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-320
EM-337
EM-414
EM-415
EM-439
EM-657
EM-983

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-320	EM-337	EM-414	EM-415	EM-439	EM-657	EM-983
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	Coastal protection, Europe	Rate of Fire Spread	SAV occurrence, St. Louis River, MN/WI, USA	Esocid spawning, St. Louis River, MN/WI, USA	WaSSI, Conterminous USA	REQI (River Ecosystem Quality Index), Italy	Atlantis ecosystem physics 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	Coastal protection, Europe	Rate of Fire Spread	Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA	Esocid spawning, St. Louis River estuary, MN & WI, USA	Water Supply Stress Index, Conterminous USA	REQI (River Ecosystem Quality Index), Marecchia River, Italy	Atlantis user's guide part I: general overview, physics & ecology
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	EU Biodiversity Action 5	None ? Comment: EU Mapping Studies	US EPA	EU Biodiversity Action 5	None	US EPA	US EPA	USDA Forest Service ? Comment: While the user guide on which model entry is based has not been peer reviewed, several peer reviewed journal articles describing this USA HUC8 version of WaSSI have been published.	None	None
EM Source Document ID	260	260	191	96	296	306	330	332	341	378	461
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.	Liquete, C., Zulian, G., Delgado, I., Stips, A., and Maes, J.	Rothermel, Richard C.	Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke	Ted R. Angradi, David W. Bolgrien, Jonathon J. Launspach, Brent J. Bellinger, Matthew A. Starry, Joel C. Hoffman, Mike E. Sierszen, Anett S. Trebitz, and Tom P. Hollenhorst	Peter Caldwell, Ge Sun, Steve McNulty, Jennifer Moore Myers, Erika Cohen, Robert Herring, Erik Martinez	Santolini, R, E. Morri, G. Pasini, G. Giovagnoli, C. Morolli, and G. Salmoiraghi	Audzijonyte, A., Gorton, R., Kaplan, I., & Fulton, E. A.
Document Year em.detail.documentYearHelp ?	2011	2011	2013	2011	2013	1972	2013	2016	2013	2014	2017
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	Assessment of coastal protection as an ecosystem service in Europe	A Mathematical model for predicting fire spread in wildland fuels	Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary	Mapping ecosystem service indicators of a Great Lakes estuarine Area of Concern	WaSSI Ecosystem Services Model	Assessing the quality of riparian areas: the case of River Ecosystem Quality Index applied to the Marecchia river (Italy)	Atlantis user’s guide part I: general overview, physics & ecology
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	Documented, not peer reviewed	Peer reviewed and published	Peer reviewed and published	Not peer reviewed but is published (explain in Comment)	Peer reviewed and published	Not peer reviewed but is published (explain in Comment)
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published USDA Forest Service report	Published journal manuscript	Published journal manuscript	While the user guide on which model entry is based has not been peer reviewed, several peer reviewed journal articles describing this USA HUC8 version of WaSSI have been published.	Published journal manuscript	Published report

Software and Access

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-320	EM-337	EM-414	EM-415	EM-439	EM-657	EM-983
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	http://firelab.org/project/farsite	Not applicable	Not applicable	http://www.wassiweb.sgcp.ncsu.edu/	Not applicable	https://research.csiro.au/atlantis/home/links/
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Sandra Lavorel	Izaskun Casado-Arzuaga	Leah Oliver	Camino Liquete	Charles McHugh	Ted R. Angradi	Ted R. Angradi	Ge Sun	Elisa Morri	Asta Audzijonyte
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	European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, I-21027 Ispra, VA, Italy	RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808	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	United States Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboraty, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804 USA	Eastern Forest Environmental Threat Assessment Center, Southern Research Station, USDA Forest Service, 920 Main Campus Dr. Venture II, Suite 300, Raleigh, NC 27606	Dept. of Earth, Life, and Environmental Sciences, Urbino university, via ca le suore, campus scientifico Enrico Mattei, Urbino 61029 Italy	University of Tasmania (Australia); Nature Research Centre (Lithuania)
Contact Email	sandra.lavorel@ujf-grenoble.fr	sandra.lavorel@ujf-grenoble.fr	izaskun.casado@ehu.es	leah.oliver@epa.gov	camino.liquete@gmail.com	cmchugh@fs.fed.us	angradi.theodore@epa.gov	angradi.theodore@epa.gov	gesun@fs.fed.us	elisa.morri@uniurb.it	Asta.Audzijonyte@utas.edu.au

EM Description

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-320	EM-337	EM-414	EM-415	EM-439	EM-657	EM-983
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)	ABSTRACT: "Mapping and assessment of ecosystem services is essential to provide scientific support to global and EU biodiversity policy. Coastal protection has been mostly analysed in the frame of coastal vulnerability studies or in local, habitat-specific assessments. This paper provides a conceptual and methodological approach to assess coastal protection as an ecosystem service at different spatial–temporal scales, and applies it to the entire EU coastal zone. The assessment of coastal protection incorporates 14 biophysical and socio-economic variables from both terrestrial and marine datasets. Those variables define three indicators: coastal protection capacity, coastal exposure and human demand for protection. A questionnaire filled by coastal researchers helped assign ranks to categorical parameters and weights to the individual variables. The three indicators are then framed into the ecosystem services cascade model to estimate how coastal ecosystems provide protection, in particular describing the service function, flow and benefit. The results are comparative and aim to support integrated land and marine spatial planning. The main drivers of change for the provision of coastal protection come from the widespread anthropogenic pressures in the European coastal zone, for which a short quantitative analysis is provided."	ABSTRACT: "The development of a mathematical model for predicting rate of fire spread and intensity applicable to a wide range of wildland fuels is presented from the conceptual stage through evaluation and demonstration of results to hypothetical fuel models. The model was developed for and is now being used as a basis for appraising fire spread and intensity in the National Fire Danger Rating System. The initial work was done using fuel arrays composed of uniform size particles. Three fuel sizes were tested over a wide range of bulk densities. These were 0.026-inch-square cut excelsior, 114-inch sticks, and 112-inch sticks. The problem of mixed fuel sizes was then resolved by weighting the various particle sizes that compose actual fuel arrays by either surface area or loading, depending upon the feature of the fire being predicted. The model is complete in the sense that no prior knowledge of a fuel's burning characteristics is required. All that is necessary are inputs describing the physical and chemical makeup of the fuel and the environmental conditions in which it is expected to burn. Inputs include fuel loading, fuel depth, fuel particle surface-area-to-volume ratio, fuel particle heat content, fuel particle moisture and mineral content, and the moisture content at which extinction can be expected. Environmental inputs are mean wind velocity and slope of terrain. For heterogeneous mixtures, the fuel properties are entered for each particle size. The model as originally conceived was for dead fuels in a uniform stratum contiguous to the ground, such as litter or grass. It has been found to be useful, however, for fuels ranging from pine needle litter to heavy logging slash and for California brush fields." FARSITE4 will no longer be supported or available for download or further supported. FlamMap6 now includes FARSITE.	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: "Estuaries provide multiple ecosystem services from which humans benefit…We described an approach, with examples, for assessing how local-scale actions affect the extent and distribution of coastal ecosystem services, using the St. Louis River estuary (SLRE) of western Lake Superior as a case study. We based our approach on simple models applied to spatially explicity biophysical data that allows us to map the providing area of ecosystem services at high resolution (10-m^2 pixel) across aquatic and riparian habitats…Aspects of our approach can be adapted by communities for use in support of local decision-making." AUTHOR'S DESCRIPTION: "We derived the decision criteria used to map the IEGS habitat proxy of esocid spawning from habitat suitability information for two species that have similar but not identical spawning habitat and behavior."	AUTHORS DESCRIPTION: "WaSSI simulates monthly water and carbon dynamics at the Hydrologic Unit Code 8 level in the US. Three modules are integrated within the WaSSI model framework. The water balance module computes ecosystem water use, evapotranspiration and the water yield from each watershed. Water yield is sometimes referred to as runoff and can be thought of as the amount of streamflow at the outlet of each watershed due to hydrologic processes in each watershed in isolation without any flow contribution from upstream watersheds. The ecosystem productivity module simulates carbon gains and losses in each watershed or grid cell as functions of evapotranspiration. The water supply and demand module routes and accumulates the water yield through the river network according to topological relationships between adjacent watersheds, subtracts consumptive water use by humans from river flows, and compares water supply to water demand to compute the water supply stress index, or WaSSI."	ABSTRACT: "Riparian areas support a set of river functions and of ecosystem services (ESs). Their role is essential in reducing negative human impacts on river functionality. These aspects could be contained in the River Basin Management Plan, which is the tool for managing and planning freshwater ecosystems in a river basin. In this paper, a new index was developed, namely the River Ecosystem Quality Index (REQI). It is composed of five ecological indices, which assess the quality of riparian areas, and it was first applied to the Marecchia river (central Italy). The REQI was also compared with the Italian River Functionality Index (IFF) and the ESs measured as the capacity of land cover in providing human benefits. Data have shown a decrease in the quality of riparian areas, from the upper to lower part of river, with 53% of all subareas showing medium-quality values…" AUTHOR'S DESCRIPTION: "The evaluation of the quality of the riparian areas is based on the analysis of two fundamental elements of riparian areas: vegetation (characteristics and distribution) and wild birds, measured with standardized methodology and used as indicators of environmental quality and changes...To represent the REQI, each of the five indicators was initially scored with its own range (Figure 3(a)—(e)). Then, all results were redistributed in ranges from 1 to 5, where 5 is the best condition of all indices. Redistributed results were finally summed."	Before delving into Atlantis we would like to provide a little bit of background on the modelling framework and this manual. Atlantis is just one of many marine ecosystem models, originally known as BM2 (BoxModel 2) it was christened Atlantis by Villy Christensen in South Africa in 2001. Marine ecosystem models have existed for more than 50 years, though they have only grown in popular use since the advent of (fast) modern computing power. They have grown from a biophysical focus to include more and more of the human dimensions. This is reflected in the structure of this manual, which sequentially works through the physical then biological before getting into the human dimensions. Atlantis was originally developed with an eye to temperate marine ecosystems and fisheries, though it has grown through time.
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	Supports global and EU biodiversity policy	None identified	None identified	Federal delisting of an area of concern (AOC)	WaSSI can be used to project the regional effects of forest land cover change, climate change, and water withdrawals on river flows, water supply stress, and ecosystem productivity (i.e., carbon sequestration).WaSSI can be used to evaluate trade-offs among management strategies that influence multiple ecosystem services	None identified	None identified
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	Not applicable	submerged aquatic vegetation	No additional description provided	Conterminous US	No additional description provided	Marine and coastal ecosystems
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	Not applicable	No scenarios presented	No scenarios presented	No scenarios presented	The effect of habitat restoration on esocid spawning area was simulated by varying biophysical changes.	No scenarios presented ? Comment: Model can be run from WaSSI website using a historic data set (1961 - 2010) or projections from various climate models representing different emissions scenarios and time periods from recent past to 2099.	No scenarios presented	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-320	EM-337	EM-414	EM-415	EM-439	EM-657	EM-983
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application	Method + Application	Method Only	Method + Application	Method + Application	Method + Application	Method + Application	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	New or revised model	New or revised model	New or revised model	New or revised model	Application of existing model ? Comment: .	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-320	EM-337	EM-414	EM-415	EM-439	EM-657	EM-983
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260	Doc-260 \| Doc-269	None	None	None	None	None	None	None	None	Doc-456 \| Doc-459
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	None	None	None	None	EM-981 \| EM-978 \| EM-985 \| EM-990 \| EM-991

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-320	EM-337	EM-414	EM-415	EM-439	EM-657	EM-983
EM Temporal Extent em.detail.tempExtentHelp ?	2007-2009	2007-2008	2000 - 2007	2006-2007	1992-2010	Not applicable	2010 - 2012	2013	1961-2009	1996-2003 ? Comment: All the ecological analyses are based on the production of a 1:10,000 scale map of land cover with detailed classes for the vegetation obtained by overlapping the photogrammetric analysis (AIMA flight 1996) and the 2003 land-use map.	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-stationary	time-stationary	Not applicable	time-stationary	time-stationary	time-dependent	time-stationary	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	future time	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	discrete	Not applicable	continuous
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	1	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Month	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-320	EM-337	EM-414	EM-415	EM-439	EM-657	EM-983
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Physiographic or Ecological	Geopolitical	Physiographic or Ecological	Geopolitical	Not applicable	Physiographic or ecological	Watershed/Catchment/HUC	Watershed/Catchment/HUC	Watershed/Catchment/HUC	Not applicable
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	Central French Alps	Bilbao Metropolitan Greenbelt	St.Croix, U.S. Virgin Islands	Shoreline of the European Union-27	Not applicable	St. Louis River Estuary	St. Louis River estuary	All 8-digit hydrologic unit codes (HUC-8) in the conterminous USA	Marecchia river catchment	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	>1,000,000 km^2	Not applicable	10-100 km^2	10-100 km^2	>1,000,000 km^2	100-1000 km^2	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-320	EM-337	EM-414	EM-415	EM-439	EM-657	EM-983
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)	Not applicable	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) ? Comment: Spatial grain for computations is the HUC-8. A HUC-12 version is under development. Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha.	spatially distributed (in at least some cases)	Not applicable
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	Not applicable	area, for pixel or radial feature	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	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	Irregular	Not applicable	0.07 m^2 to 0.70 m^2	10 m x 10 m	Computations are at the 8-digit HUC scale. MostHUC-8 watersheds are within a range of 800-8000 km^2 (500-5000 mi^2) in size.	500 m x 1000 m	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-320	EM-337	EM-414	EM-415	EM-439	EM-657	EM-983
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	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-320	EM-337	EM-414	EM-415	EM-439	EM-657	EM-983
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	No	Yes	No	Not applicable	Yes	No	No	Not applicable	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	Yes	No	Yes	No	Not applicable	Yes	No	No	Not applicable	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	p-value \| <0.001 \| unitless Nagelkerke r2 \| 0.30-0.62 \| unitless	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	No	Yes	No	No	No	Yes	No	No	Yes ? Comment: R2 values of the analysis between the REQI, the capacity of land cover to provide ESs, and the Italian River Functionality Quality Index ? IFF.	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	Yes	No	Not applicable	No	No	No	Not applicable	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	Not applicable	No	No	No	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-320	EM-337	EM-414	EM-415	EM-439	EM-657	EM-983
Europe France	Europe France	Europe Spain	None	Europe Cyprus Portugal Malta Greece Austria Latvia Netherlands Sweden Ireland Luxembourg Poland Slovakia Slovenia Bulgaria France Lithuania Romania Hungary United Kingdom Spain Czech Republic Belgium Finland Denmark Italy Germany Estonia	None	North America United States Minnesota Wisconsin	North America United States Minnesota Wisconsin	North America United States	Europe Italy	None

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

EM-65	EM-71	EM-193	EM-260	EM-320	EM-337	EM-414	EM-415	EM-439	EM-657	EM-983
None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Southwestern Atlantic	Temperate North Atlantic Northeast Atlantic Northern European Seas Celtic Seas Faroe Plateau North Sea Mediterranean Sea Mediterranean Sea Western Mediterranean Aegean Sea Alboran Sea Adriatic Sea Ionian Sea	None	None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-320	EM-337	EM-414	EM-415	EM-439	EM-657	EM-983
Centroid Latitude em.detail.ddLatHelp ?	45.05	45.05	43.25	17.75	48.2	-9999	46.72	46.74	39.83	43.89	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	6.4	6.4	-2.92	-64.75	16.35	-9999	-96.13	-92.14	-98.58	12.3	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	NAD83	WGS84	Not applicable	WGS84	WGS84	WGS84	WGS84	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Provided	Provided	Estimated	Estimated	Not applicable	Estimated	Estimated	Estimated	Estimated	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-193	EM-260	EM-320	EM-337	EM-414	EM-415	EM-439	EM-657	EM-983
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Agroecosystems \| Grasslands	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified) \| Forests \| Agroecosystems \| Created Greenspace \| Grasslands \| Scrubland/Shrubland	Near Coastal Marine and Estuarine	Terrestrial Environment (sub-classes not fully specified)	Terrestrial Environment (sub-classes not fully specified)	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Lakes and Ponds ? Comment: Watershed model represents all land areas, major streams and rivers. Since leaf area index, LAI, is an important variable, forests, created greenspaces (e.g., urban forests) and scrub/shrub subclasses are included.	Rivers and Streams \| Inland Wetlands \| Terrestrial Environment (sub-classes not fully specified)	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds \| Near Coastal Marine and Estuarine \| Open Ocean and Seas
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows	Subalpine terraces, grasslands, and meadows.	none	stony coral reef	Coastal zones	Not applicable	Freshwater estuarine system	freshwater estuary	Not applicable	Riparian zone along major river	Multiple
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 is finer than that of the Environmental Sub-class	Ecological scale is coarser than that of the Environmental Sub-class ? Comment: Terrestrial characteristics are aggregated at a broad (HUC-8) scale; different types of aquatic sub-classes are not differentiated.	Ecological scale is finer than that of 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-320	EM-337	EM-414	EM-415	EM-439	EM-657	EM-983
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Community	Not applicable	Guild or Assemblage	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Species ? Comment: Bird species for faunistic index of conservation.	Not applicable

Taxonomic level and name of organisms or groups identified

EM-65	EM-71	EM-193	EM-260	EM-320	EM-337	EM-414	EM-415	EM-439	EM-657	EM-983
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	None Available	Family Esocids	None Available	None Available	None Available

EnviroAtlas URL

EM-65	EM-71	EM-193	EM-260	EM-320	EM-337	EM-414	EM-415	EM-439	EM-657	EM-983
GAP Ecological Systems	None Available	Percent IUCN Status II, Percent GAP Status 1 & 2	None Available	Dasymetric Allocation of Population, National Hydrography Dataset Plus (NHD PlusV2), Percent potentially restorable wetlands on agricultural land	Average Annual Precipitation	Average Annual Precipitation	None Available	Dasymetric Allocation of Population, The National Hydrography Dataset (NHD), Average Annual Precipitation, Wetlands and Streams, Agricultural water use (million gallons/day), The Watershed Boundary Dataset (WBD), Percent Impervious Area, Carbon Storage by Tree Biomass	Ecosystem Markets: Imperiled Species and Habitats	Average Annual Precipitation, Average Annual Daily Potential Wind Energy

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-320	EM-337	EM-414	EM-415	EM-439	EM-657	EM-983
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 flows Gaseous / air flows Storm protection	None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	None	[Ecosystem] Provisioning Nutrition Water Ground water for drinking	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Water conditions Chemical condition of freshwaters	[Non-ecosystem] Regulation & Maintenance by natural physical structures and processes Maintenance of physical, chemical, abiotic conditions By natural chemical and physical processes Not applicable Mediation of flows by natural abiotic structures By soild (mass), liquid and gaseous (air)flows Not applicable [Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of salt waters Mediation of flows Liquid flows Hydrological cycle and water flow maintenance

<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-320	EM-337	EM-414	EM-415	EM-439	EM-657	EM-983
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	Near Coastal Marine/Estuarine (113) Composite (8) Regulation of extreme events Extreme Event Indicator	None	None	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Flow Indicator Rivers and Streams (111) Fauna (4) Fish Flow Indicator Lakes and Ponds (112) Fauna (4) Fish Flow Indicator Wetlands (12) Fauna (4) Fish Flow Indicator	Rivers and Streams (111) Water (3) Liquid water Stock Indicator	None	Open Oceans and Seas (114) Composite (8) Presence of environmental class/subclass Site Indicator Flow Indicator

EcoService Models Library (ESML)

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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)

Comment:

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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)