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.

Compare Criteria:

Show Criteria

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Comparing:

EM-65
EM-71
EM-79
EM-119
EM-193
EM-260
EM-303
EM-392
EM-414
EM-454
EM-496
EM-848
EM-857
EM-863
EM-941
EM-1019

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID

EM-65

EM-71

EM-79

EM-119

EM-193

EM-260

EM-303

EM-392

EM-414

EM-454

EM-496

EM-848

EM-857

EM-863

EM-941

EM-1019

EM Short Name

Green biomass production, Central French Alps

Community flowering date, Central French Alps

Divergence in flowering date, Central French Alps

Landscape importance for wildlife products, Europe

Cultural ecosystem services, Bilbao, Spain

Coral taxa and land development, St.Croix, VI, USA

Biological pest control, Uppland Province, Sweden

EPA H2O, Tampa Bay Region, FL,USA

SAV occurrence, St. Louis River, MN/WI, USA

Reef snorkeling opportunity, St. Croix, USVI

Sed. denitrification, St. Louis R., MN/WI, USA

National invertebrate community rank index

SLAMM

SLAMM, Tampa Bay, FL, USA

ESTIMAP - Pollination potential, Iran

SMOKE emissions model, Asia

EM Full Name

Green biomass production, Central French Alps

Community weighted mean flowering date, Central French Alps

Functional divergence in flowering date, Central French Alps

Landscape importance for wildlife products, Europe

Cultural ecosystem services, Bilbao, Spain

Coral taxa richness and land development, St.Croix, Virgin Islands, USA

Biological control of agricultural pests by natural predators, Uppland Province, Sweden

EPA H2O, Tampa Bay Region, FL, USA

Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA

Relative snorkeling opportunity (in reef), St. Croix, USVI

Sediment denitrification, St. Louis River, MN/WI, USA

National invertebrate community ranking index (NICRI)

Sea Level Affecting Marshes Model (SLAMM)

SLAMM (sea level affecting marshes model), Tampa Bay, Florida, USA

ESTIMAP - Pollination potential, Iran

Development of an anthropogenic emissions processing system for Asia using SMOKE

EM Source or Collection

EU Biodiversity Action 5

None ?

US EPA

None

US EPA

None

EM Source Document ID

260

228

191

299

321

330

335

333

407

412 ?

415 ?

434

481

Document Author

Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.

Haines-Young, R., Potschin, M. and Kienast, F.

Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H.

Oliver, L. M., Lehrter, J. C. and Fisher, W. S.

Jonsson, M., Bommarco, R., Ekbom, B., Smith, H.G., Bengtsson, J., Caballero-Lopez, B., Winqvist, C., and Olsson, O.

Ranade, P., Soter, G., Russell, M., Harvey, J., and K. Murphy

Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke

Yee, S. H., Dittmar, J. A., and L. M. Oliver

Brent J. Bellinger, Terri M. Jicha, LaRae P. Lehto, Lindsey R. Seifert-Monson, David W. Bolgrien, Matthew A. Starry, Theodore R. Angradi, Mark S. Pearson, Colleen Elonen, and Brian H. Hill

Cuffney, Tom

Warren Pinnacle Consulting, Inc.

Sherwood, E. T. and H. S. Greening

Rahimi, E., Barghjelveh, S., and P. Dong

Woo, J.H., Choi, K.C., Kim, H.K., Baek, B.H., Jang, M., Eum, J.H., Song, C.H., Ma, Y.I., Sunwoo, Y., Chang, L.S. and Yoo, S.H.

Document Year

2011

2012

2013

2011

2014

2015

2013

2014

2003

2016

2014

2020

2012

Document Title

Using plant functional traits to understand the landscape distribution of multiple ecosystem services

Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs

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

Ecological production functions for biological control services in agricultural landscapes

EPA H20 User Manual

Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary

Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI

Sediment nitrification and denitrification in a Lake Superior estuary

Invertebrate Status Index

SLAMM 6.7 beta, User's Manual

Potential impacts and management implications of climate change on Tampa Bay estuary critical coastal habitats

Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran)

Development of an anthropogenic emissions processing system for Asia using SMOKE

Document Status

Peer reviewed and published

Other or unclear (explain in Comment)

Peer reviewed and published

Comments on Status

Published journal manuscript

Published EPA report

Published journal manuscript

Published report

User's Guide from model website

Published journal manuscript

Software and Access

EM ID

EM-65

EM-71

EM-79

EM-119

EM-193

EM-260

EM-303

EM-392

EM-414

EM-454

EM-496

EM-848

EM-857

EM-863

EM-941

EM-1019

EM Software Access info Exit

Not applicable

http://www.epa.gov/ged/tbes/EPAH2O

Not applicable

http://warrenpinnacle.com/prof/SLAMM/index.html

http://warrenpinnacle.com/prof/SLAMM/index.html com/prof/SLAMM/index.html

Not applicable

https://www.cmascenter.org/smoke/

Contact Name

Sandra Lavorel

Marion Potschin

Izaskun Casado-Arzuaga

Leah Oliver

Mattias Jonsson

Marc J. Russell, Ph.D.

Ted R. Angradi

Susan H. Yee

Brent J. Bellinger ?

Tom Cuffney

Jonathan Clough

Edward T. Sherwood

Ehsan Rahini

Jung-Hun Woo

Contact Address

Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France

Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom

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

Department of Ecology, Swedish University of Agricultural Sciences, PO Box 7044, SE-750 07 Uppsala, Sweden

USEPA GED, One Sabine Island Dr., Gulf Breeze, FL 32561

U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA

US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA

3916 Sunset Ridge Rd, Raleigh, NC 27607

Warren Pinnacle Consulting, Inc. PO Box 315, Waitsfield VT, 05673

Tampa Bay Estuary Program, 263 13th Avenue South, St. Petersburg, FL 33701, USA

Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran

Department of Advanced Technology Fusion, Room 812, San-Hak Bldg., Konkuk University, Seoul, South Korea

Contact Email

sandra.lavorel@ujf-grenoble.fr

marion.potschin@nottingham.ac.uk

izaskun.casado@ehu.es

leah.oliver@epa.gov

mattias.jonsson@slu.se

russell.marc@epa.gov

angradi.theodore@epa.gov

yee.susan@epa.gov

bellinger.brent@epa.gov

tcuffney@usgs.gov

jclough@warrenpinnacle.com

esherwood@tbep.org

ehsanrahimi666@gmail.com

jwoo@konkuk.ac.kr

EM Description

EM ID

EM-65

EM-71

EM-79

EM-119

EM-193

EM-260

EM-303

EM-392

EM-414

EM-454

EM-496

EM-848

EM-857

EM-863

EM-941

EM-1019

Summary Description

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: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Wildlife Products” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain, adjacency to coastal and wetland ecosystems, as well as adjacency to landscape protection zones." AUTHOR'S DESCRIPTION: "Wildlife Products…includes the provisioning of all non-edible raw material products that are gained through non-agriculutural practices or which are produced as a by-product of commercial and non-commercial forests, primarily in non-intensively used land or semi-natural and natural areas."

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 develop a novel, mechanistic landscape model for biological control of cereal aphids, explicitly accounting for the influence of landscape composition on natural enemies varying in mobility, feeding rates and other life history traits. Finally, we use the model to map biological control services across cereal fields in a Swedish agricultural region with varying landscape complexity. The model predicted that biological control would reduce crop damage by 45–70% and that the biological control effect would be higher in complex landscapes. In a validation with independent data, the model performed well and predicted a significant proportion of biological control variation in cereal fields. However, much variability remains to be explained, and we propose that the model could be improved by refining the mechanistic understanding of predator dynamics and accounting for variation in aphid colonization."

AUTHORS DESCRIPTION: "EPA H2O is a GIS based demonstration tool for assessing ecosystem goods and services (EGS). It was developed as a preliminary assessment tool in support of research being conducted in the Tampa Bay watershed. It provides information, data, approaches and guidance that communities can use to examine alternative land use scenarios in the context of nature’s benefits to the human community. . . EPA H2O allows users for the Tampa Bay estuary and its watershed to: • Gain a greater understanding of the significance of EGS, • Explore the spatial distribution of EGS and other ecosystem features, • Obtain map and summary statistics of EGS production's potential value, • Analyze and compare potential impacts from predicted development scenarios or user specified changes in land use patterns on EGS production's potential value EPA H2O is designed for analyzing data at neighborhood to regional scales.. . The tool is transportable to other locations if the required data are available. . . .

ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake."

ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…Synthesis of scientific literature and expert opinion can be used to estimate the relative potential for recreational opportunities across different benthic habitat types (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to recreational opportunities as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative recreational opportunity j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A.palmata, Montastraea reef, patch reef, and dense or sparse gorgonians), and Mij is the magnitude associated with each habitat for a given metric j: snorkeling opportunity"

ABSTRACT: "Inorganic nitrogen (N) transformations and removal in aquatic sediments are microbially mediated, and rates influence N-transport. In this study we related physicochemical properties of a large Great Lakes embayment, the St. Louis River Estuary (SLRE) of western Lake Superior, to sediment N-transformation rates. We tested for associations among rates and N-inputs, vegetation biomass, and temperature. We measured rates of nitrification (NIT), unamended base denitrification (DeNIT), and potential denitrification [denitrifying enzyme activity (DEA)] in 2011 and 2012 across spatial and depth zones…Nitrogen cycling rates were spatially and temporally variable, but we modeled how alterations to water depth and N-inputs may impact DeNIT rates." AUTHOR'S DESCRIPTION: "We used different survey designs in 2011 and 2012. Both designs were based on area-weighted probability sampling methods, similar to those developed for EPA's Environmental Monitoring and Assessment Program (EMAP) (Crane et al., 2005; Stevens and Olsen, 2003, 2004). Sampling sites were assigned to spatial zones: “harbor” (river km 0–13), “bay” (river km 13–24), or “river” (river km 24–35) (Fig. 1). Sites were also grouped by depth zones (“shallow,” <1 m; “intermediate,” 1–2 m; and “deep,” >2 m). In 2011 (“vegetated-habitat survey”), the sample frame consisted of areas of emergent and submergent vegetation in the SLRE… The resulting sample frame included 2370 ha of potentially vegetated area out of a total SLRE area of 4378 ha. Sixty sites were distributed across the total vegetated area in each spatial zone using an uneven spatially balanced probabilistic design. Vegetated areas were more prevalent, and thus had greater sampling effort, in the bay (n = 33) and river (n = 17) than harbor (n=10) zones, and in the shallow (n=44) and intermediate (n =14) than deep (n =2) zones. All sampling was done in July. In 2012 a probabilistic sampling design (“estuary-wide survey”) was implemented to determine N-cycling rates for the entire SLRE (not just vegetated areas as in 2011). Thirty sites unevenly distributed across spatial and depth zones were sampled monthly in May–September (Fig. 1). Area weighting for each sampled site reflects the SLRE area attributable to each sample by month, spatial zone, and depth zone." "…we were able to create significant predictive models for NIT and DeNIT rates using linear combinations of physiochemical parameters…" "…Simulations of changes in DeNIT rates in response to altered water depth and surface NOx-N concentration for spring (Fig. 4A) and summer (Fig. 4B) show that for a given season, altering water depths would have a greater influence on DeNIT than rising NO3- concentration."

ABSTRACT: "The Invertebrate Status Index is a multimetric index that was derived for the NAWQA Program to provide a simple national characterization of benthic invertebrate communities. This index— referred to here as the National Invertebrate Community Ranking Index (NICRI)—provides a simple method of placing community conditions within the context of all sites sampled by the NAWQA Program. The multimetric index approach is the most commonly used method of characterizing biological conditions within the U.S. (Barbour and others, 1999). Using this approach, communities may be compared by considering how individual metrics vary among sites or by combining individual metrics into a single composite (i.e., multimetric) index and examining how this single index varies among sites. Combining metrics into a single multimetric index simplifies the presentation of results (Barbour and others, 1999) and minimizes weaknesses that may be associated with individual metrics (Ohio EPA, 1987a,b). The NICRI is a multimetric index that combines 11 metrics (RICH, EPTR, CG_R, PR_R, EPTRP, CHRP, V2DOMP, EPATOLR, EPATOLA, DIVSHAN, and EVEN; Table 1) into a single, nationally consistent, composite index. The NICRI was used to rank 140 sites of the FY94 group of study units, with median values used for sites where data were available for multiple reaches and(or) multiple years. Average metric scores were then rescaled using the PERCENTRANK function and multiplied by 100 to produce a final NICRI score that ranged from 0 (low ranking relative to other NAWQA Program sites and presumably diminished community conditions) to 100 (high ranking relative to other NAWQA Program sites and presumably excellent community conditions). "

"The Sea Level Affecting Marshes Model (SLAMM) simulates the dominant processes involved in wetland conversions and shoreline modifications during long-term sea level rise. Map distributions of wetlands are predicted under conditions of accelerated sea level rise, and results are summarized in tabular and graphical form. The newest versions of SLAMM include a Roads module to investigate the inundation frequency of road infrastructure and a stochastic uncertainty analysis module for asessing the effects of input data uncertainty on model predictions. The uncertainty analysis module can be used to produce confidence intervals for model predictions and likelihood maps."

ABSTRACT: "The Tampa Bay estuary is a unique and valued ecosystem that currently thrives between subtropical and temperate climates along Florida’s west-central coast. The watershed is considered urbanized (42 % lands developed); however, a suite of critical coastal habitats still persists. Current management efforts are focused toward restoring the historic balance of these habitat types to a benchmark 1950s period. We have modeled the anticipated changes to a suite of habitats within the Tampa Bay estuary using the sea level affecting marshes model (SLAMM) under various sea level rise (SLR) scenarios. Modeled changes to the distribution and coverage of mangrove habitats within the estuary are expected to dominate the overall proportions of future critical coastal habitats. Modeled losses in salt marsh, salt barren, and coastal freshwater wetlands by 2100 will significantly affect the progress achieved in ‘‘Restoring the Balance’’ of these habitat types over recent periods…"

Abstract: ". ..we used the ESTIMAP model to improve the results of the Lonsdorf model. For this, we included the effects of roads, railways, rivers, wetlands, lakes, altitude, climate, and ecosystem boundaries in the ESTIMAP modeling and compared the results with the Lonsdorf model. The results of the Lonsdorf model showed that the majority of Iran had a very low potential for providing pollination service and only three percent of the northern and western parts of Iran had high potential. However, the results of the ESTIMAP model showed that 16% of Iran had a high potential to provide pollination that covers most of the northern and southern parts of the country. The results of the ESTIMAP model for pollination mapping in Iran showed the Lonsdorf model of estimating pollination service can be improved through considering other relevant factors."

Air quality modeling is a useful methodology to investigate air quality degradation in various locations and to analyze effectiveness of emission reduction plans. A comprehensive air quality model usually requires a coordinated set of emissions input of all necessary chemical species. We have developed an anthropogenic emissions processing system for Asia in support of air quality modeling and analysis over Asia (named SMOKE-Asia). The SMOKE (Sparse Matrix Operator kernel Emissions) system, which was developed by U.S. EPA and has been maintained by the Carolina Environmental Program (CEP) of the University of North Carolina, was used to develop our emissions processing system. A merged version of INTEX 2006 and TRACE-P 2000 inventories was used as an initial Asian emissions inventory. The IDA (Inventory Data Analyzer) format was used to create SMOKE-ready emissions. Source Classification Codes (SCCs) and country/state/county (FIPS) code, which are the two key data fields of SMOKE IDA data structure, were created for Asia. The 38 SCCs and 2752 FIPS codes were allocated to our SMOKE-ready emissions for more comprehensive processing. US EPA’s MIMS (Multimedia Integrated Modeling System) Spatial Allocator software, along with many global and regional GIS shapes, were used to create spatial allocation profiles for Asia. Temporal allocation and chemical speciation profiles were partly regionalized using Asia-based studies. Initial data production using the developed SMOKE-Asia system was successfully performed. NOx and VOC emissions for the year 2009 were projected to be increased by 50% from those of 1997. The emission hotspots, such as large cities and large point sources, are distinguished in the domain due to spatial allocation. Regional emission peaks were distinguished due to temporally resolved emission information. The PAR (Paraffin carbon bond) and XYL (Xylene and other polyalkyl aromatics) showed the first and second largest emission rate among VOC species. Most of point source emissions are located in layers 3 to 4, which the altitude range reaches 310–550 m AGL. Qualitative inter-comparison between model output and ground/satellite measurement showed good agreements in terms of spatial and temporal patterns. We expect that the result of this study will provide better air quality modeling inputs, which will act as a major step to improve our understanding of Asian air quality.

Specific Policy or Decision Context Cited

None identified

Land management, ecosystem management, response to EU 2020 Biodiversity Strategy

Not applicable

None identified

None reported

None identified

None Identified

None identified

None reported

None provided

Biophysical Context

Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes

Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes

Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes

No additional description provided

Northern Spain; Bizkaia region

nearshore; <1.5 km offshore; <12 m depth

Spring-sown cereal croplands, where the bird chearry-oat aphid is a key aphid pest. The aphid colonizes the crop during late May and early June, depending on weather and location. The colonization phase is followed by a brief phase of rapid exponential population growth by wingless aphids, continuing until about the time of crop heading, in late June or early July. After heading, aphid populations usually decline rapidly in the crop due to decreased plant quality and migration to grasslands. The aphids are attacked by a complex of arthropod natural enemies, but parasitism is not important in the region and therefore not modelled here.

Not applicable

submerged aquatic vegetation

No additional description provided

Streams and Rivers

No additional description provided

None additional

Asia

EM Scenario Drivers

No scenarios presented

Not applicable

No scenarios presented

Land Use, EGS algorithm values,

No scenarios presented

N/A

Projected sea level rise

Varying sea level rise (baseline - 2m), and two habitat adaption strategies

N/A

EM Relationship to Other EMs or Applications

EM ID

EM-65

EM-71

EM-79

EM-119

EM-193

EM-260

EM-303

EM-392

EM-414

EM-454

EM-496

EM-848

EM-857

EM-863

EM-941

EM-1019

Method Only, Application of Method or Model Run

Method + Application

Method + Application (multiple runs exist) View EM Runs

Method + Application

Method Only

Method + Application (multiple runs exist) View EM Runs

Method + Application

New or Pre-existing EM?

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-65

EM-71

EM-79

EM-119

EM-193

EM-260

EM-303

EM-392

EM-414

EM-454

EM-496

EM-848

EM-857

EM-863

EM-941

EM-1019

Document ID for related EM

Doc-260

Doc-260 | Doc-269

Doc-231 | Doc-228

None

Doc-413 | Doc-412 | Doc-415

Doc-412 | Doc-413

Doc-432

Doc-478

EM ID for related EM

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

EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83

EM-99 | EM-120 | EM-121 | EM-162 | EM-164 | EM-165 | EM-122 | EM-123 | EM-124 | EM-125 | EM-166 | EM-170 | EM-171

None

EM-850

EM-863 | EM-864 | EM-865 | EM-866 | EM-867 | EM-868 | EM-869 | EM-870 | EM-871 | EM-872

EM-857

EM-939

EM-1012 | EM-1021

EM Modeling Approach

EM Relationship to Time

EM ID

EM-65

EM-71

EM-79

EM-119

EM-193

EM-260

EM-303

EM-392

EM-414

EM-454

EM-496

EM-848

EM-857

EM-863

EM-941

EM-1019

EM Temporal Extent

2007-2009

2007-2008

2000

2000 - 2007

2006-2007

2009

Not applicable

2010 - 2012

2006-2007, 2010

July 2011 to September 2012 ?

1991-1994

Not applicable

2002-2100

2020

1997-2009

EM Time Dependence

time-stationary

time-dependent

time-stationary

time-dependent

EM Time Reference (Future/Past)

Not applicable

past time

EM Time Continuity

Not applicable

discrete

Not applicable

continuous

EM Temporal Grain Size Value

Not applicable

user defined

Not applicable

EM Temporal Grain Size Unit

Not applicable

Year

Not applicable

EM Spatial Extent

EM ID

EM-65

EM-71

EM-79

EM-119

EM-193

EM-260

EM-303

EM-392

EM-414

EM-454

EM-496

EM-848

EM-857

EM-863

EM-941

EM-1019

Bounding Type

Physiographic or Ecological

Geopolitical

Physiographic or Ecological

Geopolitical

Geopolitical ?

Physiographic or ecological

Other

Not applicable

Watershed/Catchment/HUC

Geopolitical

Spatial Extent Name

Central French Alps

The EU-25 plus Switzerland and Norway

Bilbao Metropolitan Greenbelt

St.Croix, U.S. Virgin Islands

Uppland province

Tampa Bay region

St. Louis River Estuary

Coastal zone surrounding St. Croix

St. Louis River Estuary (of western Lake Superior)

Not applicable

Tampa Bay estuary watershed

Iran

Asia

Spatial Extent Area (Magnitude)

10-100 km^2

>1,000,000 km^2

100-1000 km^2

10-100 km^2

10,000-100,000 km^2

1000-10,000 km^2.

10-100 km^2

100-1000 km^2

10-100 km^2

Not applicable

1000-10,000 km^2.

>1,000,000 km^2

1000-10,000 km^2.

Spatial Distribution of Computations

EM ID

EM-65

EM-71

EM-79

EM-119

EM-193

EM-260

EM-303

EM-392

EM-414

EM-454

EM-496

EM-848

EM-857

EM-863

EM-941

EM-1019

EM Spatial Distribution

spatially distributed (in at least some cases)

spatially lumped (in all cases)

spatially distributed (in at least some cases)

spatially distributed (in at least some cases) ?

spatially distributed (in at least some cases)

spatially distributed (in at least some cases) ?

spatially lumped (in all cases)

Spatial Grain Type

area, for pixel or radial feature

Not applicable

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

20 m x 20 m

1 km x 1 km

2 m x 2 m

Not applicable

25 m x 25 m

30m x 30m

0.07 m^2 to 0.70 m^2

10 m x 10 m

35 km river estuary reach, 0 to 5 m depth by 1 m increment

stream reach

user defined

10 x 10 m

ha^2

Not applicable

EM Structure and Computation Approach

EM ID

EM-65

EM-71

EM-79

EM-119

EM-193

EM-260

EM-303

EM-392

EM-414

EM-454

EM-496

EM-848

EM-857

EM-863

EM-941

EM-1019

EM Computational Approach

Analytic

Logic- or rule-based

Analytic

Numeric

Analytic

Numeric

EM Determinism

deterministic

Statistical Estimation of EM

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

None

None

Conventional (frequentist) statistics

None

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

None

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID

EM-65

EM-71

EM-79

EM-119

EM-193

EM-260

EM-303

EM-392

EM-414

EM-454

EM-496

EM-848

EM-857

EM-863

EM-941

EM-1019

Model Calibration Reported?

Yes

Not applicable

Yes

Unclear

Model Goodness of Fit Reported?

Yes

Not applicable

Unclear

Goodness of Fit (metric| value | unit)

Adjusted R | 69.9 | Not applicable

R squared | 45.2 | Not applicable

R squared | 35.5 | Not applicable

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

None

r2 | 0.51 | unitless

None

Model Operational Validation Reported?

Yes

Not applicable

Yes

Model Uncertainty Analysis Reported?

Yes

Not applicable ?

Unclear

Model Sensitivity Analysis Reported?

Yes ?

Yes

Not applicable ?

Unclear

Model Sensitivity Analysis Include Interactions?

Not applicable

Yes

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-79

EM-119

EM-193

EM-260

EM-303

EM-392

EM-414

EM-454

EM-496

EM-848

EM-857

EM-863

EM-941

EM-1019

Europe
- France

Europe
- France

Europe
- France

Europe
- Cyprus
- Portugal
- Malta
- Greece
- Austria
- Latvia
- Netherlands
- Sweden
- Ireland
- Luxembourg
- Poland
- Slovakia
- Slovenia
- France
- Lithuania
- Hungary
- Switzerland
- Spain
- Czech Republic
- Belgium
- Norway
- Finland
- Denmark
- Italy
- Germany
- Estonia

Europe
- Spain

None

Europe
- Sweden

North America
- United States
  - Florida

North America
- United States
  - Minnesota
  - Wisconsin

None

North America
- United States
  - Minnesota
  - Wisconsin

North America
- United States

None

North America
- United States
  - Florida

Europe

Asia

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

EM-65

EM-71

EM-79

EM-119

EM-193

EM-260

EM-303

EM-392

EM-414

EM-454

EM-496

EM-848

EM-857

EM-863

EM-941

EM-1019

None

Tropical Atlantic
- West Tropical Atlantic
  - Tropical Southwestern Atlantic

None

Tropical Atlantic
- West Tropical Atlantic
  - Tropical Northwestern Atlantic
    - Eastern Caribbean

None

Tropical Atlantic
- West Tropical Atlantic
  - Tropical Northwestern Atlantic
    - Floridian

None

Western Indo-Pacific

Centroid Lat/Long (Decimal Degree)

EM ID

EM-65

EM-71

EM-79

EM-119

EM-193

EM-260

EM-303

EM-392

EM-414

EM-454

EM-496

EM-848

EM-857

EM-863

EM-941

EM-1019

Centroid Latitude

45.05

50.53

43.25

17.75

59.52

28.05

46.72

17.73

46.74

Not applicable

27.76

32.29

38.63

Centroid Longitude

6.4

7.6

-2.92

-64.75

17.9

-82.52

-96.13

-64.77

-96.13

Not applicable

-82.54

53.68

117.79

Centroid Datum

WGS84

NAD83

WGS84

Not applicable

WGS84

Centroid Coordinates Status

Provided

Estimated

Provided

Estimated

Not applicable

Estimated

Environments and Scales Modeled

EM ID

EM-65

EM-71

EM-79

EM-119

EM-193

EM-260

EM-303

EM-392

EM-414

EM-454

EM-496

EM-848

EM-857

EM-863

EM-941

EM-1019

EM Environmental Sub-Class

Agroecosystems | Grasslands

Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified)

Near Coastal Marine and Estuarine

Agroecosystems | Grasslands

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

Rivers and Streams | Inland Wetlands

Rivers and Streams

Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Ground Water

Inland Wetlands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified)

Terrestrial Environment (sub-classes not fully specified)

Atmosphere

Specific Environment Type

Subalpine terraces, grasslands, and meadows

Subalpine terraces, grasslands, and meadows.

Subalpine terraces, grasslands, and meadows

Not applicable

none

stony coral reef

Spring-sown cereal croplands and surrounding grassland and non-arable land

All terestrial landcover and waterbodies

Freshwater estuarine system

Coral reefs

River and riverine estuary (lake)

benthic habitat

coastal and near coastal wetlands and adjacent environments

Esturary and associated urban and terrestrial environment

terrestrial land types

Asian atmosphere

EM Ecological Scale

Not applicable

Ecological scale is coarser 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 corresponds to 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

Not applicable

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID

EM-65

EM-71

EM-79

EM-119

EM-193

EM-260

EM-303

EM-392

EM-414

EM-454

EM-496

EM-848

EM-857

EM-863

EM-941

EM-1019

EM Organismal Scale

Community

Not applicable

Guild or Assemblage

Individual or population, within a species

Not applicable

Guild or Assemblage

Not applicable

Other (Comment) ?

Not applicable

Taxonomic level and name of organisms or groups identified

EM-65

EM-71

EM-79

EM-119

EM-193

EM-260

EM-303

EM-392

EM-414

EM-454

EM-496

EM-848

EM-857

EM-863

EM-941

EM-1019

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

Genus
- Neuroptera: Chrysopidae
- Araneae: Linyphiidae
- Coleoptera: Carabidae
- Araneae:Lycosidae
- Coleoptera: Staphylinidae
- Coleoptera: Coccinellidae
- Diptera: Syrphidae
Species
- Rhopalosiphum padi L.

None Available

Species
- Apis mellifera
- Apis florea

None Available

EnviroAtlas URL

EM-65

EM-71

EM-79

EM-119

EM-193

EM-260

EM-303

EM-392

EM-414

EM-454

EM-496

EM-848

EM-857

EM-863

EM-941

EM-1019

GAP Ecological Systems

None Available

Percent GAP Status 1 & 2

Percent IUCN Status II, Percent GAP Status 1 & 2

None Available

GAP Ecological Systems

The National Hydrography Dataset (NHD), GAP Ecological Systems, Total Annual Reduced Nitrogen Deposition, Waterbody area, Percent GAP Status 1 & 2, Acres of Land Enrolled in the Conservation Reserve Program (CRP), The Watershed Boundary Dataset (WBD), Carbon Storage by Tree Biomass

Average Annual Precipitation

None Available

Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition

None Available

The National Hydrography Dataset (NHD), National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Water supply from NID reservoirs (million gallons), Carbon Storage by Tree Biomass

National Hydrography Dataset Plus (NHD PlusV2)

The National Hydrography Dataset (NHD), GAP Ecological Systems, Average Annual Precipitation, Waterbody area

None Available

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

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

EM-65

EM-71

EM-79

EM-119

EM-193

EM-260

EM-303

EM-392

EM-414

EM-454

EM-496

EM-848

EM-857

EM-863

EM-941

EM-1019

None

[Ecosystem] Provisioning
- Materials
  - Biomass
    - Fibres and other materials from plants, algae and animals for direct use or processing

[Ecosystem] Cultural
- Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings]
  - Physical and experiential interactions
    - Physical use of land-/seascapes in different environmental settings
    - Experiential use of plants, animals and land-/seascapes in different environmental settings
  - Intellectual and representative interactions
    - Aesthetic

[Non-ecosystem] Regulation & Maintenance by natural physical structures and processes
- Maintenance of physical, chemical, abiotic conditions
  - By natural chemical and physical processes
    - Not applicable

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Pest and disease control
    - Pest control

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Atmospheric composition and climate regulation
    - Global climate regulation by reduction of greenhouse gas concentrations
- Mediation of flows
  - Liquid flows
    - Flood protection
- Mediation of waste, toxics and other nuisances
  - Mediation by ecosystems
    - Filtration/sequestration/storage/accumulation by ecosystems

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Lifecycle maintenance, habitat and gene pool protection
    - Maintaining nursery populations and habitats

[Ecosystem] Cultural
- Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings]
  - Physical and experiential interactions
    - Experiential use of plants, animals and land-/seascapes in different environmental settings

[Ecosystem] Regulation & Maintenance
- Mediation of waste, toxics and other nuisances
  - Mediation by biota
    - Bio-remediation by micro-organisms, algae, plants, and animals

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Lifecycle maintenance, habitat and gene pool protection
    - Maintaining nursery populations and habitats

[Ecosystem] Regulation & Maintenance
- Mediation of flows
  - Liquid flows
    - Flood protection
- Mediation of waste, toxics and other nuisances
  - Mediation by ecosystems
    - Filtration/sequestration/storage/accumulation by ecosystems

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Lifecycle maintenance, habitat and gene pool protection
    - Maintaining nursery populations and habitats

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Lifecycle maintenance, habitat and gene pool protection
    - Pollination and seed dispersal

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Atmospheric composition and climate regulation
    - Global climate regulation by reduction of greenhouse gas concentrations

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

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

EM-65

EM-71

EM-79

EM-119

EM-193

EM-260

EM-303

EM-392

EM-414

EM-454

EM-496

EM-848

EM-857

EM-863

EM-941

EM-1019

Grasslands (23)
- Flora (5)
  - Green biomass
    - Stock Indicator
Agroecosystems (22)
- Flora (5)
  - Green biomass
    - Stock Indicator

None

Rivers and Streams (111)
- Fauna (4)
  - Wildlife
    - Stock Indicator
Barren / Rock and Sand (28)
- Fauna (4)
  - Wildlife
    - Stock Indicator
Forests (21)
- Fauna (4)
  - Wildlife
    - Stock Indicator
Tundra (25)
- Fauna (4)
  - Wildlife
    - Stock Indicator
Scrubland/Shrubland (24)
- Fauna (4)
  - Wildlife
    - Stock Indicator
Grasslands (23)
- Fauna (4)
  - Wildlife
    - Stock Indicator
Wetlands (12)
- Fauna (4)
  - Wildlife
    - Stock Indicator
Lakes and Ponds (112)
- Fauna (4)
  - Wildlife
    - Stock 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

Grasslands (23)
- Fauna (4)
  - Pest predators
    - Stock Indicator
Agroecosystems (22)
- Fauna (4)
  - Pest predators
    - Stock Indicator

Rivers and Streams (111)
- Composite (8)
  - Regulation of extreme events
    - Extreme Event Indicator

None

Forests (21)
- Fauna (4)
  - Wildlife
    - Stock Indicator
Scrubland/Shrubland (24)
- Fauna (4)
  - Wildlife
    - Stock Indicator
Urban/Surburban (27)
- Fauna (4)
  - Wildlife
    - Stock Indicator

Atmosphere
- Atmosphere (1)
  - Air
    - Quality Indicator

US EPA

EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

EM Variables by Category

All EM Descriptors

EM Descriptors by Modeling Concepts

Comparing:

EM Identity and Description

Comment:

Comment:

Comment:

Comment:

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

EM Modeling Approach

Comment:

Comment:

Comment:

Comment:

Comment:

Comment:

Comment:

EM Locations, Environments, Ecology

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

Comment:

Comment:

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

Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

Comment:

Taxonomic level and name of organisms or groups identified

EnviroAtlas URL

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

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

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

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