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-51
EM-65
EM-71
EM-193
EM-260
EM-414
EM-455
EM-590
EM-1019

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-12	EM-51	EM-65	EM-71	EM-193	EM-260	EM-414	EM-455	EM-590	EM-1019
EM Short Name em.detail.shortNameHelp ?	Evoland v3.5 (bounded growth), Eugene, OR, USA	EnviroAtlas-Nat. filtration-water	Green biomass production, Central French Alps	Community flowering date, Central French Alps	Cultural ecosystem services, Bilbao, Spain	Coral taxa and land development, St.Croix, VI, USA	SAV occurrence, St. Louis River, MN/WI, USA	Value of a reef dive site, St. Croix, USVI	Fish species richness, Puerto Rico, USA	SMOKE emissions model, Asia
EM Full Name em.detail.fullNameHelp ?	Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA	US EPA EnviroAtlas - Natural filtration (of water by tree cover); Example is shown for Durham NC and vicinity, USA	Green biomass production, Central French Alps	Community weighted mean flowering date, Central French Alps	Cultural ecosystem services, Bilbao, Spain	Coral taxa richness and land development, St.Croix, Virgin Islands, USA	Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA	Value of a dive site (reef), St. Croix, USVI	Fish species richness, Puerto Rico, USA	Development of an anthropogenic emissions processing system for Asia using SMOKE
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	Envision	US EPA \| EnviroAtlas \| i-Tree ? Comment: EnviroAtlas uses an application of the i-Tree Hydro model.	EU Biodiversity Action 5	EU Biodiversity Action 5	None ? Comment: EU Mapping Studies	US EPA	US EPA	US EPA	None	None
EM Source Document ID	47 ? Comment: Doc 183 is a secondary source for the Evoland model.	223	260	260	191	96	330	335	355	481
Document Author em.detail.documentAuthorHelp ?	Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V.	US EPA Office of Research and Development - National Exposure Research Laboratory	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.	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	Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco	Woo, J.H., Choi, K.C., Kim, H.K., Baek, B.H., Jang, M., Eum, J.H., Song, C.H., Ma, Y.I., Sunwoo, Y., Chang, L.S. and Yoo, S.H.
Document Year em.detail.documentYearHelp ?	2008	2013	2011	2011	2013	2011	2013	2014	2007	2012
Document Title em.detail.sourceIdHelp ?	Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests	EnviroAtlas - Featured Community	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	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	Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean	Development of an anthropogenic emissions processing system for Asia using SMOKE
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
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published on US EPA EnviroAtlas website	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-12	EM-51	EM-65	EM-71	EM-193	EM-260	EM-414	EM-455	EM-590	EM-1019
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	http://evoland.bioe.orst.edu/ ? Comment: Software is likely available.	https://www.epa.gov/enviroatlas	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	https://www.cmascenter.org/smoke/
Contact Name em.detail.contactNameHelp ?	Michael R. Guzy	EnviroAtlas Team	Sandra Lavorel	Sandra Lavorel	Izaskun Casado-Arzuaga	Leah Oliver	Ted R. Angradi	Susan H. Yee	Simon Pittman	Jung-Hun Woo
Contact Address	Oregon State University, Dept. of Biological and Ecological Engineering	Not reported	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	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	1305 East-West Highway, Silver Spring, MD 20910, USA	Department of Advanced Technology Fusion, Room 812, San-Hak Bldg., Konkuk University, Seoul, South Korea
Contact Email	Not reported	enviroatlas@epa.gov	sandra.lavorel@ujf-grenoble.fr	sandra.lavorel@ujf-grenoble.fr	izaskun.casado@ehu.es	leah.oliver@epa.gov	angradi.theodore@epa.gov	yee.susan@epa.gov	simon.pittman@noaa.gov	jwoo@konkuk.ac.kr

EM Description

EM ID em.detail.idHelp ?	EM-12	EM-51	EM-65	EM-71	EM-193	EM-260	EM-414	EM-455	EM-590	EM-1019
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."	The Natural Filtration model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. METADATA ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina... runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA DESCRIPTION: "The i-Tree Hydro model estimates the effects of tree and impervious cover on hourly stream flow values for a watershed (Wang et al 2008). i-Tree Hydro also estimates changes in water quality using hourly runoff estimates and mean and median national event mean concentration (EMC) values. The model was calibrated using hourly stream flow data to yield the best fit between model and measured stream flow results… After calibration, the model was run a number of times under various conditions to see how the stream flow would respond given varying tree and impervious cover in the watershed… The term event mean concentration (EMC) is a statistical parameter used to represent the flow-proportional average concentration of a given parameter during a storm event. EMC data is used for estimating pollutant loading into watersheds. The response outputs were calculated as kg of pollutant per square meter of land area for pollutants. These per square meter values were multiplied by the square meters of land area in the block group to estimate the effects at the block group level." METADATA DESCRIPTION PARAPHRASED: Changes in water quality were estimated for the following pollutants (entered as separate runs); total suspended solids (TSS), total phosphorus, soluble phosphorus, nitrites and nitrates, total Kjeldahl nitrogen (TKN), biochemical oxygen demand (BOD5), chemical oxygen demand (COD5), and copper. "Reduction in annual runoff (census block group)" variable data was derived from the EnviroAtlas water recharge coverage which used the i-Tree Hydro model.	ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)."	ABSTRACT: "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 "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: “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...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…Another method to quantify recreational opportunities is to use survey data of tourists and recreational visitors to the reefs to generate statistical models to quantify the link between reef condition and production of recreation-related ecosystem services. Wielgus et al. (2003) used interviews with SCUBA divers in Israel to derive coefficients for a choice model in which willingness to pay for higher quality dive sites was determined in part by a weighted combination of factors identified with dive quality: Relative value of dive site = 0.1227(Scoral+Sfish+Acoral+Afish)+0.0565V where Scoral, Sfish are coral and fish richness, Acoral, Afish are abundances of fish and coral per square meter, and V is water visibility (meters)."	ABSTRACT: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems."	Air quality modeling is a useful methodology to investigate air quality degradation in various locations and to analyze effectiveness of emission reduction plans. A comprehensive air quality model usually requires a coordinated set of emissions input of all necessary chemical species. We have developed an anthropogenic emissions processing system for Asia in support of air quality modeling and analysis over Asia (named SMOKE-Asia). The SMOKE (Sparse Matrix Operator kernel Emissions) system, which was developed by U.S. EPA and has been maintained by the Carolina Environmental Program (CEP) of the University of North Carolina, was used to develop our emissions processing system. A merged version of INTEX 2006 and TRACE-P 2000 inventories was used as an initial Asian emissions inventory. The IDA (Inventory Data Analyzer) format was used to create SMOKE-ready emissions. Source Classification Codes (SCCs) and country/state/county (FIPS) code, which are the two key data fields of SMOKE IDA data structure, were created for Asia. The 38 SCCs and 2752 FIPS codes were allocated to our SMOKE-ready emissions for more comprehensive processing. US EPA’s MIMS (Multimedia Integrated Modeling System) Spatial Allocator software, along with many global and regional GIS shapes, were used to create spatial allocation profiles for Asia. Temporal allocation and chemical speciation profiles were partly regionalized using Asia-based studies. Initial data production using the developed SMOKE-Asia system was successfully performed. NOx and VOC emissions for the year 2009 were projected to be increased by 50% from those of 1997. The emission hotspots, such as large cities and large point sources, are distinguished in the domain due to spatial allocation. Regional emission peaks were distinguished due to temporally resolved emission information. The PAR (Paraffin carbon bond) and XYL (Xylene and other polyalkyl aromatics) showed the first and second largest emission rate among VOC species. Most of point source emissions are located in layers 3 to 4, which the altitude range reaches 310–550 m AGL. Qualitative inter-comparison between model output and ground/satellite measurement showed good agreements in terms of spatial and temporal patterns. We expect that the result of this study will provide better air quality modeling inputs, which will act as a major step to improve our understanding of Asian air quality.
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	None identified	Land management, ecosystem management, response to EU 2020 Biodiversity Strategy	Not applicable	None identified	None identified	None provided	None provided
Biophysical Context	No additional description provided	No additional description provided	Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes	Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes	Northern Spain; Bizkaia region	nearshore; <1.5 km offshore; <12 m depth	submerged aquatic vegetation	No additional description provided	Hard and soft benthic habitat types approximately to the 33m isobath	Asia
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	No scenarios presented	No scenarios presented	Not applicable	No scenarios presented	No scenarios presented	No scenarios presented	NA

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-12	EM-51	EM-65	EM-71	EM-193	EM-260	EM-414	EM-455	EM-590	EM-1019
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application (multiple runs exist) View EM Runs	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	Application of existing model	New or revised model	New or revised model	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	Application of existing model

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

EM ID em.detail.idHelp ?	EM-12	EM-51	EM-65	EM-71	EM-193	EM-260	EM-414	EM-455	EM-590	EM-1019
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-198	Doc-260	Doc-260 \| Doc-269	None	None	None	None	Doc-355	Doc-478
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-333 \| EM-369	EM-137 \| EM-142	EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-71 \| EM-79 \| EM-80 \| EM-81 \| EM-82 \| EM-83	EM-65 \| EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-79 \| EM-80 \| EM-81 \| EM-82 \| EM-83	None	None	None	None	EM-698 \| EM-699	EM-1012 \| EM-1021

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-12	EM-51	EM-65	EM-71	EM-193	EM-260	EM-414	EM-455	EM-590	EM-1019
EM Temporal Extent em.detail.tempExtentHelp ?	1990-2050	1999-2010	2007-2009	2007-2008	2000 - 2007	2006-2007	2010 - 2012	2006-2007, 2010	2000-2005	1997-2009
EM Time Dependence em.detail.timeDependencyHelp ?	time-dependent	time-stationary ? Comment: The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, operated on an hourly timestep. The final annual flow parameter however is time stationary.	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	future time	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	past time
EM Time Continuity em.detail.continueDiscreteHelp ?	discrete	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	continuous
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	2	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Year	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-12	EM-51	EM-65	EM-71	EM-193	EM-260	EM-414	EM-455	EM-590	EM-1019
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Geopolitical	Physiographic or Ecological	Physiographic or Ecological	Geopolitical	Physiographic or Ecological	Physiographic or ecological	Physiographic or ecological	Physiographic or ecological	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA	Durham, NC and vicinity	Central French Alps	Central French Alps	Bilbao Metropolitan Greenbelt	St.Croix, U.S. Virgin Islands	St. Louis River Estuary	Coastal zone surrounding St. Croix	SW Puerto Rico,	Asia
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	100-1000 km^2	10-100 km^2	10-100 km^2	100-1000 km^2	10-100 km^2	10-100 km^2	100-1000 km^2	100-1000 km^2	1000-10,000 km^2.

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-12	EM-51	EM-65	EM-71	EM-193	EM-260	EM-414	EM-455	EM-590	EM-1019
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)	spatially lumped (in all 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 lumped (in all cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	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	Not applicable	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	varies	irregular	20 m x 20 m	20 m x 20 m	2 m x 2 m	Not applicable	0.07 m^2 to 0.70 m^2	10 m x 10 m	not reported	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-12	EM-51	EM-65	EM-71	EM-193	EM-260	EM-414	EM-455	EM-590	EM-1019
EM Computational Approach em.detail.emComputationalApproachHelp ?	Numeric	Analytic ? Comment: The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, was numeric. The final parameter however did not require iteration.	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic	Numeric
EM Determinism em.detail.deterStochHelp ?	stochastic	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	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-12	EM-51	EM-65	EM-71	EM-193	EM-260	EM-414	EM-455	EM-590	EM-1019
Model Calibration Reported? em.detail.calibrationHelp ?	Unclear	Unclear	No	No	No	Yes	Yes	Yes	No	Unclear
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	Yes	Yes	No	Yes	Yes	No	Yes	Unclear
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	Adjusted R \| 69.9 \| Not applicable	R squared \| 45.2 \| Not applicable	None	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	R squared \| 0.55 \| unitless Cohen's Kappa statistic \| 0.54 \| unitless AUC \| 0.79 \| unitless	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	Unclear	Yes	No	Yes	No	Yes	Yes	Yes	Yes
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	Unclear	No	No	No	Yes	No	No	No	Unclear
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No ? Comment: Sensitivity analysis performed for agent values only.	Unclear	No	No	No	No	No	No	Yes	Unclear
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	No	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

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

EM-12	EM-51	EM-65	EM-71	EM-193	EM-260	EM-414	EM-455	EM-590	EM-1019
North America United States Oregon	North America United States North Carolina	Europe France	Europe France	Europe Spain	None	North America United States Minnesota Wisconsin	None	None	Asia

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

EM-12	EM-51	EM-65	EM-71	EM-193	EM-260	EM-414	EM-455	EM-590	EM-1019
None	None	None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Southwestern Atlantic	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Greater Antilles	Western Indo-Pacific

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-12	EM-51	EM-65	EM-71	EM-193	EM-260	EM-414	EM-455	EM-590	EM-1019
Centroid Latitude em.detail.ddLatHelp ?	44.11	35.99	45.05	45.05	43.25	17.75	46.72	17.73	17.9	38.63
Centroid Longitude em.detail.ddLongHelp ?	-123.09	-78.96	6.4	6.4	-2.92	-64.75	-96.13	-64.77	67.11	117.79
Centroid Datum em.detail.datumHelp ?	WGS84	None provided	WGS84	WGS84	WGS84	NAD83	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Provided	Provided	Provided	Estimated	Estimated	Estimated	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-12	EM-51	EM-65	EM-71	EM-193	EM-260	EM-414	EM-455	EM-590	EM-1019
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Rivers and Streams \| Forests \| Agroecosystems \| Created Greenspace	Rivers and Streams \| Created Greenspace	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	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Near Coastal Marine and Estuarine	Near Coastal Marine and Estuarine	Atmosphere
Specific Environment Type em.detail.specificEnvTypeHelp ?	Agricultural-urban interface at river junction	Urban areas including streams	Subalpine terraces, grasslands, and meadows	Subalpine terraces, grasslands, and meadows.	none	stony coral reef	Freshwater estuarine system	Coral reefs	shallow coral reefs	Asian atmosphere
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is finer than that of the Environmental Sub-class	Not applicable	Not applicable	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	Not applicable

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-12	EM-51	EM-65	EM-71	EM-193	EM-260	EM-414	EM-455	EM-590	EM-1019
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Community	Community	Not applicable	Guild or Assemblage	Not applicable	Guild or Assemblage	Guild or Assemblage	Not applicable

Taxonomic level and name of organisms or groups identified

EM-12	EM-51	EM-65	EM-71	EM-193	EM-260	EM-414	EM-455	EM-590	EM-1019
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	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	None Available	None Available	other (fish class)	None Available

EnviroAtlas URL

EM-12	EM-51	EM-65	EM-71	EM-193	EM-260	EM-414	EM-455	EM-590	EM-1019
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	GAP Ecological Systems	None Available	Percent IUCN Status II, Percent GAP Status 1 & 2	None Available	Average Annual Precipitation	None Available	None Available	None Available

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-51	EM-65	EM-71	EM-193	EM-260	EM-414	EM-455	EM-590	EM-1019
[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 Water conditions Chemical condition of freshwaters Mediation of waste, toxics and other nuisances Mediation by biota Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals	None	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	[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 Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Experiential use of plants, animals and land-/seascapes in different environmental settings	[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 Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations

<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-51	EM-65	EM-71	EM-193	EM-260	EM-414	EM-455	EM-590	EM-1019
Forests (21) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Agroecosystems (22) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator	Rivers and Streams (111) Water (3) Liquid water Quality Indicator Urban/Suburban (27) Composite (8) Presence of environmental class/subclass Quality Indicator	Grasslands (23) Flora (5) Green biomass Stock Indicator Agroecosystems (22) Flora (5) Green biomass Stock Indicator	None	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	None	Near Coastal Marine/Estuarine (113) Composite (8) Presence of environmental class/subclass Quality Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator	Atmosphere Atmosphere (1) Air Quality Indicator

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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)