EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?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.
EM Identity and Description
EM ID
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EM-541 ![]() |
EM-979 |
EM Short Name
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InVEST fisheries, lobster, South Africa | Predicting ecosystem service values, Bangladesh |
EM Full Name
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Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa | Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh |
EM Source or Collection
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InVEST | None |
EM Source Document ID
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349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
457 |
Document Author
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Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby | Morshed, S. R., Fattah, M. A., Haque, M. N., & Morshed, S. Y. |
Document Year
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2018 | 2022 |
Document Title
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Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals | Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh |
Document Status
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Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript |
EM ID
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EM-541 ![]() |
EM-979 |
https://www.naturalcapitalproject.org/invest/ | Not applicable | |
Contact Name
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Michelle Ward | Syed Riad Morshed |
Contact Address
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ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia | Department of Urban and Regional Planning, Khulna University of Engineering and Technology, Khulna, Bangladesh |
Contact Email
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m.ward@uq.edu.au | riad.kuet.urp16@gmail.com |
EM ID
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EM-541 ![]() |
EM-979 |
Summary Description
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AUTHOR'S DESCRIPTION: "Here we develop a method for assessing future scenarios of environmental management change that improve coastal ecosystem services and thereby, support the success of the SDGs. We illustrate application of the method using a case study of South Africa’s West Coast Rock Lobster fishery within the Table Mountain National Park (TMNP) Marine Protected Area...We calculated the retrospective and current value of the West Coast Rock Lobster fishery using published and unpublished data from various sources and combined the market worth of landed lobster from recreational fishers, small-scale fisheries (SSF), large-scale fisheries (LSF) and poachers. Then using the InVEST tool, we combined data to build scenarios that describe possible futures for the West Coast Rock Lobster fishery (see Table 1). The first scenario, entitled ‘Business as Usual’ (BAU), takes the current situation and most up-to-date data to model the future if harvest continues at the existing rate. The second scenario is entitled ‘Redirect the Poachers’ (RP), which attempts to model implementation of strict management, whereby poaching is minimised from the Marine Protected Area and other economic and nutritional sources are made available through government initiatives. The third scenario, entitled ‘Large Scale Cutbacks’ (LSC), excludes large-scale fisheries from harvesting West Coast Rock Lobster within the TMNP Marine Protected Area." | 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. |
Specific Policy or Decision Context Cited
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Future rock lobster fisheries management | N/A |
Biophysical Context
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No additional description provided | Jashore city, Bangladesh |
EM Scenario Drivers
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Fisheries exploitation; fishing vulnerability (of age classes) | No scenarios presented |
EM ID
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EM-541 ![]() |
EM-979 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application |
New or Pre-existing EM?
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Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-541 ![]() |
EM-979 |
Document ID for related EM
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None | None |
EM ID for related EM
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None | None |
EM Modeling Approach
EM ID
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EM-541 ![]() |
EM-979 |
EM Temporal Extent
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1986-2115 | 2000-2050 |
EM Time Dependence
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time-dependent | time-dependent |
EM Time Reference (Future/Past)
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future time | both |
EM Time Continuity
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discrete | discrete |
EM Temporal Grain Size Value
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1 | 10 |
EM Temporal Grain Size Unit
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Year | Year |
EM ID
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EM-541 ![]() |
EM-979 |
Bounding Type
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Geopolitical | Geopolitical |
Spatial Extent Name
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Table Mountain National Park Marine Protected Area | Jashore city, Bangladesh |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | 1000-10,000 km^2. |
EM ID
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EM-541 ![]() |
EM-979 |
EM Spatial Distribution
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spatially lumped (in all cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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Not applicable | map scale, for cartographic feature |
Spatial Grain Size
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Not applicable | 30m |
EM ID
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EM-541 ![]() |
EM-979 |
EM Computational Approach
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Numeric | Analytic |
EM Determinism
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deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-541 ![]() |
EM-979 |
Model Calibration Reported?
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No | Yes |
Model Goodness of Fit Reported?
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No | Yes |
Goodness of Fit (metric| value | unit)
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None |
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Model Operational Validation Reported?
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Yes ?Comment:A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data. |
Yes |
Model Uncertainty Analysis Reported?
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No | Unclear |
Model Sensitivity Analysis Reported?
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No | Unclear |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-541 ![]() |
EM-979 |
None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-541 ![]() |
EM-979 |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-541 ![]() |
EM-979 |
Centroid Latitude
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-34.18 | 23.95 |
Centroid Longitude
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18.35 | 89.12 |
Centroid Datum
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WGS84 | other |
Centroid Coordinates Status
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Provided | Provided |
EM ID
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EM-541 ![]() |
EM-979 |
EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | Urban city |
EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-541 ![]() |
EM-979 |
EM Organismal Scale
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Individual or population, within a species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-541 ![]() |
EM-979 |
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None Available |
EnviroAtlas URL
EM-541 ![]() |
EM-979 |
Big game hunting recreation demand | GAP Ecological Systems |
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-541 ![]() |
EM-979 |
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<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-541 ![]() |
EM-979 |
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