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-68
EM-86
EM-127
EM-154
EM-438
EM-838
EM-985

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-127	EM-154	EM-438	EM-838	EM-985
EM Short Name em.detail.shortNameHelp ?	Fodder crude protein content, Central French Alps	Area and hotspots of soil retention, South Africa	Annual profit - carbon plantings, South Australia	Mangrove development, Tampa Bay, FL, USA	InVESTv3.0 Nutrient retention, Guánica Bay	Eastern meadowlark abundance, Piedmont region, USA	Atlantis ecosystem assessment submodel
EM Full Name em.detail.fullNameHelp ?	Fodder crude protein content, Central French Alps	Area and hotspots of soil retention, South Africa	Annual profit from carbon plantings, South Australia	Mangrove wetland development, Tampa Bay, FL, USA	InVEST (Integrated Valuation of Environmental Services and Tradeoffs)v3.0 Nutrient retention, Guánica Bay, Puerto Rico, USA	Eastern meadowlark abundance, Piedmont ecoregion, USA	Lessons in modelling and management of marine ecosystems: the Atlantis experience
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	None	None	US EPA	US EPA \| InVEST	None	None
EM Source Document ID	260	271	243	97	338	405	463
Document Author em.detail.documentAuthorHelp ?	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S.	Crossman, N. D., Bryan, B. A., and Summers, D. M.	Osland, M. J., Spivak, A. C., Nestlerode, J. A., Lessmann, J. M., Almario, A. E., Heitmuller, P. T., Russell, M. J., Krauss, K. W., Alvarez, F., Dantin, D. D., Harvey, J. E., From, A. S., Cormier, N. and Stagg, C.L.	Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher	Riffel, S., Scognamillo, D., and L. W. Burger	Fulton, E.A., Link, J.S., Kaplan, I.C., Savina‐Rolland, M., Johnson, P., Ainsworth, C., Horne, P., Gorton, R., Gamble, R.J., Smith, A.D. and Smith, D.C.
Document Year em.detail.documentYearHelp ?	2011	2008	2011	2012	2017	2008	2011
Document Title em.detail.sourceIdHelp ?	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Mapping ecosystem services for planning and management	Carbon payments and low-cost conservation	Ecosystem development after mangrove wetland creation: plant–soil change across a 20-year chronosequence	Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico	Effects of the Conservation Reserve Program on northern bobwhite and grassland birds	Lessons in modelling and management of marine ecosystems: the Atlantis experience
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
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

Software and Access

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-127	EM-154	EM-438	EM-838	EM-985
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	http://www.naturalcapitalproject.org/invest/	Not applicable	https://noaa-fisheries-integrated-toolbox.github.io/Atlantis
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Benis Egoh	Neville D. Crossman	Michael Osland	Susan H. Yee	Sam Riffell	Elizabeth Fulton
Contact Address	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy	CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia	U.S. Environmental Protection Agency, Gulf Ecology Division, gulf Breeze, FL 32561	U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA	CSIRO Wealth from Oceans Flagship, Division of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas. 7001, Australia
Contact Email	sandra.lavorel@ujf-grenoble.fr	Not reported	neville.crossman@csiro.au	mosland@usgs.gov	yee.susan@epa.gov	sriffell@cfr.msstate.edu	beth.fulton@csiro.au

EM Description

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-127	EM-154	EM-438	EM-838	EM-985
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., fodder crude protein content), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in fodder crude protein content 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…Fodder crude protein for each pixel was calculated and mapped using model estimates...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 fodder protein content. 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."	AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil retention was modelled as a function of vegetation or litter cover and soil erosion potential. Schoeman et al. (2002) modelled soil erosion potential and derived eight erosion classes, ranging from low to severe erosion potential for South Africa. The vegetation cover was mapped by ranking vegetation types using expert knowledge of their ability to curb erosion. We used Schulze (2004) index of litter cover which estimates the soil surface covered by litter based on observations in a range of grasslands, woodlands and natural forests. According to Quinton et al. (1997) and Fowler and Rockstrom (2001) soil erosion is slightly reduced with about 30%, significantly reduced with about 70% vegetation cover. The range of soil retention was mapped by selecting all areas that had vegetation or litter cover of more than 30% for both the expert classified vegetation types and litter accumulation index within areas with moderate to severe erosion potential. The hotspot was mapped as areas with severe erosion potential and vegetation/litter cover of at least 70% where maintaining the cover is essential to prevent erosion. An assumption was made that the potential for this service is relatively low in areas with little natural vegetation or litter cover."	ABSTRACT: "A price on carbon is expected to generate demand for carbon offset schemes. This demand could drive investment in tree-based monocultures that provide higher carbon yields than diverse plantings of native tree and shrub species, which sequester less carbon but provide greater variation in vegetation structure and composition. Economic instruments such as species conservation banking, the creation and trading of credits that represent biological-diversity values on private land, could close the financial gap between monocultures and more diverse plantings by providing payments to individuals who plant diverse species in locations that contribute to conservation and restoration goals. We studied a highly modified agricultural system in southern Australia that is typical of many temperate agriculture zones globally (i.e., has a high proportion of endangered species, high levels of habitat fragmentation, and presence of non-native species). We quantified the economic returns...from carbon plantings (monoculture and mixed tree and shrubs) under six carbon-price scenarios." AUTHOR'S DESCRIPTION: "The economic returns of carbon plantings are highly variable and depend primarily on carbon yield and price and opportunity costs (Newell & Stavins 2000; Richards & Stokes 2004; Torres et al. 2010)...The spatial variation in carbon yield and costs, including establishment, maintenance, transaction, and opportunity costs, means that the net economic returns of carbon plantings are also likely to vary spatially."	ABSTRACT: "Mangrove wetland restoration and creation effortsare increasingly proposed as mechanisms to compensate for mangrove wetland losses. However, ecosystem development and functional equivalence in restored and created mangrove wetlands are poorly understood. We compared a 20-year chronosequence of created tidal wetland sites in Tampa Bay, Florida (USA) to natural reference mangrove wetlands. Across the chronosequence, our sites represent the succession from salt marsh to mangrove forest communities. Our results identify important soil and plant structural differences between the created and natural reference wetland sites; however, they also depict a positive developmental trajectory for the created wetland sites that reflects tightly coupled plant-soil development. Because upland soils and/or dredge spoils were used to create the new mangrove habitats, the soils at younger created sites and at lower depths (10–30 cm) had higher bulk densities, higher sand content, lower soil organic matter (SOM), lower total carbon (TC), and lower total nitrogen (TN) than did natural reference wetland soils. However, in the upper soil layer (0–10 cm), SOM, TC, and TN increased with created wetland site age simultaneously with mangrove forest growth. The rate of created wetland soil C accumulation was comparable to literature values for natural mangrove wetlands. Notably, the time to equivalence for the upper soil layer of created mangrove wetlands appears to be faster than for many other wetland ecosystem types. Collectively, our findings characterize the rate and trajectory of above- and below-ground changes associated with ecosystem development in created mangrove wetlands; this is valuable information for environmental managers planning to sustain existing mangrove wetlands or mitigate for mangrove wetland losses."	Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "Nutrient retention was estimated by first calculating water yield and establishing the quantity of nitrogen or phosphorus retained by different land cover classes using a water purification model (InVEST 3.0.0; Tallis et al., 2013). Different land cover classes were assumed to have different capacities for retaining nutrients, depending on the efficiency of vegetation in removing either nitrogen or phosphorus and the rates of nitrogen or phosphorus loading." “Use of other models in conjunction with this model:Average runoff per pixel modeled here were derived from the InVEST Water Yield model"	ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. "	Models are key tools for integrating a wide range of system information in a common framework. Attempts to model exploited marine ecosystems can increase understanding of system dynamics; identify major processes, drivers and responses; highlight major gaps in knowledge; and provide a mechanism to ‘road test’ management strategies before implementing them in reality. The Atlantis modelling framework has been used in these roles for a decade and is regularly being modified and applied to new questions (e.g. it is being coupled to climate, biophysical and economic models to help consider climate change impacts, monitoring schemes and multiple use management). This study describes some common lessons learned from its implementation, particularly in regard to when these tools are most effective and the likely form of best practices for ecosystem-based management (EBM). Most importantly, it highlighted that no single management lever is sufficient to address the many trade-offs associated with EBM and that the mix of measures needed to successfully implement EBM will differ between systems and will change through time. Although it is doubtful that any single management action will be based solely on Atlantis, this modelling approach continues to provide important insights for managers when making natural resource management decisions.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	None identified	Not applicable	Improving water quality	None reported	None identified
Biophysical Context	Elevation ranges from 1552 to 2442 m, on predominantely south-facing slopes	Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential.	Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm.	mangrove forest,Salt marsh, estuary, sea level,	No additional description provided	Conservation Reserve Program lands left to go fallow	N/A
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	Carbon prices at $10/t CO2^-e, $15/t CO2^-e, $20/t CO2^-e, $25/t CO2^-e, $30/t CO2^-e, and $40/t CO2^-e	Not applicable	No scenarios presented	N/A	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-127	EM-154	EM-438	EM-838	EM-985
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs ? Comment: Runs are differentiated based on the the expected annual profit from two types of carbon plantings: 1) Tree-based monocultures (i.e., monoculture carbon planting) and 2) Diverse plantings of native tree and shrub species (i.e., ecological carbon planting)	Method + Application	Method + Application	Method + Application	Method Only
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	New or revised model	New or revised model	New or revised model	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-68	EM-86	EM-127	EM-154	EM-438	EM-838	EM-985
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260 \| Doc-269	Doc-271 ? Comment: Document 273 used for source information on soil erosion potential variable	Doc-245 \| Doc-246 \| Doc-247 \| Doc-243	None	Doc-309 \| Doc-205	Doc-405	Doc-456 \| Doc-459 \| Doc-461
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-65 \| EM-66 \| EM-69 \| EM-70 \| EM-71 \| EM-79 \| EM-80 \| EM-81 \| EM-82 \| EM-83	EM-85 \| EM-87 \| EM-88	EM-128 \| EM-141	None	EM-363 \| EM-112	EM-831 \| EM-841 \| EM-842 \| EM-843 \| EM-844 \| EM-845 \| EM-846 \| EM-847	EM-978 \| EM-981 \| EM-983 \| EM-990 \| EM-991

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-127	EM-154	EM-438	EM-838	EM-985
EM Temporal Extent em.detail.tempExtentHelp ?	2007-2009	Not reported	2009-2050	1990-2010	1980 - 2013	2008	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-dependent	time-dependent	time-dependent	time-stationary	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	future time	future time	other or unclear (comment)	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	discrete	continuous	discrete	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	1	Not applicable	1	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Year	Not applicable	Year	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-127	EM-154	EM-438	EM-838	EM-985
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Geopolitical	Physiographic or Ecological	Physiographic or Ecological	Watershed/Catchment/HUC	Physiographic or ecological	Not applicable
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	South Africa	Agricultural districts of the state of South Australia	Tampa Bay	Guanica Bay Study Area	Piedmont Ecoregion	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	>1,000,000 km^2	100,000-1,000,000 km^2	100-1000 km^2	1000-10,000 km^2.	100,000-1,000,000 km^2	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-127	EM-154	EM-438	EM-838	EM-985
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 distributed (in at least some cases)	spatially lumped (in all cases)	Not applicable
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	Not applicable	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	Distributed across catchments with average size of 65,000 ha	1 ha x 1 ha	m^2	30 m x 30 m	Not applicable	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-127	EM-154	EM-438	EM-838	EM-985
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	Numeric	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	None	None	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-127	EM-154	EM-438	EM-838	EM-985
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	No	No	No	Yes	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	No	No	No	No	No	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	Adjusted R \| 62.4 \|	None	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	No	No	No	No	No	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	Yes	No	No	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	Yes	No	Yes	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	No	Not applicable	Unclear	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

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

EM-68	EM-86	EM-127	EM-154	EM-438	EM-838	EM-985
Europe France	Africa South Africa	Oceania Australia	North America United States Florida	North America U.S. Caribbean Territories	North America United States North Carolina Georgia Alabama Maryland New Jersey South Carolina Pennsylvania	None

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

EM-68	EM-86	EM-127	EM-154	EM-438	EM-838	EM-985
None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Floridian ? Comment: Realm: Tropical Atlantic Region: West Tropical Atlantic Province: Tropical Northwestern Atlantic Ecoregion: Floridian	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-127	EM-154	EM-438	EM-838	EM-985
Centroid Latitude em.detail.ddLatHelp ?	45.05	-30	-34.9	27.8	17.97	36.23	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	6.4	25	138.7	-82.4	-66.93	-81.9	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	WGS84	WGS84	WGS84	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Estimated	Estimated	Estimated	Estimated	Estimated	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-127	EM-154	EM-438	EM-838	EM-985
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Terrestrial Environment (sub-classes not fully specified)	Agroecosystems	Near Coastal Marine and Estuarine	Aquatic Environment (sub-classes not fully specified) \| Inland Wetlands \| Near Coastal Marine and Estuarine \| Open Ocean and Seas \| Forests \| Agroecosystems \| Created Greenspace \| Scrubland/Shrubland \| Barren	Grasslands	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds \| Near Coastal Marine and Estuarine \| Open Ocean and Seas
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows	Not reported	Agricultural land for annual crops, annual legumes, and grazing of sheep and cows	Created Mangrove wetlands	13 LULC were used	grasslands	Multiple
EM Ecological Scale em.detail.ecoScaleHelp ?	Not applicable	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-68	EM-86	EM-127	EM-154	EM-438	EM-838	EM-985
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Not applicable	Guild or Assemblage	Not applicable	Not applicable	Species	Not applicable

Taxonomic level and name of organisms or groups identified

EM-68	EM-86	EM-127	EM-154	EM-438	EM-838	EM-985
None Available	None Available	Species Acacia implexa Eucalyptus microcarpa Acacia verniciflua Eucalyptus polyanthemos Eucalyptus globulus Eucalyptus sideroxylon Acacia mearnsii Acacia pycnantha Eucalyptus camaldulensis Eucalyptus kochii Acacia dealbata	Species Laguncularia racemosa Avicennia germinans Rhizophora mangle Spartina alterniflora	None Available	Species Eastern meadowlark	None Available

EnviroAtlas URL

EM-68	EM-86	EM-127	EM-154	EM-438	EM-838	EM-985
GAP Ecological Systems, Carbon storage by tree biomass (kg/m2)	None Available	Carbon Storage by Tree Biomass	None Available	Total Annual Reduced Nitrogen Deposition, The Watershed Boundary Dataset (WBD)	GAP Ecological Systems, U.S. EPA (Omernik) ecoregions	Big game hunting recreation demand, Percent GAP Status 1 & 2, Total Employment

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-68	EM-86	EM-127	EM-154	EM-438	EM-838	EM-985
[Ecosystem] Provisioning Materials Biomass Materials from plants, algae and animals for agricultural use	[Ecosystem] Regulation & Maintenance Mediation of flows Mass flows Mass stabilisation and control of erosion rates	[Ecosystem] Regulation & Maintenance 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 Soil formation and composition Decomposition and fixing processes Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal	[Ecosystem] Regulation & Maintenance Mediation of flows Mass flows Buffering and attenuation of mass flows 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] Provisioning Nutrition Biomass Wild animals and their outputs [Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats

<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-68	EM-86	EM-127	EM-154	EM-438	EM-838	EM-985
Agroecosystems (22) Flora (5) Fodder quality Quality Indicator	None	None	Near Coastal Marine/Estuarine (113) Flora (5) Trees Stock Indicator Seaweed Stock Indicator Composite (8) Regulation of extreme events Quality Indicator	None	Grasslands (23) Fauna (4) Birds Stock Indicator	None

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:

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

Comment:

EM Modeling Approach

EM Locations, Environments, Ecology

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

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

Comment:

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)