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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	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	Continuation of trends
EM Short Name em.detail.shortNameHelp ?	Community flowering date, Central French Alps	Soil carbon and plant traits, Central French Alps	Cultural ecosystem services, Bilbao, Spain	Coral taxa and land development, St.Croix, VI, USA	Erosion prevention by vegetation, Portel, Portugal	SAV occurrence, St. Louis River, MN/WI, USA	Mangrove connectivity, St. Croix, USVI	EnviroAtlas - Crops with no pollinator habitat	SLAMM, Tampa Bay, FL, USA	CommunityViz, Albany county, Wyoming
EM Full Name em.detail.fullNameHelp ?	Community weighted mean flowering date, Central French Alps	Soil carbon potential estimated from plant functional traits, Central French Alps	Cultural ecosystem services, Bilbao, Spain	Coral taxa richness and land development, St.Croix, Virgin Islands, USA	Soil erosion prevention provided by vegetation cover, Portel municipality, Portugal	Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA	Mangrove connectivity (of reef), St. Croix, USVI	US EPA EnviroAtlas - Acres of pollinated crops with no nearby pollinator habitat, USA	SLAMM (sea level affecting marshes model), Tampa Bay, Florida, 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	EU Biodiversity Action 5	* ? Comment: EU Mapping Studies	US EPA	EU Biodiversity Action 5	US EPA	US EPA	US EPA \| EnviroAtlas	None	*
EM Source Document ID	260	260	191	96	281	330	335	262	415 ? Comment: Secondary sources: Documents 412 and 413.	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.	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.	Guerra, C.A., Pinto-Correia, T., Metzger, M.J.	Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke	Yee, S. H., Dittmar, J. A., and L. M. Oliver	US EPA Office of Research and Development - National Exposure Research Laboratory	Sherwood, E. T. and H. S. Greening	Lieske, S. N., Mullen, S., Knapp, M., & Hamerlinck, J. D.
Document Year em.detail.documentYearHelp ?	2011	2011	2013	2011	*	2013	*	2013	2014	2003
Document Title em.detail.sourceIdHelp ?	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	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	Mapping soil erosion prevention using an ecosystem service modeling framework for integrated land management and policy	Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	EnviroAtlas - National	Potential impacts and management implications of climate change on Tampa Bay estuary critical coastal habitats	Wyoming Community Viz TM Partnership Phase I Pilot: Aquifer Protection and Community Viz TM in Albany County, Wyoming
Document Status em.detail.statusCategoryHelp ?	*	*	*	*	*	*	*	*	Peer reviewed and published	Not peer reviewed but is published (explain in Comment)
Comments on Status em.detail.commentsOnStatusHelp ?	*	*	*	*	*	*	*	Published on US EPA EnviroAtlas website	Published journal manuscript	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	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	Continuation of trends
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	https://www.epa.gov/enviroatlas	http://warrenpinnacle.com/prof/SLAMM/index.html com/prof/SLAMM/index.html	https://communityviz.com/
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Sandra Lavorel	Izaskun Casado-Arzuaga	Leah Oliver	Carlos A. Guerra	Ted R. Angradi	Susan H. Yee	EnviroAtlas Team	Edward T. Sherwood	Scott Lieske
Contact Address	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	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	Instituto de Ciências Agrárias e Ambientais Mediterrânicas, Universidade de Évora, Pólo da Mitra, Apartado 94, 7002-554 Évora, Portugal	U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	Not reported	Tampa Bay Estuary Program, 263 13th Avenue South, St. Petersburg, FL 33701, USA	Department of Agricultural & Applied Economics University of Wyoming, Laramie WY 82071
Contact Email	sandra.lavorel@ujf-grenoble.fr	sandra.lavorel@ujf-grenoble.fr	izaskun.casado@ehu.es	leah.oliver@epa.gov	cguerra@uevora.pt	angradi.theodore@epa.gov	yee.susan@epa.gov	enviroatlas@epa.gov	esherwood@tbep.org	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	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	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." AUTHOR'S DESCRIPTION: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables."	ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The soil carbon ecosystem service map was a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to the soil carbon ecosystem service are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions."	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 present an integrative conceptual framework to estimate the provision of soil erosion prevention (SEP) by combining the structural impact of soil erosion and the social–ecological processes that allow for its mitigation. The framework was tested and illustrated in the Portel municipality in Southern Portugal, a Mediterranean silvo-pastoral system that is prone to desertification and soil degradation. The results show a clear difference in the spatial and temporal distribution of the capacity for ecosystem service provision and the actual ecosystem service provision." AUTHOR'S DESCRIPTION: "To begin assessing the contribution of SEP we need to identify the structural impact of soil erosion, that is, the erosion that would occur when vegetation is absent and therefore no ES is provided. It determines the potential soil erosion in a given place and time and is related to rainfall erosivity (that is, the erosive potential of rainfall), soil erodibility (as a characteristic of the soil type) and local topography. Although external drivers can have an effect on these variables (for example, climate change), they are less prone to be changed directly by human action. The actual ES provision reduces the total amount of structural impact, and we define the remaining impact as the ES mitigated impact. We can then define the capacity for ES provision as a key component to determine the fraction of the structural impact that is mitigated…Following the conceptual outline, we will estimate the SEP provided by vegetation cover using an adaptation of the Universal Soil Loss Equation (USLE)."	ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake."	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…An alternative method to estimate potential fisheries production is to quantify not just the percent coverages of key habitats (F1)–(F6), but the degree of connectivity among those habitats. Many species that utilize coral reef habitat as adults are dependent on mangrove or seagrass nursery habitats as juveniles (Nagelkerken et al., 2000; Dorenbosch et al., 2006). In the Caribbean, the community structure or adult biomass of more than 150 reef fish species was affected by the presence of mangroves in the vicinity of reefs (Mumby et al., 2004). The value of habitat for fish production will therefore depend on the degree of connectivity between reefs and nearby mangroves (Mumby, 2006) and can be estimated as Cij = D - √(mix-rix)2+(mjy-rjy)2 where Cij is the connectivity between each reef cell i and nearby mangrove cell j, and D is the maximum migratory distance between mangroves and reefs (assumed to be 10 km), weighted by the distance between cells (x,y coordinates) such that shorter distances result in greater connectivity. The row sums then give the total connectivity of each reef cell to mangroves."	DATA FACT SHEET: "This EnviroAtlas national map estimates the total acres of agricultural crops within each 12-digit hydrologic unit (HUC) that have varying amounts of nearby forested pollinator habitat. The crop types selected from the U.S. Department of Agriculture Cropland Data Layer (CDL) require (or would benefit from) the presence of pollinators, but crops may have no nearby native pollinator habitat. This metric is based on the average flight distance of native bees, both social and solitary, that nest in woodland habitats and forage on native plants and cultivated crops." "The metric was generated using the ESRI ArcMap Neighborhood Distance tool in conjunction with a blended landcover grid, which included the 2006 National Land Cover Dataset (NLCD) and U.S. Department of Agriculture National Agricultural Statistics Service Cropland Data Layer (CDL). Pollinator habitat is defined as trees (fruit, nut, deciduous, and evergreen) for nesting and associated woodland for additional pollen sources. Crops that either require or benefit from pollination were selected and a distance measure of 2.8 kilometers (the average bee species’ foraging distance from the nest4) was used to assess presence or absence of nearby native pollinator habitat. The total area of crops without nearby pollinator habitat was summarized by 12-digit HUC boundaries taken from the NHDPlusV2 Watershed Boundary Dataset (WBD Snapshot)."	ABSTRACT: "The Tampa Bay estuary is a unique and valued ecosystem that currently thrives between subtropical and temperate climates along Florida’s west-central coast. The watershed is considered urbanized (42 % lands developed); however, a suite of critical coastal habitats still persists. Current management efforts are focused toward restoring the historic balance of these habitat types to a benchmark 1950s period. We have modeled the anticipated changes to a suite of habitats within the Tampa Bay estuary using the sea level affecting marshes model (SLAMM) under various sea level rise (SLR) scenarios. Modeled changes to the distribution and coverage of mangrove habitats within the estuary are expected to dominate the overall proportions of future critical coastal habitats. Modeled losses in salt marsh, salt barren, and coastal freshwater wetlands by 2100 will significantly affect the progress achieved in ‘‘Restoring the Balance’’ of these habitat types over recent periods…"	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	Not applicable	*	*	*	*	None identified	None provided
Biophysical Context	Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes	Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes	Northern Spain; Bizkaia region	nearshore; <1.5 km offshore; <12 m depth	Open savannah-like forest of cork (Quercus suber) and holm (Quercus ilex) oaks, with trees of different ages randomly dispersed in changing densities, and pastures in the under cover. The pastures are mostly natural in a mosaic with patches of shrubs, which differ in size and the distribution depends mainly on the grazing intensity. Shallow, poor soils are prone to erosion, especially in areas with high grazing pressure.	submerged aquatic vegetation	*	*	No additional description provided	Groundwater recharge area, City of Laramie
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	Not applicable	Different land management practices as represented by the comparison of different grazing intensities (i.e., livestock densities) in the whole study area and in three Civil Parishes within the study area	No scenarios presented	No scenarios presented	No scenarios presented	Varying sea level rise (baseline - 2m), and two habitat adaption strategies	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	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	Continuation of trends
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist)	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist)	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	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	Application of existing 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	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	Continuation of trends
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260 \| Doc-269	Doc-260	None	None	Doc-282 \| Doc-283 \| Doc-284 \| Doc-285	None	None	None	Doc-412 \| Doc-413	Doc-473
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-65 \| EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-79 \| EM-80 \| EM-81 \| EM-82 \| EM-83	EM-65 \| EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-71 \| EM-79 \| EM-80 \| EM-81 \| EM-82	None	None	None	None	None	None	EM-857	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	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	Continuation of trends
EM Temporal Extent em.detail.tempExtentHelp ?	2007-2008	Not reported	2000 - 2007	2006-2007	January to December 2003	2010 - 2012	2006-2007, 2010	2001-2015	2002-2100	2050
EM Time Dependence em.detail.timeDependencyHelp ?	*	*	*	*	time-dependent	*	*	*	time-stationary	*
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	*	*	*	*	future time	*	*	*	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 ?	*	*	*	*	Month	*	*	*	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	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	Continuation of trends
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Physiographic or Ecological	Geopolitical	Physiographic or Ecological	Geopolitical	Physiographic or ecological	Physiographic or ecological	Geopolitical	Watershed/Catchment/HUC	*
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	Central French Alps	Bilbao Metropolitan Greenbelt	St.Croix, U.S. Virgin Islands	Portel municipality	St. Louis River Estuary	Coastal zone surrounding St. Croix	conterminous United States	Tampa Bay estuary watershed	Laramie City's aquifer protection area
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	10-100 km^2	100-1000 km^2	10-100 km^2	100-1000 km^2	10-100 km^2	100-1000 km^2	>1,000,000 km^2	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	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	Continuation of trends
EM Spatial Distribution em.detail.distributeLumpHelp ?	*	*	*	spatially lumped (in all cases)	*	* ? Comment: BH: Each individual transect?s data was parceled into location reports, and that each report?s ?quadrat? area was dependent upon the angle of the hydroacoustic sampling beam. The spatial grain is 0.07 m^2, 0.20 m^2 and 0.70 m^2 for depths of 1 meter, 2 meters and 3 meters, respectively.	*	*	spatially distributed (in at least some cases)	spatially lumped (in all cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	*	*	*	Not applicable	*	*	*	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	20 m x 20 m	2 m x 2 m	Not applicable	250 m x 250 m	0.07 m^2 to 0.70 m^2	10 m x 10 m	irregular	10 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	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	Continuation of trends
EM Computational Approach em.detail.emComputationalApproachHelp ?	*	*	*	*	*	*	*	*	Analytic	Numeric
EM Determinism em.detail.deterStochHelp ?	*	*	*	*	*	*	*	*	deterministic	*
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	*	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	*	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	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	Continuation of trends
Model Calibration Reported? em.detail.calibrationHelp ?	*	*	*	Yes	*	Yes	Yes	*	No	Unclear
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	*	*	Yes	*	Yes	*	*	No	*
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	R squared \| 45.2 \| Not applicable	*	*	R squared (taxa richness) \| 0.74 \| Not applicable p-value \| 0.0020 \| unitless	*	p-value \| <0.001 \| unitless Nagelkerke r2 \| 0.30-0.62 \| unitless	*	*	None	*
Model Operational Validation Reported? em.detail.validationHelp ?	*	*	Yes	*	*	Yes	Yes	*	No	Unclear
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	*	*	*	Yes	*	*	*	*	No	Unclear
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	*	*	*	*	*	*	*	*	No	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	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	Continuation of trends
Europe France	Europe France	Europe Spain	*	Europe Portugal	North America United States Minnesota Wisconsin	*	North America United States	North America United States Florida	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	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	Continuation of trends
*	*	*	Tropical Atlantic West Tropical Atlantic Tropical Southwestern Atlantic	*	*	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	*	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Floridian	*

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	New or revised model	New or revised model	New or revised model	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	Continuation of trends
Centroid Latitude em.detail.ddLatHelp ?	45.05	45.05	43.25	17.75	38.3	46.72	17.73	39.5	27.76	41.31
Centroid Longitude em.detail.ddLongHelp ?	6.4	6.4	-2.92	-64.75	-7.7	-96.13	-64.77	-98.35	-82.54	-105.46
Centroid Datum em.detail.datumHelp ?	*	*	*	NAD83	*	*	*	*	WGS84	*
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Provided	Provided	*	*	*	*	*	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	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	Continuation of trends
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	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	Terrestrial Environment (sub-classes not fully specified) \| Forests \| Agroecosystems \| Scrubland/Shrubland	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Near Coastal Marine and Estuarine	Terrestrial Environment (sub-classes not fully specified)	Inland Wetlands \| Near Coastal Marine and Estuarine \| 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.	Subalpine terraces, grasslands, and meadows.	none	stony coral reef	Silvo-pastoral system	Freshwater estuarine system	Coral reefs and mangroves	Terrestrial	Esturary and associated urban and terrestrial environment	watershed
EM Ecological Scale em.detail.ecoScaleHelp ?	Not applicable	Ecological scale is coarser than that of the Environmental Sub-class	*	*	Ecological scale is coarser than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	*	*	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class

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	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	Continuation of trends
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Community	*	Guild or Assemblage	*	*	Community	Guild or Assemblage	Not applicable	*

Taxonomic level and name of organisms or groups identified

New or revised model	New or revised model	New or revised model	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	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	*	*	*	other bee	None Available	*

EnviroAtlas URL

New or revised model	New or revised model	New or revised model	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	Continuation of trends
None Available	None Available	Percent IUCN Status II, Percent GAP Status 1 & 2	None Available	Average Annual Precipitation	Average Annual Precipitation	None Available	GAP Ecological Systems, The Watershed Boundary Dataset (WBD)	National Hydrography Dataset Plus (NHD PlusV2)	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	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	Continuation of trends
*	[Ecosystem] Regulation & Maintenance Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[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] Regulation & Maintenance Mediation of flows Mass flows Buffering and attenuation of mass flows Mass stabilisation and control of erosion rates	*	*	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal	[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	New or revised model	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	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) Fauna (4) Invertebrates Quality Indicator	Forests (21) Soil (2) Specific types of soil Flow Indicator Scrubland/Shrubland (24) Soil (2) Specific types of soil Flow Indicator Agroecosystems (22) Soil (2) Specific types of soil Flow Indicator	*	*	Agroecosystems (22) Fauna (4) Pollinators Stock Indicator	None	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	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	Continuation of trends
Driving Variables (and Units) em.detail.drivingVariableHelp ?	Combination of legacy and current land use (Not applicable)	Soil Carbon (%)	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)	Flow accumulation (Not reported) Longitude (Decimal degrees) Maximum daily precipitation (mm day^-1) Normalized difference vegetation index (unitless) Precipitation (mm) Slope (degrees) Soil organic matter (%) Soil permeability class (Not applicable) Soil structure parameter (unitless) Soil texture (unitless)	Bed slope (%) Depth (water) (m) Directional fetch distance (km) Directional mean monthly wind velocity (Not reported) Directional percent frequency (wind) (%) Fractal dimension (bed) (unitless) Lag SAV presence/absence (Not applicable)	Latitude of mangrove cells (UTM) Latitude of reef cells (UTM) Longitude of mangrove cells (UTM) Longitude of reef cells (UTM)	12-digit hydrological unit code (HUC12) (Not applicable) Cropland class (Not applicable) Land use/land cover (Not applicable)	Dike location (Not applicable) Elevation (ft) Land use land cover (Not applicable) Slope (%)	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 combination of legacy and current land use for community-weighted mean date of flowering onset (unitless)	Variable weight for soil carbon (unitless)	None	Coefficient on percent of land use classes with a LDI = 1.0 (Not applicable) Coefficient on station depth (Not applicable)	None	Coefficient on bed slope (unitless) Coefficient on depth (unitless) Coefficient on depth x REI (unitless) Coefficient on depth x bed slope (unitless) Coefficient on depth x fractal dimension (unitless) Coefficient on fractal dimension (bed) (unitless) Coefficient on lag SAV P/A (unitless) Coefficient on relative exposure index (wind) (unitless) Intercept (unitless)	Maximum migratory distance between mangroves and reef (fish) (km)	Average bee species foraging distance from nest (km) Crop requires/benefits from pollination (y/n by pixel) (Not applicable) Pollinator habitat - tree class (y/n by pixel) (Not applicable)	Beach sedimentation rate (mm yr^-1) Direction offshore (N, S, E, W) (Not applicable) Frequency of overwash (years) Great diurnal tide range (m) Historic trend (eustatic sea level) (mm yr^-1) Horizontal erosion rate (m yr^-1) MTL minus NAVD88 (m) Minimum elevation (wetland type) (m) Salt marsh elevation (above MTL) (m) Sea level rise (year 2100) (m) Vertical accretion rate (mm yr^-1)	*

Intermediate

EM ID em.detail.idHelp ?	New or revised model	New or revised model	New or revised model	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	Continuation of trends
Intermediate (Computed) Variables (and Units) em.detail.intermediateVariableHelp ?	None	None	None	None	Rainfall erosivity factor (MJ mm ha^-1 h^-1 month^-1) Soil erodibility factor (Not reported) Topographic factor (unitless) Vegetation cover factor (unitless)	Relative exposure index (wind) (unitless)	Distance between reef and mangrove cells (xy coordinates) (m)	None	None	*

Response

EM ID em.detail.idHelp ?	New or revised model	New or revised model	New or revised model	New or revised model	EM-321	New or revised model	Application of existing model	New or revised model	EM-863	Continuation of trends
Observed Response Variables (and Units) em.detail.observedResponseHelp ?	Community-weighted mean flowering date (observed) (Julian Day)	None	None	Coral taxa richness (No. taxa m^-2)	None	Submerged aquatic vegetation (SAV) occurrence (%)	None	None	None	*
Computed Response Variables (and Units) em.detail.computedResponseHelp ?	Community-weighted mean flowering date (modeled) (Julian Day)	Soil carbon value (Not applicable)	Aesthetic quality of the landscape (Not applicable) Recreation provision index (Not applicable)	None	Actual ES provision (soil erosion prevention) (t mo^-1) Capacity for soil erosion prevention (unitless) Mitigated soil erosion (t mo^-1) Structural soil erosion (t/month)	Submerged aquatic vegetation (SAV) probability (%)	Mangrove connectivity (for reef fish) (m)	Acres of pollinated crops with no nearby pollinator habitat (per HUC12) (acres)	Coastal freshwater wetlands (area in year 2100) (km ^2) Coastal freshwater wetlands gain/loss (area in year 2100) (km ^2) Mangroves (area in year 2100) (km ^2) Mangroves gain/loss (area in year 2100) (km^2) Salt barrens (area in year 2100) (km^2) Salt barrens gain/loss (area in year 2100) (km^2) Salt marshes (area in year 2100) (km^2) Salt marshes gain/loss (area in year 2100) (km^2)	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

: SLAMM (sea level affecting marshes model), Tampa Bay, Florida, USA (EM-863)

EM Identity and Description

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

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