GIS data (Not reported) Topography (Not reported) Vehicle miles traveled (miles) Water consumption data (millions of gallons) Water quality data (nitrate levels) (ppm)
Constant or Factor Variables (and Units) em.detail.constantFactorVariableHelp ?	None	Carbon sequestration rate (g C m^-2 yr^-1) Cost to remove nitrogen ($ kg^-1) Denitrification rate (g N m^-2 y^-1) Social cost of carbon ($ ton^-1 CO_2)	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)	Age at graduation to the adult pool (months) Density dependent catchability (unitless) Feeding time adjust rate [0,1] (unitless) Fraction of other mortality sensitive to changes in feeding time (unitless) Juvenile growth efficiency (unitless) Juvenile growth rate ( not reported) Maximum relative Production/Biomass (P/B) (unitless) Maximum relative feeding time (Not reported) Predator effect on feeding time [0,1] (unitless) Prey vulnerability parameter 1 (unitless) Prey vulnerability parameter 2 (unitless) QBmax/QBo (for handing time) [>1] (unitless) Slope (metabolism) (Not reported) Switching power parameter [0,2] (unitless) von Bertalanffy growth parameter (unitless)	*

Intermediate

EM ID em.detail.idHelp ?	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	New or revised model	Continuation of trends
Intermediate (Computed) Variables (and Units) em.detail.intermediateVariableHelp ?	None	None	Fraction of area within each grid cell for each habitat type (%)	Particle load removal (ppm/unit time) Runoff (in./hr)	None	None	Adult biomass (kg) Total mortality rate of adult pool (Not reported) Total mortality rate of juvenile pool (not reported) Trophic flow (biomass/time)	*

Response

EM ID em.detail.idHelp ?	New or revised model	New or revised model	Application of existing model	New or revised model	EM-779	New or revised model	New or revised model	Continuation of trends
Observed Response Variables (and Units) em.detail.observedResponseHelp ?	None	None	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	None	*
Computed Response Variables (and Units) em.detail.computedResponseHelp ?	Aesthetic quality of the landscape (Not applicable) Recreation provision index (Not applicable)	Total value of carbon sequestration (Thousands US$ yr^-1) Total value of denitrification (Millions US $ yr^-1)	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 ($)	Biomass change rate (kg/time)	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

Comment:

Comment:

Comment:

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

EM Modeling Approach

Comment:

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

Comment:

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