EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.

This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:

Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
Intermediate (Computed) Variables
Response Variables
- Computed Response Variables
- Measured Response Variables

EM Variables by Category

A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:

Policy Regarding Use or Management of Ecosystem Resources
Land Surface (or Water Body Bed) Cover, Use or Substrate
Human Demographic Data
Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
Monetary Values

Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.

All EM Descriptors

This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:

EM Identity and Description
EM Modeling Approach
EM Locations, Environments, Ecology
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system

EM Descriptors by Modeling Concepts

This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:

Conceptual Model
Modeling Objective
Modeling Context
Potential for Model Linkage
Feasibility of Model Use
Model Certainty
Model Structural Information

Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.

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Comparing:

EM-12
EM-71
EM-121
EM-131
EM-414
EM-417
EM-418
EM-464
EM-632
EM-784
EM-939

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-12	EM-71	EM-121	EM-131	EM-414	EM-417	EM-418	EM-464	EM-632	EM-784	EM-939
EM Short Name em.detail.shortNameHelp ?	Evoland v3.5 (bounded growth), Eugene, OR, USA	Community flowering date, Central French Alps	Landscape importance for recreation, Europe	InVEST marine water quality, Hood Canal, WA, USA	SAV occurrence, St. Louis River, MN/WI, USA	SWAT, Guanica Bay, Puerto Rico, USA	SIRHI, St. Croix, USVI	Mangrove connectivity, St. Croix, USVI	Waterfowl pairs, CREP wetlands, Iowa, USA	Wildflower mix supporting bees, Florida, USA	ESTIMAP- Recreation, Europe
EM Full Name em.detail.fullNameHelp ?	Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA	Community weighted mean flowering date, Central French Alps	Landscape importance for recreation, Europe	InVEST (Integrated Valuation of Envl. Services and Tradeoffs) marine water quality, Hood Canal, WA, USA	Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA	SWAT (Soil and Water Assessment Tool) Guánica Bay, Puerto Rico, USA	SIRHI (SImplified Reef Health Index), St. Croix, USVI	Mangrove connectivity (of reef), St. Croix, USVI	Waterfowl pairs, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	Wildflower planting mix supporting bees in agricultural landscapes, Florida, USA	ESTIMAP- Recreation, Europe
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	Envision	EU Biodiversity Action 5	EU Biodiversity Action 5	InVEST	US EPA	US EPA	US EPA	US EPA	None	None	None
EM Source Document ID	47 ? Comment: Doc 183 is a secondary source for the Evoland model.	260	228	205	330	334	335	335	372	400	432
Document Author em.detail.documentAuthorHelp ?	Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V.	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Haines-Young, R., Potschin, M. and Kienast, F.	Toft, J. E., Burke, J. L., Carey, M. P., Kim, C. K., Marsik, M., Sutherland, D. A., Arkema, K. K., Guerry, A. D., Levin, P. S., Minello, T. J., Plummer, M., Ruckelshaus, M. H., and Townsend, H. M.	Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke	Hu, W. and Y. Yuan	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever	Williams, N.M., Ward, K.L., Pope, N., Isaacs, R., Wilson, J., May, E.A., Ellis, J., Daniels, J., Pence, A., Ullmann, K., and J. Peters	Zulian, G., Parrachini, M.L., Maes, J.,
Document Year em.detail.documentYearHelp ?	2008	2011	2012	2013	2013	2013	2014	2014	2010	2015	2013
Document Title em.detail.sourceIdHelp ?	Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs	From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA	Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary	Evaluation of Soil Erosion and Sediment Yield for the Ridge Watersheds in the Guanica Bay Watershed, Puerto Rico, Using the SWAT Model	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt	Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States	ESTIMAP: Ecosystem services mapping at the European scale
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published EPA report	Published journal manuscript	Published journal manuscript	Published report	Published journal manuscript	Published report

Software and Access

EM ID em.detail.idHelp ?	EM-12	EM-71	EM-121	EM-131	EM-414	EM-417	EM-418	EM-464	EM-632	EM-784	EM-939
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	http://evoland.bioe.orst.edu/ ? Comment: Software is likely available.	Not applicable	Not applicable	https://www.naturalcapitalproject.org/invest/	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	N.A.
Contact Name em.detail.contactNameHelp ?	Michael R. Guzy	Sandra Lavorel	Marion Potschin	J.E. Toft	Ted R. Angradi	Yongping Yuan	Susan H. Yee	Susan H. Yee	David Otis	Neal Williams	Grazia Zulian
Contact Address	Oregon State University, Dept. of Biological and Ecological Engineering	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom	Not reported	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	USEPA, ORD, NERL, Environmental sciences Division, Las Vegas, Nevada	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University	Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616	Joint Research Centre, Via Enrico Fermi 2749, TP 272, 21027 Ispra (VA), Italy
Contact Email	Not reported	sandra.lavorel@ujf-grenoble.fr	marion.potschin@nottingham.ac.uk	jetoft@stanford.edu	angradi.theodore@epa.gov	Yuan.Yongping@epa.gov	yee.susan@epa.gov	yee.susan@epa.gov	dotis@iastate.edu	nmwilliams@ucdavis.edu	grazia.zulian@jrc.ec.europa.e

EM Description

EM ID em.detail.idHelp ?	EM-12	EM-71	EM-121	EM-131	EM-414	EM-417	EM-418	EM-464	EM-632	EM-784	EM-939
Summary Description em.detail.summaryDescriptionHelp ?	Note: A more recent version of this model exists. See Related EMs below for links to related models/applications. ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses."	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: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Recreation” ... The potential to deliver services is assumed to be influenced by land-use ... and bioclimatic and landscape properties such as mountainous terrain, adjacency to coastal and wetland ecosystems, as well as adjacency to landscape protection zones." AUTHOR'S DESCRIPTION: "Recreation… is broadly defined as all areas where landscape properties are favourable for active recreation purposes."	Marine Water Quality Model. Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "We used outputs from the freshwater models as inputs to the marine water quality model.We adapted a box model that has been successfully applied in Puget Sound (Babson et al., 2006; Sutherland et al., 2011) to simulate seasonal and interannual variations in salinity, water temperature, and nitrates in the Canal." (p. 4)	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."	AUTHOR'S DESCRIPTION: " SWAT is a physically-based continuous watershed simulation model that operates on a daily time step. It is designed for long-term simulations. The U.S. Department of Agriculture-Agriculture Research Station (USDA-ARS) Grassland, Soil and Water Research Laboratory in Temple, Texas created SWAT in the early 1990s. It has undergone continual review and expansion of capabilities since it was created (Arnold et al., 1998; Neitsch, et al., 2011a and b). This model has the ability to predict changes in water, sediment, nutrient and pesticide loads with respect to the different management conditions in watershed. Major components of the SWAT model include hydrology, weather, erosion, soil temperature, crop growth, nutrients, pesticides and agricultural management practices (Neitsch et al., 2011b). SWAT subdivides a watershed into multiple sub-watersheds, and the subwatersheds are further divided into Hydrologic Response Units (HRUs) that consist of homogeneous land use, soils, slope, and management (Gassman et al., 2007; Neitsch, et al., 2011b; Williams et al., 2008).	ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of indicators have been proposed for measuring reef integrity, defined as the capacity to maintain healthy function and retention of diversity (Turner et al., 2000). The Simplified Integrated Reef Health Index (SIRHI) combines four attributes of reef condition into a single index: SIRHI = ΣiGi where Gi are the grades on a scale of 1 to 5 for four key reef attributes: percent coral cover, percent macroalgal cover, herbivorous fish biomass, and commercial fish biomass (Table2; Healthy Reefs Initiative, 2010). For a number of coral reef condition attributes, including fish richness, coral richness, and reef structural complexity, available data were point surveys from field monitoring by the US Environmental Protection Agency (see Oliver et al. (2011)) or the NOAA Caribbean Coral Reef Ecosystem Monitoring Program (see Pittman et al. (2008)). To generate continuous maps of coral condition for St. Croix, we fitted regression tree models to point survey data for St. Croix and then used models to predict reef condition in non-sampled locations (Fig. 1). In general, we followed the methods of Pittman et al. (2007) which generated predictive models for fish richness using readily available benthic habitat maps and bathymetry data. Because these models rely on readily available data (benthic habitat maps and bathymetry data), the models have the potential for high transferability to other locati	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."	ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "Number of duck pairs per site was estimated for 6 species of ducks: Mallard (Anas platyrhynchos), Blue-winged Teal (Anas discors), Northern Shoveler (Anas clypeata), Gadwall (Anas strepera), Northern Pintail (Anas acuta), and Wood Duck (Aix sponsa), using models developed by Cowardin et al. (1995). Pair abundance was based on wetland class (i.e., temporary, seasonal, semi-permanent, lake, or river), wetland size, and a set of species specific regression coefficients. All CREP wetlands were considered semi-permanent for this analysis; therefore only coefficients associated with the semipermanent wetland pair model were used in calculations. The general equation used to estimate the pairs per wetland was: Pairs = e (a + bx + α) * p where, e = mathematical constant ≈ 2.718, a = species specific regression coefficient a, b = species specific regression coefficient b, x = the natural log of wetland size, α = species specific alpha value, and p = proportion of the basin containing water (assumed to be 0.90 for this analysis)"	Abstract: " Global trends in pollinator-dependent crops have raised awareness of the need to support managed and wild bee populations to ensure sustainable crop production. Provision of sufficient forage resources is a key element for promoting bee populations within human impacted landscapes, particularly those in agricultural lands where demand for pollination service is high and land use and management practices have reduced available flowering resources. Recent government incentives in North America and Europe support the planting of wildflowers to benefit pollinators; surprisingly, in North America there has been almost no rigorous testing of the performance of wildflower mixes, or their ability to support wild bee abundance and diversity. We tested different wildflower mixes in a spatially replicated, multiyear study in three regions of North America where production of pollinatordependent crops is high: Florida, Michigan, and California. In each region, we quantified flowering among wildflower mixes composed of annual and perennial species, and with high and low relative diversity. We measured the abundance and species richness of wild bees, honey bees, and syrphid flies at each mix over two seasons. In each region, some but not all wildflower mixes provided significantly greater floral display area than unmanaged weedy control plots. Mixes also attracted greater abundance and richness of wild bees, although the identity of best mixes varied among regions. By partitioning floral display size from mix identity we show the importance of display size for attracting abundant and diverse wild bees. Season-long monitoring also revealed that designing mixes to provide continuous bloom throughout the growing season is critical to supporting the greatest pollinator species richness. Contrary to expectation, perennials bloomed in their first season, and complementarity in attraction of pollinators among annuals and perennials suggests that inclusion of functionally diverse species may provide the greatest benefit. Wildflower mixes may be particularly important for providing resources for some taxa, such as bumble bees, which are known to be in decline in several regions of North America. No mix consistently attained the full diversity that was planted. Further study is needed on how to achieve the desired floral display and diversity from seed mixes. " Additional information in supplemental Appendices online: http://dx.doi.org/10.1890/14-1748.1.sm	AUTHOR Descriptions: "ESTIMAP consists of a set of separate components, each of which can be run separately. The models have been all framed in the ecosystem services cascade model [4] which connects ecosystem structure and functioning to human well-being through the flow of ecosystem services. At present, three modules are operational and described in further detail in this report: pollination, recreation and coastal protectionPeople can benefit from the opportunities provided by nature for recreational activities if they are able to reach them. The Recreation Opportunity spectrum was chosen as a method to map different degrees of service available according to their proximity to the people. Remoteness and proximity have been addressed in the second step of the analysis, in order to assess how the benefit (recreation) can be delivered to people. The proxy that has been identified couples information on both variables and has been mapped by classifying the EU into zones of proximity versus remoteness. From the ROS perspective this part takes into account remoteness and to some extent expected social experience. Distance from roads and residential areas have been used as inputs. The information on the road network is provided by the TeleAtlas database, and covers all paved roads in Europe. Gravel roads have been discarded to ease the processing. Residential areas are extracted from CORINE land cover classes “continuous urban fabric” and “discontinuous urban fabric”, therefore, all urban patches larger than 25 ha are considered in the mapping. In the current exercise there was the necessity to adapt overseas experiences to the peculiarities of the European continent, especially considering that the EU does not contain large wilderness areas like other continents "
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…"	None identified	None identified	Land use change	None identified	None Identified	None identified	None identified	None identified	None identrified	None
Biophysical Context	No additional description provided	Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes	No additional description provided	No additional description provided	submerged aquatic vegetation	Need to fill in	No additional description provided	No additional description provided	Prairie pothole region of north-central Iowa	field plots near agricultural fields (mixed row crop, almond, walnuts), central valley, Ca	Continential Scale
EM Scenario Drivers em.detail.scenarioDriverHelp ?	Five scenarios that include urban growth boundaries and various combinations of unconstrainted development, fish conservation, agriculture and forest reserves. ? Comment: Additional alternatives included adding agricultural and forest reserves, and adding or removing urban growth boundaries to the three main scenarios.	No scenarios presented	No scenarios presented	future land use and land cover; Climate change	No scenarios presented	Planting type, fertilizing rate, harvest rate	No scenarios presented	No scenarios presented	No scenarios presented	Varied wildflower planting mixes of annuals and perennials	N.A.

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-12	EM-71	EM-121	EM-131	EM-414	EM-417	EM-418	EM-464	EM-632	EM-784	EM-939
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application	Method + Application (multiple runs exist)	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application (multiple runs exist) View EM Runs	Method Only
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	New or revised model	Application of existing model	Application of existing model	New or revised model	New or revised model	New or revised model

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

EM ID em.detail.idHelp ?	EM-12	EM-71	EM-121	EM-131	EM-414	EM-417	EM-418	EM-464	EM-632	EM-784	EM-939
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-47 \| Doc-313 \| Doc-314 ? Comment: Doc 183 is a secondary source for the Evoland model.	Doc-260 \| Doc-269	Doc-231 \| Doc-228	None	None	None	None	None	None	None	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-333 \| EM-369	EM-65 \| EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-79 \| EM-80 \| EM-81 \| EM-82 \| EM-83	EM-99 \| EM-119 \| EM-120 \| EM-162 \| EM-164 \| EM-165 \| EM-122 \| EM-123 \| EM-124 \| EM-125 \| EM-170 \| EM-171	None	None	None	None	None	EM-705 \| EM-703 \| EM-702 \| EM-701 \| EM-700	EM-796 \| EM-797 \| EM-804 \| EM-805 \| EM-806 \| EM-812 \| EM-814	EM-941

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-12	EM-71	EM-121	EM-131	EM-414	EM-417	EM-418	EM-464	EM-632	EM-784	EM-939
EM Temporal Extent em.detail.tempExtentHelp ?	1990-2050	2007-2008	2000	varies by run, see runs for values	2010 - 2012	1981-2004	2006-2007, 2010	2006-2007, 2010	2002-2007	2011-2012	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-dependent	time-stationary	time-stationary	time-stationary	time-stationary	time-dependent	time-stationary	time-stationary	time-stationary	time-dependent	Not applicable
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	future time	Not applicable	Not applicable	Not applicable	Not applicable	future time	Not applicable	Not applicable	Not applicable	past time	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	discrete	Not applicable	Not applicable	Not applicable	Not applicable	discrete	Not applicable	Not applicable	Not applicable	discrete	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	2	Not applicable	Not applicable	Not applicable	Not applicable	1	Not applicable	Not applicable	Not applicable	1	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Year	Not applicable	Not applicable	Not applicable	Not applicable	Day	Not applicable	Not applicable	Not applicable	Year	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-12	EM-71	EM-121	EM-131	EM-414	EM-417	EM-418	EM-464	EM-632	EM-784	EM-939
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or Ecological	Geopolitical	Physiographic or ecological	Physiographic or ecological	Watershed/Catchment/HUC	Physiographic or ecological	Physiographic or ecological	Multiple unrelated locations (e.g., meta-analysis)	Point or points ? Comment: This is a guess based on information in the document. 3 field sites were separated by up to 9km	No location (no locational reference given)
Spatial Extent Name em.detail.extentNameHelp ?	Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA	Central French Alps	The EU-25 plus Switzerland and Norway	Hood Canal	St. Louis River Estuary	Guanica Bay, Puerto Rico watersheds	Coastal zone surrounding St. Croix	Coastal zone surrounding St. Croix	CREP (Conservation Reserve Enhancement Program) wetland sites	Agricultural plots	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	10-100 km^2	>1,000,000 km^2	100-1000 km^2	10-100 km^2	100-1000 km^2	100-1000 km^2	100-1000 km^2	1-10 km^2	10-100 km^2	>1,000,000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-12	EM-71	EM-121	EM-131	EM-414	EM-417	EM-418	EM-464	EM-632	EM-784	EM-939
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases) ? Comment: Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha.	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases) ? Comment: 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 distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially distributed (in at least some cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	area, for pixel or radial feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	varies	20 m x 20 m	1 km x 1 km	Not reported	0.07 m^2 to 0.70 m^2	30m x 30m	10 m x 10 m	10 m x 10 m	multiple, individual, irregular shaped sites	Not applicable	Pixel size

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-12	EM-71	EM-121	EM-131	EM-414	EM-417	EM-418	EM-464	EM-632	EM-784	EM-939
EM Computational Approach em.detail.emComputationalApproachHelp ?	Numeric	Analytic	Logic- or rule-based	Analytic	Analytic	Numeric	Analytic	Analytic	Analytic	Numeric	Numeric
EM Determinism em.detail.deterStochHelp ?	stochastic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Other or unclear (comment) ? Comment: Agent based modeling results in response indices.	Conventional (frequentist) statistics	None	Other or unclear (comment)	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-12	EM-71	EM-121	EM-131	EM-414	EM-417	EM-418	EM-464	EM-632	EM-784	EM-939
Model Calibration Reported? em.detail.calibrationHelp ?	Unclear	No	No	No	Yes	Yes ? Comment: Used 1981 and 1982 data to calibrate hydrology.	Yes	Yes	Unclear	No	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Yes	No	No	Yes	No ? Comment: Calibration for both the stream flow and Sediment concentration of the mode	No	No	No	No	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	R squared \| 45.2 \| Not applicable	None	None	p-value \| <0.001 \| unitless Nagelkerke r2 \| 0.30-0.62 \| unitless	Nash-Sutcliffe \| 0.82 \| unitless Rsquared \| 0.89 \| unitless Rsquared for sed \| 0.15 \| unitless NSE for sed \| -0.02 \| unitless RME for sed \| 0.15 \| unitless	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	Yes	No	Yes	Yes ? Comment: Validation with 1983-1984 data from USGS. Used streamflow and water quality data from two stations	Yes	Yes	Unclear	No	Unclear
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	No	Unclear	No	No	No	No	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No ? Comment: Sensitivity analysis performed for agent values only.	No	No	No	No	Yes ? Comment: Yes for both runoff and sediment	No	No	No	No	Yes
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	No	Not applicable	Not applicable	Not applicable	Not applicable	Unclear

EM Locations, Environments, Ecology

Location of EM Application

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

EM-12	EM-71	EM-121	EM-131	EM-414	EM-417	EM-418	EM-464	EM-632	EM-784	EM-939
North America United States Oregon	Europe France	Europe Cyprus Portugal Malta Greece Austria Latvia Netherlands Sweden Ireland Luxembourg Poland Slovakia Slovenia France Lithuania Hungary Switzerland Spain Czech Republic Belgium Norway Finland Denmark Italy Germany Estonia	North America United States Washington	North America United States Minnesota Wisconsin	North America United States Puerto Rico U.S. Caribbean Territories	None	None	North America United States Iowa	North America United States Florida	Europe

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

EM-12	EM-71	EM-121	EM-131	EM-414	EM-417	EM-418	EM-464	EM-632	EM-784	EM-939
None	None	None	Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Puget Trough/Georgia Basin	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-12	EM-71	EM-121	EM-131	EM-414	EM-417	EM-418	EM-464	EM-632	EM-784	EM-939
Centroid Latitude em.detail.ddLatHelp ?	44.11	45.05	50.53	47.8	46.72	18.19	17.73	17.73	42.62	29.4	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	-123.09	6.4	7.6	-122.7	-96.13	-66.76	-64.77	-64.77	-93.84	-82.18	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	NAD83	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Provided	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Estimated	Provided	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-12	EM-71	EM-121	EM-131	EM-414	EM-417	EM-418	EM-464	EM-632	EM-784	EM-939
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Rivers and Streams \| Forests \| Agroecosystems \| Created Greenspace	Agroecosystems \| Grasslands	Aquatic Environment (sub-classes not fully specified) \| Terrestrial Environment (sub-classes not fully specified)	Near Coastal Marine and Estuarine	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Terrestrial Environment (sub-classes not fully specified)	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine	Inland Wetlands \| Agroecosystems \| Grasslands	Agroecosystems	Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	Agricultural-urban interface at river junction	Subalpine terraces, grasslands, and meadows.	Not applicable	glacier-carver saltwater fjord	Freshwater estuarine system	watershed	Coral reefs	Coral reefs and mangroves	Wetlands buffered by grassland set in agricultural land	Agricultural landscape	Not applicable
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is finer than that of the Environmental Sub-class	Not applicable	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-12	EM-71	EM-121	EM-131	EM-414	EM-417	EM-418	EM-464	EM-632	EM-784	EM-939
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Community	Not applicable	Not applicable	Not applicable	Not applicable	Guild or Assemblage	Community	Species	Species	Not applicable

Taxonomic level and name of organisms or groups identified

EM-12	EM-71	EM-121	EM-131	EM-414	EM-417	EM-418	EM-464	EM-632	EM-784	EM-939
other Fish Species Red-Legged Frog Osprey Western Terrestrial Garter Snake Little Brown Myotis Belted Kingfisher Wood Duck Northern River Otter Great Blue Heron Roughskin Newt Pacific Water Shrew American Beaver Mink Western Pond Turtle Killdeer Northwestern Salamander	None Available	None Available	None Available	None Available	None Available	other snapper Family grouper parrotfish surgeonfish	None Available	Species Wood Duck (Aix sponsa) Northern Pintail (Anas acuta) Mallard (Anas platyrhynchos) Blue-winged Teal (Anas discors) Northern Shoveler (Anas clypeata) Gadwall (Anas strepera)	other Bees Species Lupinus succulentus Trifolium obtusiflorum Helianthus bolaneri Eschscholzia californica Lotus scoparius Rudbeckia hirta Trifolium willdenovii Phacelia tanacetifolia Phacelia californica Trifolium fucatum Helianthus bolanderi Grindelia camporum Lupinus formosus Nemophila menziesii Trichostema lanceolatum Achillea millefolium Clarkia unguinculata lupinus densiflorus	None Available

EnviroAtlas URL

EM-12	EM-71	EM-121	EM-131	EM-414	EM-417	EM-418	EM-464	EM-632	EM-784	EM-939
Dasymetric Allocation of Population, GAP Ecological Systems, The National Hydrography Dataset (NHD), Percent Natural Land Cover in Stream Buffer , Hectares of Vegetable Crops, Employment Rate	None Available	Percent GAP Status 1 & 2	None Available	Average Annual Precipitation	National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Agricultural water use (million gallons/day), The Watershed Boundary Dataset (WBD)	None Available	None Available	None Available	None Available	Dasymetric Allocation of Population, Ecosystem Markets: Imperiled Species and Habitats, Waterbody area

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

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

EM-12	EM-71	EM-121	EM-131	EM-414	EM-417	EM-418	EM-464	EM-632	EM-784	EM-939
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	None	[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 Entertainment	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of salt waters Mediation of flows Liquid flows Hydrological cycle and water flow maintenance	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[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 Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[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 Pollination and seed dispersal	[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

<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>

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

EM-12	EM-71	EM-121	EM-131	EM-414	EM-417	EM-418	EM-464	EM-632	EM-784	EM-939
Forests (21) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Agroecosystems (22) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator	None	Rivers and Streams (111) Composite (8) Presence of environmental class/subclass Quality Indicator Barren / Rock and Sand (28) Composite (8) Presence of environmental class/subclass Quality Indicator Forests (21) Composite (8) Presence of environmental class/subclass Quality Indicator Tundra (25) Composite (8) Presence of environmental class/subclass Quality Indicator Scrubland/Shrubland (24) Composite (8) Presence of environmental class/subclass Quality Indicator Grasslands (23) Composite (8) Presence of environmental class/subclass Quality Indicator Wetlands (12) Composite (8) Presence of environmental class/subclass Quality Indicator Lakes and Ponds (112) Composite (8) Presence of environmental class/subclass Quality Indicator Urban/Suburban (27) Composite (8) Presence of environmental class/subclass Quality Indicator	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Quality Indicator	None	None	None	None	Wetlands (12) Fauna (4) Duck Stock Indicator	Agroecosystems (22) Fauna (4) Pollinators Site Indicator	None

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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

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

<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>

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