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-24
EM-88
EM-598
EM-979
EM-991

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EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-598	EM-979	EM-991
EM Short Name em.detail.shortNameHelp ?	i-Tree Eco: Carbon storage & sequestration, USA	Area and hotspots of carbon storage, South Africa	DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland	Predicting ecosystem service values, Bangladesh	Atlantis ecosystem harvest submodel
EM Full Name em.detail.fullNameHelp ?	i-Tree Eco carbon storage and sequestration (trees), USA	Area and hotspots of carbon storage, South Africa	DeNitrification-DeComposition simulation of N2O flux Ireland	Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh	Lessons in modelling and management of marine ecosystems: the Atlantis experience
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	i-Tree \| USDA Forest Service	None	None	None	None
EM Source Document ID	195	271	358	457	463
Document Author em.detail.documentAuthorHelp ?	Nowak, D. J., Greenfield, E. J., Hoehn, R. E. and Lapoint, E.	Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S.	Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M.	Morshed, S. R., Fattah, M. A., Haque, M. N., & Morshed, S. Y.	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 ?	2013	2008	2010	2022	2011
Document Title em.detail.sourceIdHelp ?	Carbon storage and sequestration by trees in urban and community areas of the United States	Mapping ecosystem services for planning and management	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	Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh	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
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-598	EM-979	EM-991
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	Not applicable	http://www.dndc.sr.unh.edu	Not applicable	https://research.csiro.au/atlantis/home/links/
Contact Name em.detail.contactNameHelp ?	David J. Nowak	Benis Egoh	M. Abdalla	Syed Riad Morshed	Elizabeth Fulton
Contact Address	USDA Forest Service, Northern Research Station, Syracuse, NY 13210, USA	Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy	Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland	Department of Urban and Regional Planning, Khulna University of Engineering and Technology, Khulna, Bangladesh	Division of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas.
Contact Email	dnowak@fs.fed.us	Not reported	abdallm@tcd.ie	riad.kuet.urp16@gmail.com	beth.fulton@csiro.au

EM Description

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-598	EM-979	EM-991
Summary Description em.detail.summaryDescriptionHelp ?	ABSTRACT: "Carbon storage and sequestration by urban trees in the United States was quantified to assess the magnitude and role of urban forests in relation to climate change. Urban tree field data from 28 cities and 6 states were used to determine the average carbon density per unit of tree cover. These data were applied to statewide urban tree cover measurements to determine total urban forest carbon storage and annual sequestration by state and nationally. Urban whole tree carbon storage densities average 7.69 kg C m^2 of tree cover and sequestration densities average 0.28 kg C m^2 of tree cover per year. Total tree carbon storage in U.S. urban areas (c. 2005) is estimated at 643 million tonnes ($50.5 billion value; 95% CI = 597 million and 690 million tonnes) and annual sequestration is estimated at 25.6 million tonnes ($2.0 billion value; 95% CI = 23.7 million to 27.4 million tonnes)."	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…In this study, only carbon storage was mapped because of a lack of data on the other functions related to the regulation of global climate such as carbon sequestration and the effects of changes in albedo. Carbon is stored above or below the ground and South African studies have found higher levels of carbon storage in thicket than in savanna, grassland and renosterveld (Mills et al., 2005). This information was used by experts to classify vegetation types (Mucina and Rutherford, 2006), according to their carbon storage potential, into three categories: low to none (e.g. desert), medium (e.g. grassland), high (e.g. thicket, forest) (Rouget et al., 2004). All vegetation types with medium and high carbon storage potential were identified as the range of carbon storage. Areas of high carbon storage potential where it is essential to retain this store were mapped as the carbon storage hotspot."	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. 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.	Land Use/Land Cover (LULC) provides provisional, supporting, cultural, and regulating ecosystem services that contribute to ecological environments, enhance human health and living, have economic advantages for sustaining living organisms. LULC transformation due to enormous urban expansion diminishing Ecosystem Services Values (ESVs) and discouraging sustainability. Though unplanned LULC transformation practice became more prevalent in developing countries, comprehensive assessment of LULC changes and their influences in ESVs are rarely attempted. This study aimed to illustrate and forecast the LULC changes and their influences on ESVs change in Jashore using remote sensing technologies. ESVs estimation and change analysis were conducted by utilizing -derived LULC data of the year 2000, 2010, and 2020 with the corresponding global value coefficients of each LULC type which are previously published. For simulating future LULC and ESVs, Land Change Modeler of TerrSet Geospatial Monitoring and Modeling Software was used in Multi-Layer Perceptron-Markov Chain and Artificial Neural Network method. The decline of agricultural land by 13.13% and waterbody by 5.79% has resulted in the reduction of total ESVs US$0.23 million (24.47%) during 2000–2020. The forecasted result shows that the built-up area will be dominant LULC in the future, and ESVs of provisioning and cultural services will be diminished by $0.107 million, $63400.3 by 2050 with the declination of agricultural, waterbody, vegetation, and vacant land covers. The study signifies the importance of a strategic rational land-use plan to strictly monitor and control the encroachment of built-up areas into vegetation, waterbodies, and agricultural land in addition to scientific mitigative policies for ensuring ecological sustainability.	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 ?	Not reported	None identified	climate change	N/A	None identified
Biophysical Context	Urban areas 3.0% of land in U.S. and Urban/community land (5.3%) in 2000.	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.	Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C	Jashore city, Bangladesh	NA
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	fertilization	No scenarios presented	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-598	EM-979	EM-991
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method + Application	Method Only
New or Pre-existing EM? em.detail.newOrExistHelp ?	Application of existing 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-24	EM-88	EM-598	EM-979	EM-991
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	None	Doc-271	None	None	Doc-456 \| Doc-459 \| Doc-461 \| Doc-463
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-85 \| EM-86 \| EM-87	EM-593	None	EM-978 \| EM-981 \| EM-983 \| EM-985 \| EM-990

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-598	EM-979	EM-991
EM Temporal Extent em.detail.tempExtentHelp ?	1989-2010	Not reported	1961-1990	2000-2050	Not applicable
EM Time Dependence em.detail.timeDependencyHelp ?	time-dependent	time-stationary	time-dependent	time-dependent	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	future time	Not applicable	both	both	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	discrete	Not applicable	discrete	discrete	continuous
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	1	Not applicable	1	10	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Year	Not applicable	Day	Year	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-598	EM-979	EM-991
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Geopolitical	Point or points	Geopolitical	Not applicable
Spatial Extent Name em.detail.extentNameHelp ?	United States	South Africa	Oak Park Research centre	Jashore city, Bangladesh	Not applicable
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	>1,000,000 km^2	1-10 ha	1000-10,000 km^2.	Not applicable

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-598	EM-979	EM-991
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially distributed (in at least some cases)	Not applicable
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	map scale, for cartographic feature	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	1 m^2	Distributed across catchments with average size of 65,000 ha	Not applicable	30m	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-598	EM-979	EM-991
EM Computational Approach em.detail.emComputationalApproachHelp ?	Numeric	Analytic	Numeric	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Machine learning	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-598	EM-979	EM-991
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	Yes	Yes	Not applicable
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	Yes ? Comment: Actual value was not given, just that results were very poor. Simulation results were 258% of observed	Yes	Not applicable
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	r squared \| 00.0 \| unitless	Accuracy assessment value \| 93.33-96.67 \| % Kappa statistic \| 0.9143-0.9577 \| N/A	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	Yes	Yes	Not applicable
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	Yes ? Comment: An error of sampling was reported, but not an error of estimation Estimation error was unknown and reported as likely larger than the error of sampling.	No	No	Unclear	Not applicable
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	Unclear	Not applicable
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	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-24	EM-88	EM-598	EM-979	EM-991
North America United States ? Comment: EM presents carbon storage and sequestration rates for country and by individual state	Africa South Africa	Europe Ireland	Asia India	None

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

EM-24	EM-88	EM-598	EM-979	EM-991
None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-598	EM-979	EM-991
Centroid Latitude em.detail.ddLatHelp ?	40.16	-30	52.86	23.95	Not applicable
Centroid Longitude em.detail.ddLongHelp ?	-99.79	25	6.54	89.12	Not applicable
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	None provided	other	Not applicable
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Provided	Provided	Not applicable

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-24	EM-88	EM-598	EM-979	EM-991
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Forests \| Created Greenspace	Terrestrial Environment (sub-classes not fully specified)	Agroecosystems	Terrestrial Environment (sub-classes not fully specified)	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds \| Near Coastal Marine and Estuarine \| Open Ocean and Seas
Specific Environment Type em.detail.specificEnvTypeHelp ?	Urban forests	Not applicable	farm pasture	Urban city	Multiple
EM Ecological Scale em.detail.ecoScaleHelp ?	Zone within an ecosystem	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-24	EM-88	EM-598	EM-979	EM-991
EM Organismal Scale em.detail.orgScaleHelp ?	Species ? Comment: Trees were identified to species for the differential growth and biomass estimates part of the analysis.	Not applicable	Not applicable	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-24	EM-88	EM-598	EM-979	EM-991
None Available	None Available	None Available	None Available	None Available

EnviroAtlas URL

EM-24	EM-88	EM-598	EM-979	EM-991
GAP Ecological Systems, Average Annual Precipitation	None Available	GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day)	GAP Ecological Systems	Big game hunting recreation demand

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-24	EM-88	EM-598	EM-979	EM-991
[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 Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	[Ecosystem] Provisioning Nutrition Biomass Cultivated crops Energy Biomass-based energy sources Plant-based resources Materials Biomass Fibres and other materials from plants, algae and animals for direct use or processing [Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Mediation of flows Liquid flows Hydrological cycle and water flow maintenance Mass flows Mass stabilisation and control of erosion rates Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems [Ecosystem] Cultural Spiritual, symbolic and other interactions with biota, ecosystems, and land-/seascapes [environmental settings] Spiritual and/or emblematic Sacred and/or religious 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] 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-24	EM-88	EM-598	EM-979	EM-991
Forests (21) Flora (5) Trees Stock Indicator Atmosphere Composite (8) Regulation of extreme events Flow Indicator Urban/Suburban (27) Flora (5) Trees Flow Indicator Composite (8) Regulation of extreme events Quality Indicator	None	Agroecosystems (22) Flora (5) Green biomass Stock Indicator	Scrubland/Shrubland (24) Composite (8) Regulation of extreme events Site Indicator Urban/Surburban (27) Composite (8) Presence of environmental class/subclass Site 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

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

EM Modeling Approach

Comment:

Comment:

EM Locations, Environments, Ecology

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

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)