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-184
EM-193
EM-260
EM-321
EM-414
EM-593
EM-938
EM-940

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-184	EM-193	EM-260	EM-321	EM-414	EM-593	EM-938	EM-940
EM Short Name em.detail.shortNameHelp ?	Green biomass production, Central French Alps	Community flowering date, Central French Alps	ROS (Recreation Opportunity Spectrum), Europe	Cultural ecosystem services, Bilbao, Spain	Coral taxa and land development, St.Croix, VI, USA	Erosion prevention by vegetation, Portel, Portugal	SAV occurrence, St. Louis River, MN/WI, USA	DayCent N2O flux simulation, Ireland	OpenNSPECT v. 1.2	OpenNSPECT v. 1.1, California, U.S.
EM Full Name em.detail.fullNameHelp ?	Green biomass production, Central French Alps	Community weighted mean flowering date, Central French Alps	ROS (Recreation Opportunity Spectrum), Europe	Cultural ecosystem services, Bilbao, Spain	Coral taxa richness and land development, St.Croix, Virgin Islands, USA	Soil erosion prevention provided by vegetation cover, Portel municipality, Portugal	Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA	DayCent simulation N2O flux and climate change, Ireland	OpenNSPECT v. 1.2	OpenNSPECT v. 1.1, California, U.S.
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	EU Biodiversity Action 5	EU Biodiversity Action 5	None ? Comment: EU Mapping Studies	US EPA	EU Biodiversity Action 5	US EPA	None	None	None
EM Source Document ID	260	260	293	191	96	281	330	358	431	433 ? Comment: Additional source for this EM: NOAA, 2012. National Oceanic and Atmospheric Administration. Technical Guide for OpenNSPECT, Version 1.1, p. 44. http://www.csc.noaa.gov/digitalcoast/tools/opennspect.
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.	Paracchini, M.L., Zulian, G., Kopperoinen, L., Maes, J., Schägner, J.P., Termansen, M., Zandersen, M., Perez-Soba, M., Scholefield, P.A., and Bidoglio, G.	Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H.	Oliver, L. M., Lehrter, J. C. and Fisher, W. S.	Guerra, C.A., Pinto-Correia, T., Metzger, M.J.	Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke	Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M.	Eslinger, David L., H. Jamieson Carter, Matt Pendleton, Shan Burkhalter, Margaret Allen	Morrison, K. D. and C. A. Kolden
Document Year em.detail.documentYearHelp ?	2011	2011	2014	2013	2011	2014	2013	2010	2012	2015
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 cultural ecosystem services: A framework to assess the potential for outdoor recreation across the EU	Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning	Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands	Mapping soil erosion prevention using an ecosystem service modeling framework for integrated land management and policy	Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary	Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture	“OpenNSPECT: The Open-source Nonpoint Source Pollution and Erosion Comparison Tool.” NOAA Office for Coastal Management, Charleston, South Carolina. Accessed (11/2022) at https://coast.noaa.gov/digitalcoast/tools/opennspect.html	Modeling the impacts of wildfire on runoff and pollutant transport from coastal watersheds to the nearshore environment
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 journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Webpage	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-184	EM-193	EM-260	EM-321	EM-414	EM-593	EM-938	EM-940
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	https://coast.noaa.gov/digitalcoast/tools/opennspect.html	https://coast.noaa.gov/digitalcoast/tools/opennspect.html
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Sandra Lavorel	Maria Luisa Paracchini	Izaskun Casado-Arzuaga	Leah Oliver	Carlos A. Guerra	Ted R. Angradi	M. Abdalla	Not reported	Crystal A. Kolden
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	Joint Research Centre, Institute for Environment and Sustainability, Via E.Fermi, 2749, I-21027 Ispra (VA), Italy	Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain	National Health and Environmental Research Effects Laboratory	Instituto de Ciências Agrárias e Ambientais Mediterrânicas, Universidade de Évora, Pólo da Mitra, Apartado 94, 7002-554 Évora, Portugal	U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA	Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland	NOAA Coastal Services Center, 2234 South Hobson Avenue Charleston, South Carolina 29405-2413	Not reported
Contact Email	sandra.lavorel@ujf-grenoble.fr	sandra.lavorel@ujf-grenoble.fr	luisa.paracchini@jrc.ec.europa.eu	izaskun.casado@ehu.es	leah.oliver@epa.gov	cguerra@uevora.pt	angradi.theodore@epa.gov	abdallm@tcd.ie	Not reported	ckolden@uidaho. Edu

EM Description

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-184	EM-193	EM-260	EM-321	EM-414	EM-593	EM-938	EM-940
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: "Research on ecosystem services mapping and valuing has increased significantly in recent years. However, compared to provisioning and regulating services, cultural ecosystem services have not yet beenfully integrated into operational frameworks. One reason for this is that transdisciplinarity is required toaddress the issue, since by definition cultural services (encompassing physical, intellectual, spiritual inter-actions with biota) need to be analysed from multiple perspectives (i.e. ecological, social, behavioural).A second reason is the lack of data for large-scale assessments, as detailed surveys are a main sourceof information. Among cultural ecosystem services, assessment of outdoor recreation can be based ona large pool of literature developed mostly in social and medical science, and landscape and ecologystudies. This paper presents a methodology to include recreation in the conceptual framework for EUwide ecosystem assessments (Maes et al., 2013), which couples existing approaches for recreation man-agement at country level with behavioural data derived from surveys and population distribution data.The proposed framework is based on three components: the ecosystem function (recreation potential),the adaptation of the Recreation Opportunity Spectrum framework to characterise the ecosystem serviceand the distribution of potential demand in the EU."	ABSTRACT "This paper presents a method to quantify cultural ecosystem services (ES) and their spatial distribution in the landscape based on ecological structure and social evaluation approaches. The method aims to provide quantified assessments of ES to support land use planning decisions. A GIS-based approach was used to estimate and map the provision of recreation and aesthetic services supplied by ecosystems in a peri-urban area located in the Basque Country, northern Spain. Data of two different public participation processes (frequency of visits to 25 different sites within the study area and aesthetic value of different landscape units) were used to validate the maps. Three maps were obtained as results: a map showing the provision of recreation services, an aesthetic value map and a map of the correspondences and differences between both services. The data obtained in the participation processes were found useful for the validation of the maps. A weak spatial correlation was found between aesthetic quality and recreation provision services, with an overlap of the highest values for both services only in 7.2 % of the area. A consultation with decision-makers indicated that the results were considered useful to identify areas that can be targeted for improvement of landscape and recreation management."	AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294)	ABSTRACT: "We present an integrative conceptual framework to estimate the provision of soil erosion prevention (SEP) by combining the structural impact of soil erosion and the social–ecological processes that allow for its mitigation. The framework was tested and illustrated in the Portel municipality in Southern Portugal, a Mediterranean silvo-pastoral system that is prone to desertification and soil degradation. The results show a clear difference in the spatial and temporal distribution of the capacity for ecosystem service provision and the actual ecosystem service provision." AUTHOR'S DESCRIPTION: "To begin assessing the contribution of SEP we need to identify the structural impact of soil erosion, that is, the erosion that would occur when vegetation is absent and therefore no ES is provided. It determines the potential soil erosion in a given place and time and is related to rainfall erosivity (that is, the erosive potential of rainfall), soil erodibility (as a characteristic of the soil type) and local topography. Although external drivers can have an effect on these variables (for example, climate change), they are less prone to be changed directly by human action. The actual ES provision reduces the total amount of structural impact, and we define the remaining impact as the ES mitigated impact. We can then define the capacity for ES provision as a key component to determine the fraction of the structural impact that is mitigated…Following the conceptual outline, we will estimate the SEP provided by vegetation cover using an adaptation of the Universal Soil Loss Equation (USLE)."	ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake."	Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios.	"This open-source version of the Nonpoint Source Pollution and Erosion Comparison Tool is used to investigate potential water quality impacts from climate change and development to other land uses. The downloadable tool is designed to be broadly applicable for coastal and noncoastal areas alike. Tool functions simulate erosion, pollution, and the accumulation from overland flow. OpenNSPECT uses spatial elevation data to calculate flow direction and flow accumulation throughout a watershed. To do this, land cover, precipitation, and soils data are processed to estimate runoff volume at both the local and watershed levels. Coefficients representing the contribution of each land cover class to the expected pollutant load are also applied to land cover data to approximate total pollutant loads. These coefficients are taken from published sources or can be derived from local water quality studies. The output layers display estimates of runoff volume, pollutant loads, pollutant concentration, and total sediment yield. Requires MapWindow GIS v.4.8.8 (open source software)"	ABSTRACT: "Wildfire is a common disturbance that can significantly alter vegetation in watersheds and affect the rate of sediment and nutrient transport to adjacent nearshore oceanic environments. Changes in runoff resulting from heterogeneous wildfire effects are not well-understood due to both limitations in the field measurement of runoff and temporally-limited spatial data available to parameterize runoff models. We apply replicable, scalable methods for modeling wildfire impacts on sediment and nonpoint source pollutant export into the nearshore environment, and assess relationships between wildfire severity and runoff. Nonpoint source pollutants were modeled using a GIS-based empirical deterministic model parameterized with multi-year land cover data to quantify fire-induced increases in transport to the nearshore environment. Results indicate post-fire concentration increases in phosphorus by 161 percent, sediments by 350 percent and total suspended solids (TSS) by 53 percent above pre-fire years. Higher wildfire severity was associated with the greater increase in exports of pollutants and sediment to the nearshore environment, primarily resulting from the conversion of forest and shrubland to grassland. This suggests that increasing wildfire severity with climate change will increase potential negative impacts to adjacent marine ecosystems. The approach used is replicable and can be utilized to assess the effects of other types of land cover change at landscape scales. It also provides a planning and prioritization framework for management activities associated with wildfire, including suppression, thinning, and post-fire rehabilitation, allowing for quantification of potential negative impacts to the nearshore environment in coastal basins."
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	None identified	Land management, ecosystem management, response to EU 2020 Biodiversity Strategy	Not applicable	None identified	None identified	climate change	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	No additional description provided	Northern Spain; Bizkaia region	nearshore; <1.5 km offshore; <12 m depth	Open savannah-like forest of cork (Quercus suber) and holm (Quercus ilex) oaks, with trees of different ages randomly dispersed in changing densities, and pastures in the under cover. The pastures are mostly natural in a mosaic with patches of shrubs, which differ in size and the distribution depends mainly on the grazing intensity. Shallow, poor soils are prone to erosion, especially in areas with high grazing pressure.	submerged aquatic vegetation	Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C	No additional description provided	Central California coast includes twelve adjacent watersheds covering 87,638 ha and rises steeply from sea level to just below 1800 m within a few km from the coast, and experiences a Mediterranean climate, with fire season typically lasting from June to November. Precipitation is dependent on elevation ranging from 65 cm near the coast to over 130 cm at ridge top. Three ecological zones occur within the study area. These zones are comprised of grasslands, coastal sage scrub, chaparral, oak forests, mixed broadleaf evergreen forest, and coniferous forests.
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	No scenarios presented	Not applicable	Different land management practices as represented by the comparison of different grazing intensities (i.e., livestock densities) in the whole study area and in three Civil Parishes within the study area	No scenarios presented	air temperature, precipitation, Atmospheric CO2 concentrations	No scenarios presented	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-184	EM-193	EM-260	EM-321	EM-414	EM-593	EM-938	EM-940
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 Only	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	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	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-65	EM-71	EM-184	EM-193	EM-260	EM-321	EM-414	EM-593	EM-938	EM-940
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260	Doc-260 \| Doc-269	Doc-290 \| Doc-291 \| Doc-289	None	None	Doc-282 \| Doc-283 \| Doc-284 \| Doc-285	None	None	None	Doc-431
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	EM-598	EM-940	EM-938

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-184	EM-193	EM-260	EM-321	EM-414	EM-593	EM-938	EM-940
EM Temporal Extent em.detail.tempExtentHelp ?	2007-2009	2007-2008	Not reported	2000 - 2007	2006-2007	January to December 2003	2010 - 2012	1961-1990	Not applicable	2005-2008
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-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	future time	Not applicable	both	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	discrete	Not applicable	discrete	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	1	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	Month	Not applicable	Day	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-184	EM-193	EM-260	EM-321	EM-414	EM-593	EM-938	EM-940
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Physiographic or Ecological	Geopolitical	Geopolitical	Physiographic or Ecological	Geopolitical	Physiographic or ecological	Point or points	Not applicable	Watershed/Catchment/HUC
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	Central French Alps	European Union countries	Bilbao Metropolitan Greenbelt	St.Croix, U.S. Virgin Islands	Portel municipality	St. Louis River Estuary	Oak Park Research centre	Not applicable	Big Sur region, central California
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	10-100 km^2	>1,000,000 km^2	100-1000 km^2	10-100 km^2	100-1000 km^2	10-100 km^2	1-10 ha	Not applicable	100-1000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-184	EM-193	EM-260	EM-321	EM-414	EM-593	EM-938	EM-940
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 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) ? 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 lumped (in all cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	Not applicable	area, for pixel or radial feature	area, for pixel or radial feature	Not applicable	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	20 m x 20 m	100 m x 100 m	2 m x 2 m	Not applicable	250 m x 250 m	0.07 m^2 to 0.70 m^2	Not applicable	30 m	irregular

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-184	EM-193	EM-260	EM-321	EM-414	EM-593	EM-938	EM-940
EM Computational Approach em.detail.emComputationalApproachHelp ?	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
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	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-184	EM-193	EM-260	EM-321	EM-414	EM-593	EM-938	EM-940
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	No	No	Yes	No	Yes	No	Not applicable	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	Yes	No	No	Yes	No	Yes	Yes ? Comment: for N2O fluxes	Not applicable	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	Adjusted R \| 69.9 \| Not applicable	R squared \| 45.2 \| Not applicable	None	None	R squared (taxa richness) \| 0.74 \| Not applicable p-value \| 0.0020 \| unitless	None	p-value \| <0.001 \| unitless Nagelkerke r2 \| 0.30-0.62 \| unitless	r squared \| 0.32 \| uniteless	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	No	No	Yes	No	No	Yes	Yes	Not applicable	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	Yes	No	No	No	Not applicable	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	No	No	No	Not applicable	No
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

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-184	EM-193	EM-260	EM-321	EM-414	EM-593	EM-938	EM-940
Europe France	Europe France	Europe Cyprus Portugal Malta Greece Austria Latvia Netherlands Sweden Ireland Luxembourg Poland Slovakia Slovenia Bulgaria France Lithuania Croatia Romania Hungary United Kingdom Spain Czech Republic Belgium Finland Denmark Italy Germany Estonia	Europe Spain	None	Europe Portugal	North America United States Minnesota Wisconsin	Europe Ireland	None	North America United States California

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

EM-65	EM-71	EM-184	EM-193	EM-260	EM-321	EM-414	EM-593	EM-938	EM-940
None	None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Southwestern Atlantic	None	None	None	None	Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Northern California

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-184	EM-193	EM-260	EM-321	EM-414	EM-593	EM-938	EM-940
Centroid Latitude em.detail.ddLatHelp ?	45.05	45.05	48.2	43.25	17.75	38.3	46.72	52.86	Not applicable	35.96
Centroid Longitude em.detail.ddLongHelp ?	6.4	6.4	16.35	-2.92	-64.75	-7.7	-96.13	6.54	Not applicable	-121.43
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	NAD83	WGS84	WGS84	None provided	Not applicable	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Provided	Estimated	Provided	Estimated	Estimated	Estimated	Provided	Not applicable	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-184	EM-193	EM-260	EM-321	EM-414	EM-593	EM-938	EM-940
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Agroecosystems \| Grasslands	Terrestrial Environment (sub-classes not fully specified)	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified) \| Forests \| Agroecosystems \| Created Greenspace \| Grasslands \| Scrubland/Shrubland	Near Coastal Marine and Estuarine	Terrestrial Environment (sub-classes not fully specified) \| Forests \| Agroecosystems \| Scrubland/Shrubland	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Agroecosystems	Aquatic Environment (sub-classes not fully specified) \| Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified)	Rivers and Streams \| Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows	Subalpine terraces, grasslands, and meadows.	Not applicable	none	stony coral reef	Silvo-pastoral system	Freshwater estuarine system	farm pasture	Coastal and non-coastal	Coastal watersheds
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 coarser than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-65	EM-71	EM-184	EM-193	EM-260	EM-321	EM-414	EM-593	EM-938	EM-940
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Community	Not applicable	Not applicable	Guild or Assemblage	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-65	EM-71	EM-184	EM-193	EM-260	EM-321	EM-414	EM-593	EM-938	EM-940
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	None Available	None Available	None Available

EnviroAtlas URL

EM-65	EM-71	EM-184	EM-193	EM-260	EM-321	EM-414	EM-593	EM-938	EM-940
GAP Ecological Systems	None Available	Dasymetric Allocation of Population, GAP Ecological Systems, Ecosystem Markets: Imperiled Species and Habitats	Percent IUCN Status II, Percent GAP Status 1 & 2	None Available	Average Annual Precipitation	Average Annual Precipitation	GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day)	Average Annual Precipitation	Average Annual Precipitation

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-184	EM-193	EM-260	EM-321	EM-414	EM-593	EM-938	EM-940
None	None	[Ecosystem] Cultural Spiritual, symbolic and other interactions with biota, ecosystems, and land-/seascapes [environmental settings] Spiritual and/or emblematic Symbolic Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Physical use of land-/seascapes in different environmental settings Experiential use of plants, animals and land-/seascapes in different environmental settings Intellectual and representative interactions Aesthetic Entertainment	[Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Physical use of land-/seascapes in different environmental settings Experiential use of plants, animals and land-/seascapes in different environmental settings Intellectual and representative interactions Aesthetic	[Non-ecosystem] Regulation & Maintenance by natural physical structures and processes Maintenance of physical, chemical, abiotic conditions By natural chemical and physical processes Not applicable	[Ecosystem] Regulation & Maintenance Mediation of flows Mass flows Buffering and attenuation of mass flows Mass stabilisation and control of erosion rates	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection 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	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Hydrological cycle and water flow maintenance Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[Ecosystem] Regulation & Maintenance Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by 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-184	EM-193	EM-260	EM-321	EM-414	EM-593	EM-938	EM-940
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 Ice and Snow (26) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Forests (21) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Tundra (25) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Grasslands (23) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scrubland/Shrubland (24) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Lakes and Ponds (112) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Wetlands (12) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator	Near Coastal Marine/Estuarine (113) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scapes: views, sounds and scents of land, sea, sky or a combination Site Characteristic Indicator Forests (21) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scapes: views, sounds and scents of land, sea, sky or a combination Site Characteristic Indicator Grasslands (23) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scapes: views, sounds and scents of land, sea, sky or a combination Site Characteristic Indicator Scrubland/Shrubland (24) Composite (8) Presence of environmental class/subclass Site Characteristic Indicator Scapes: views, sounds and scents of land, sea, sky or a combination Site Characteristic Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Invertebrates Quality Indicator	Forests (21) Soil (2) Specific types of soil Flow Indicator Scrubland/Shrubland (24) Soil (2) Specific types of soil Flow Indicator Agroecosystems (22) Soil (2) Specific types of soil Flow Indicator	None	Agroecosystems (22) Flora (5) Green biomass Stock Indicator	None	None

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EM Locations, Environments, Ecology

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

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

Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

Taxonomic level and name of organisms or groups identified

EnviroAtlas URL

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

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

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

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