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-65
EM-71
EM-193
EM-260
EM-430
EM-542
EM-700
EM-719
EM-890
EM-964
EM-1006

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-430	EM-542	EM-700	EM-719	EM-890	EM-964	EM-1006
EM Short Name em.detail.shortNameHelp ?	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	Carbon sequestration, Guánica Bay, Puerto Rico	Coastal protection in Belize	Mallard recruits, CREP wetlands, Iowa, USA	Seed mix for native plant establishment, IA, USA	HWB Blood pressure, Great Lakes waterfront, USA	EcoSim II - method	Vista land-sea planning submodel
EM Full Name em.detail.fullNameHelp ?	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	Carbon sequestration, Guánica Bay, Puerto Rico, USA	Coastal Protection provided by Coral, Seagrasses and Mangroves in Belize:	Mallard duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	Cost-effective seed mix design for native plant establishment, Iowa, USA	Human well being indicator- Blood pressure, Great Lakes waterfront, USA	EcoSim II - method	A technical guide to the integrated land-sea planning toolkit
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	EU Biodiversity Action 5	None ? Comment: EU Mapping Studies	US EPA	US EPA	InVEST	None	None	None	None	None
EM Source Document ID	260	260	191	96	338	350	372 ? Comment: Document 373 is a secondary source for this EM.	394	422 ? Comment: Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers	448	473
Document Author em.detail.documentAuthorHelp ?	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H.	Oliver, L. M., Lehrter, J. C. and Fisher, W. S.	Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher	Guannel, G., Arkema, K., Ruggiero, P., and G. Verutes	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	Meissen, J.	Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick	Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell	Crist, P., Madden, K., Varley, I., Eslinger, D., Walker, D., Anderson, A., Morehead, S. and Dunton, K.,
Document Year em.detail.documentYearHelp ?	2011	2011	2013	2011	2017	2016	2010	2018	None	2000	2009
Document Title em.detail.sourceIdHelp ?	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning	Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands	Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico	The Power of Three: Coral Reefs, Seagrasses and Mangroves Protect Coastal Regions and Increase Their Resilience	Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt	Cost-effective seed mix design and first-year management	Human well-being and natural capital indictors for Great Lakes waterfront revitalization	Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II	Integrated Land-Sea Planning: A Technical Guide to the Integrated Land-Sea Planning Toolkit.
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 but unpublished (explain in Comment)	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 journal manuscript	Published report	Published report	Journal manuscript submitted or in review	Published journal manuscript	Published report

Software and Access

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-430	EM-542	EM-700	EM-719	EM-890	EM-964	EM-1006
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not identified in paper	Not applicable	Not applicable	Not applicable	https://ecopath.org/downloads/	https://repositories.lib.utexas.edu/bitstreams/3dee92a8-9373-4bcc-be25-eda74e81fabf/download
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Sandra Lavorel	Izaskun Casado-Arzuaga	Leah Oliver	Susan H. Yee	Greg Guannel	David Otis	Justin Meissen	Ted Angradi	Carl Walters	Patrick Crist ? Comment: No contact information provided
Contact Address	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain	National Health and Environmental Research Effects Laboratory	U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	The Nature Conservancy, Coral Gables, FL. USA	U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University	Tallgrass Prairie Center, University of Northern Iowa	USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804	Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4	None provided
Contact Email	sandra.lavorel@ujf-grenoble.fr	sandra.lavorel@ujf-grenoble.fr	izaskun.casado@ehu.es	leah.oliver@epa.gov	yee.susan@epa.gov	greg.guannel@gmail.com	dotis@iastate.edu	Not reported	tedangradi@gmail.com	c.walters@oceans.ubc.ca	patrick@planitfwd.com

EM Description

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-430	EM-542	EM-700	EM-719	EM-890	EM-964	EM-1006
Summary Description em.detail.summaryDescriptionHelp ?	ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)."	ABSTRACT: "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)	AUTHOR'S DESCRIPTION: "In addition to affecting water quality, the ecosystem services of nitrogen retention, phosphorous retention, and sediment retention were also considered to contribute to stakeholder goals of maintaining the productivity of agricultural land and reducing soil loss. Two additional metrics, nitrogen fixation and rates of carbon sequestration into soil and sediment, were also calculated as potential measures of soil quality and agricultural productivity. Carbon sequestration and nitrogen fixation rates were assigned to each land cover class, applying the mean of rates for natural sub-tropical ecosystems obtained from the literature."	AUTHOR'S DESCRIPTION: "Natural habitats have the ability to protect coastal communities against the impacts of waves and storms, yet it is unclear how different habitats complement each other to reduce those impacts. Here, we investigate the individual and combined coastal protection services supplied by live corals on reefs, seagrass meadows, and mangrove forests during both non-storm and storm conditions, and under present and future sea-level conditions. Using idealized profiles of fringing and barrier reefs, we quantify the services supplied by these habitats using various metrics of inundation and erosion. We find that, together, live corals, seagrasses, and mangroves supply more protection services than any individual habitat or any combination of two habitats. Specifically, we find that, while mangroves are the most effective at protecting the coast under non-storm and storm conditions, live corals and seagrasses also moderate the impact of waves and storms, thereby further reducing the vulnerability of coastal regions. Also, in addition to structural differences, the amount of service supplied by habitats in our analysis is highly dependent on the geomorphic setting, habitat location and forcing conditions: live corals in the fringing reef profile supply more protection services than seagrasses; seagrasses in the barrier reef profile supply more protection services than live corals; and seagrasses, in our simulations, can even compensate for the long-term degradation of the barrier reef. Results of this study demonstrate the importance of taking integrated and place-based approaches when quantifying and managing for the coastal protection services supplied by ecosystems."	ABSTRACT: "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…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2Rn where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = HZB/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632).	AUTHOR'S DESCRIPTION: "Restoring ecosystem services at scale requires executing conservation programs in a way that is resource and cost efficient as well as ecologically effective…Seed mix design is one of the largest determinants of project cost and ecological outcomes for prairie reconstructions. In particular, grass-to-forb seeding ratio affects cost since forb seed can be much more expensive relative to grass species (Prairie Moon Nursery 2012). Even for seed mixes with the same overall seeding rates, a mix with a low grass-to-forb seeding ratio is considerably more expensive than one with a high grass-to-forb ratio. Seeding rates for different plant functional groups that are too high or low may also adversely affect ecological outcomes…First-year management may also play a role in cost-effective prairie reconstruction. Post-agricultural sites where restoration typically occurs are often quickly dominated by fast-growing annual weeds by the time sown prairie seeds begin germinating (Smith et al. 2010)… Williams and others (2007) showed that prairie seedlings sown into established warm-season grasses were reliant on high light conditions created by frequently mowing tall vegetation in order to survive in subsequent years…Our objective was to compare native plant establishment and cost effectiveness with and without first-year mowing for three different seed mixes that differed in grass to forb ratio and soil type customization. With knowledge of plant establishment, cost effectiveness, and mowing management outcomes, conservation practitioners will be better equipped to restore prairie efficiently and successfully."	ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context."	ABSTRACT: " EcoSim II uses results from the Ecopath procedure for trophic mass-balance analysis to define biomass dynamics models for predicting temporal change in exploited ecosystems. Key populations can be repre- sented in further detail by using delay-difference models to account for both biomass and numbers dynamics. A major problem revealed by linking the population and biomass dynamics models is in representation of population responses to changes in food supply; simple proportional growth and reproductive responses lead to unrealistic predic- tions of changes in mean body size with changes in fishing mortality. EcoSim II allows users to specify life history mechanisms to avoid such unrealistic predictions: animals may translate changes in feed- ing rate into changes in reproductive rather than growth rates, or they may translate changes in food availability into changes in foraging time that in turn affects predation risk. These options, along with model relationships for limits on prey availabil- ity caused by predation avoidance tactics, tend to cause strong compensatory responses in modeled populations. It is likely that such compensatory responses are responsible for our inability to find obvious correlations between interacting trophic components in fisheries time-series data. But Eco- sim II does not just predict strong compensatory responses: it also suggests that large piscivores may be vulnerable to delayed recruitment collapses caused by increases in prey species that are in turn competitors/predators of juvenile piscivores "	NatureServe Vista is a broad assessment and planning decision support tool focused on conservation of specific mapped features or “conservation elements.” It facilitates capturing spatial and non-spatial information and conservation requirements for elements, defining scenarios of land use, management, conservation, disturbance, etc., and evaluating the impacts of scenarios on the elements. Vista also contains powerful internal tools and interoperability with outside tools to facilitate mitigating site-level conflicts, offsite mitigation, and development of alternative scenarios. The primary objective (though not exclusive application) of the tool is to develop/mitigate alternative scenarios such that they meet explicit conservation goals for the elements. Vista can also support goal seeking for competing land uses while preventing development of scenarios that attempt to meet goals for conflicting things in the same place. The primary role of NatureServe Vista in this toolkit is to evaluate the impacts of land use scenarios on conservation elements in terrestrial, freshwater, and marine ecosystems. It does this through direct evaluation of land use scenarios from CommunityViz (augmented with other use, management, disturbance data) and interoperating with N-SPECT to evaluate water quality impacts on aquatic/marine elements.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	Land management, ecosystem management, response to EU 2020 Biodiversity Strategy	Not applicable	None identified	Future rock lobster fisheries management	None identified	Seed mix design and management practices for native plant restoration	None identified	None	None provided
Biophysical Context	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	No additional description provided	barrier reef and fringing reef in nearshore coastal marine system	Prairie Pothole Region of Iowa	The soils underlying the study site are primarily poorly drained Clyde clay loams, with a minor component of somewhat poorly drained Floyd loams in the northwest (NRCS 2016). Topographically, the study site is level, and slopes do not exceed 5% grade. Land use prior to this experiment was agricultural, with corn and soybeans consistently grown in rotation at the site.	Waterfront districts on south Lake Michigan and south lake Erie	None, Ocean ecosystems	Not applicable
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	Not applicable	No scenarios presented	Reef type, Sea level increase, storm conditions, seagrass conditions, coral conditions, vegetation types and conditions	No scenarios presented	No scenarios presented	N/A	N/A	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-430	EM-542	EM-700	EM-719	EM-890	EM-964	EM-1006
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method Only	Method Only
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	New or revised model	New or revised model	New or revised model	Application of existing model	New or revised model	New or revised model	New or revised model	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-65	EM-71	EM-193	EM-260	EM-430	EM-542	EM-700	EM-719	EM-890	EM-964	EM-1006
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260	Doc-260 \| Doc-269	None	None	None	None	Doc-372 \| Doc-373	Doc-395	Doc-422	None	Doc-473
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-71 \| EM-79 \| EM-80 \| EM-81 \| EM-82 \| EM-83	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-705 \| EM-704 \| EM-703 \| EM-702 \| EM-701 \| EM-632	EM-728	EM-886 \| EM-888 \| EM-889 \| EM-891 \| EM-893 \| EM-894 \| EM-895	None	EM-1003 \| EM-1005 \| EM-1007 \| EM-1008

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-430	EM-542	EM-700	EM-719	EM-890	EM-964	EM-1006
EM Temporal Extent em.detail.tempExtentHelp ?	2007-2009	2007-2008	2000 - 2007	2006-2007	1978 - 2013	2005-2013	1987-2007	2015-2017	2022	Not applicable	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	time-dependent	time-stationary	time-dependent	time-stationary	time-dependent	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	both	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	discrete	Not applicable	discrete	Not applicable	discrete ? Comment: Modeller dependent	other or unclear (comment)
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	1	Not applicable	1	Not applicable	1	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Second	Not applicable	Year	Not applicable	Day	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-430	EM-542	EM-700	EM-719	EM-890	EM-964	EM-1006
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Physiographic or Ecological	Geopolitical	Physiographic or Ecological	Watershed/Catchment/HUC	Geopolitical	Multiple unrelated locations (e.g., meta-analysis)	Other	Geopolitical	Other	Not applicable
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	Central French Alps	Bilbao Metropolitan Greenbelt	St.Croix, U.S. Virgin Islands	Guanica Bay watershed	Coast of Belize	CREP (Conservation Reserve Enhancement Program	Iowa State University Northeast Research and Demonstration Farm	Great Lakes waterfront	Not applicable	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	10-100 km^2	100-1000 km^2	10-100 km^2	1000-10,000 km^2.	100-1000 km^2	10,000-100,000 km^2	<1 ha	1000-10,000 km^2.	Not applicable	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-430	EM-542	EM-700	EM-719	EM-890	EM-964	EM-1006
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially lumped (in all cases)	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 lumped (in all cases)	other or unclear (comment)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	Not applicable	area, for pixel or radial feature	length, for linear feature (e.g., stream mile)	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	Not applicable	Not applicable	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	20 m x 20 m	2 m x 2 m	Not applicable	30 m x 30 m	1 meter	multiple, individual, irregular sites	20 ft x 28 ft	Not applicable	Not applicable	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-430	EM-542	EM-700	EM-719	EM-890	EM-964	EM-1006
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic	Analytic	Numeric	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	stochastic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	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-65	EM-71	EM-193	EM-260	EM-430	EM-542	EM-700	EM-719	EM-890	EM-964	EM-1006
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	No	Yes	No	No	Unclear	Not applicable	No	No	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	Yes	No	Yes	No	No	No	Not applicable	No	No	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	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	None	None	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	No	Yes	No	No	No ? Comment: Used the SWAN model (see below for referenece) with Generation 1 or 2 wind-wave formulations to validate the wave development portion of the model. Booij N, Ris RC, Holthuijsen LH. A third-generation wave model for coastal regions 1. Model description and validation. J Geophys Res. American Geophysical Union; 1999;104: 7649?7666.	No	No	No	Not applicable	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	Yes	No	No	No	Not applicable	No	Not applicable	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	No	No	Not applicable	Yes	Not applicable	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

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

EM-65	EM-71	EM-193	EM-260	EM-430	EM-542	EM-700	EM-719	EM-890	EM-964	EM-1006
Europe France	Europe France	Europe Spain	None	North America U.S. Caribbean Territories	North America Belize	North America United States Iowa	North America United States Iowa	North America United States Illinois Minnesota Ohio Wisconsin	None	None

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

EM-65	EM-71	EM-193	EM-260	EM-430	EM-542	EM-700	EM-719	EM-890	EM-964	EM-1006
None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Southwestern Atlantic	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Greater Antilles	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Western Caribbean	None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-430	EM-542	EM-700	EM-719	EM-890	EM-964	EM-1006
Centroid Latitude em.detail.ddLatHelp ?	45.05	45.05	43.25	17.75	17.96	18.63	42.62	42.93	42.26	Not applicable	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	6.4	6.4	-2.92	-64.75	-67.02	-88.22	-93.84	-92.57	-87.84	Not applicable	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	NAD83	WGS84	WGS84	WGS84	WGS84	WGS84	Not applicable	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Provided	Provided	Estimated	Estimated	Estimated	Estimated	Provided	Estimated	Not applicable	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-430	EM-542	EM-700	EM-719	EM-890	EM-964	EM-1006
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Agroecosystems \| Grasslands	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified) \| Forests \| Agroecosystems \| Created Greenspace \| Grasslands \| Scrubland/Shrubland	Near Coastal Marine and Estuarine	Inland Wetlands \| Near Coastal Marine and Estuarine \| Forests \| Agroecosystems \| Grasslands \| Scrubland/Shrubland \| Barren	Near Coastal Marine and Estuarine	Inland Wetlands \| Agroecosystems \| Grasslands	Agroecosystems \| Grasslands	Terrestrial Environment (sub-classes not fully specified)	Open Ocean and Seas	Not applicable
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows	Subalpine terraces, grasslands, and meadows.	none	stony coral reef	13 LULC were used	coral reefs	Wetlands buffered by grassland within agroecosystems	Research farm in historic grassland	Lake Michigan & Lake Erie waterfront	Pelagic	None
EM Ecological Scale em.detail.ecoScaleHelp ?	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 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 corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-430	EM-542	EM-700	EM-719	EM-890	EM-964	EM-1006
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Community	Not applicable	Guild or Assemblage	Not applicable	Guild or Assemblage	Individual or population, within a species	Community	Not applicable	Other (Comment) ? Comment: Varied levels of taxonomic order	Community

Taxonomic level and name of organisms or groups identified

EM-65	EM-71	EM-193	EM-260	EM-430	EM-542	EM-700	EM-719	EM-890	EM-964	EM-1006
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	Species Mallard (Anas platyrhynchos)	None Available	None Available	other Whiting shrimp Whales Seals Cod Phytoplankton Mackerel Benthos Zooplankton Anchovy	None Available

EnviroAtlas URL

EM-65	EM-71	EM-193	EM-260	EM-430	EM-542	EM-700	EM-719	EM-890	EM-964	EM-1006
GAP Ecological Systems	None Available	Percent IUCN Status II, Percent GAP Status 1 & 2	None Available	The Watershed Boundary Dataset (WBD), Percent Impervious Area, Carbon Storage by Tree Biomass	GAP Ecological Systems, National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation	Acres of Land Enrolled in the Conservation Reserve Program (CRP)	GAP Ecological Systems	GAP Ecological Systems, Enabling Conditions	Big game hunting recreation demand	Watersheds, Ecosystem Markets: Imperiled Species and Habitats

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-65	EM-71	EM-193	EM-260	EM-430	EM-542	EM-700	EM-719	EM-890	EM-964	EM-1006
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 Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection Mass flows Mass stabilisation and control of erosion rates	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Pollination and seed dispersal	None	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs [Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes Weathering processes Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Mediation of flows Liquid flows Hydrological cycle and water flow maintenance Mass flows Buffering and attenuation of mass flows Mass stabilisation and control of erosion rates Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems Dilution by atmosphere, freshwater and marine ecosystems

<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-65	EM-71	EM-193	EM-260	EM-430	EM-542	EM-700	EM-719	EM-890	EM-964	EM-1006
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) Regulation of extreme events Extreme Event Indicator Flow Indicator	Wetlands (12) Fauna (4) Duck Stock Indicator	Grasslands (23) Composite (8) Presence of environmental class/subclass Quality Indicator	None	Open Oceans and Seas (114) Fauna (4) Fish Flow 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

Comment:

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)