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-106 | EM-368 |
EM-541 ![]() |
EM Short Name
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Value of Habitat for Shrimp, Campeche, Mexico | InVEST - Water Yield (v3.0) | InVEST fisheries, lobster, South Africa |
EM Full Name
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Value of Habitat for Shrimp, Campeche, Mexico | InVEST v3.0 Reservoir Hydropower Projection, aka Water Yield | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa |
EM Source or Collection
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None | InVEST | InVEST |
EM Source Document ID
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227 | 311 |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
Document Author
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Barbier, E. B., and Strand, I. | Natural Capital Project | Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby |
Document Year
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1998 | 2015 | 2018 |
Document Title
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Valuing mangrove-fishery linkages: A case study of Campeche, Mexico | Water Yield: Reservoir Hydropower Production- InVEST (v3.0) | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Web published | Published journal manuscript |
EM ID
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EM-106 | EM-368 |
EM-541 ![]() |
Not applicable | https://www.naturalcapitalproject.org/invest/ | https://www.naturalcapitalproject.org/invest/ | |
Contact Name
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E.B. Barbier | Natural Capital Project | Michelle Ward |
Contact Address
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Environment Department, University of York, York YO1 5DD, UK | 371 Serra Mall, Stanford University, Stanford, Ca 94305 | ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia |
Contact Email
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Not reported | invest@naturalcapitalproject.org | m.ward@uq.edu.au |
EM ID
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EM-106 | EM-368 |
EM-541 ![]() |
Summary Description
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AUTHOR'S DESCRIPTION: "We assume throughout that shrimp harvesting occurs through open access management that yields production which is exported internationally, and we modify a standard open access fishery model to account explicitly for the effect of the mangrove area on carrying capacity and thus production.We derive the conditions determining the long-run equilibrium of the model, including the comparative static effects of a change in mangrove area, on this equilibrium. Through regressing a relationship between shrimp harvest, effort and mangrove area over time, we estimate parameters based on the combinations of the bioeconomic parameters of the model determining the comparative statics. By incorporating additional economic data, we are able to simulate an estimate of the effect of changes in mangrove area in Laguna de Terminos on the production and value of shrimp harvests in Campeche state." (153) | Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. AUTHOR'S DESCRIPTION: "The InVEST Reservoir Hydropower model estimates the relative contributions of water from different parts of a landscape, offering insight into how changes in land use patterns affect annual surface water yield and hydropower production. Modeling the connections between landscape changes and hydrologic processes is not simple. Sophisticated models of these connections and associated processes (such as the WEAP model) are resource and data intensive and require substantial expertise. To accommodate more contexts, for which data are readily available, InVEST maps and models the annual average water yield from a landscape used for hydropower production, rather than directly addressing the affect of LULC changes on hydropower failure as this process is closely linked to variation in water inflow on a daily to monthly timescale. Instead, InVEST calculates the relative contribution of each land parcel to annual average hydropower production and the value of this contribution in terms of energy production. The net present value of hydropower production over the life of the reservoir also can be calculated by summing discounted annual revenues. The model runs on a gridded map. It estimates the quantity and value of water used for hydropower production from each subwatershed in the area of interest. It has three components, which run sequentially. First, it determines the amount of water running off each pixel as the precipitation less the fraction of the water that undergoes evapotranspiration. The model does not differentiate between surface, subsurface and baseflow, but assumes that all water yield from a pixel reaches the point of interest via one of these pathways. This model then sums and averages water yield to the subwatershed level. The pixel-scale calculations allow us to represent the heterogeneity of key driving factors in water yield such as soil type, precipitation, vegetation type, etc. However, the theory we are using as the foundation of this set of models was developed at the subwatershed to watershed scale. We are only confident in the interpretation of these models at the subwatershed scale, so all outputs are summed and/or averaged to the subwatershed scale. We do continue to provide pixel-scale representations of some outputs for calibration and model-checking purposes only. These pixel-scale maps are not to be interpreted for understanding of hydrological processes or to inform decision making of any kind. | 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." |
Specific Policy or Decision Context Cited
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None identified | None identified | Future rock lobster fisheries management |
Biophysical Context
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Gulf of Mexico; mangrove-lagoon system | None applicable | No additional description provided |
EM Scenario Drivers
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No scenarios presented | N/A | Fisheries exploitation; fishing vulnerability (of age classes) |
EM ID
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EM-106 | EM-368 |
EM-541 ![]() |
Method Only, Application of Method or Model Run
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Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs |
New or Pre-existing EM?
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New or revised 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
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EM-106 | EM-368 |
EM-541 ![]() |
Document ID for related EM
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None | Doc-307 | Doc-280 | Doc-338 | Doc-205 | None |
EM ID for related EM
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EM-185 | EM-319 | EM-437 | EM-148 | EM-344 | EM-111 | None |
EM Modeling Approach
EM ID
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EM-106 | EM-368 |
EM-541 ![]() |
EM Temporal Extent
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1980-1990 | Not applicable | 1986-2115 |
EM Time Dependence
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time-stationary | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | future time | future time |
EM Time Continuity
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Not applicable | discrete | discrete |
EM Temporal Grain Size Value
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Not applicable | 1 | 1 |
EM Temporal Grain Size Unit
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Year | Year | Year |
EM ID
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EM-106 | EM-368 |
EM-541 ![]() |
Bounding Type
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Physiographic or Ecological | Not applicable | Geopolitical |
Spatial Extent Name
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Laguna de Terminos Mangrove system | Not applicable | Table Mountain National Park Marine Protected Area |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | Not applicable | 100-1000 km^2 |
EM ID
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EM-106 | EM-368 |
EM-541 ![]() |
EM Spatial Distribution
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spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:pixel is likely 30m x 30m |
spatially lumped (in all cases) |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | Not applicable |
Spatial Grain Size
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1 km x 1 km | Not specified | Not applicable |
EM ID
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EM-106 | EM-368 |
EM-541 ![]() |
EM Computational Approach
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Analytic | Numeric | Numeric |
EM Determinism
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deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-106 | EM-368 |
EM-541 ![]() |
Model Calibration Reported?
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Yes |
Yes ?Comment:Annual Yield can be calibrated with actual yield based up 10 year average input data though this was an "optional" part of the model. Calibrate with total precipitation and potential evapotranspiration. Before the calibration process is commenced, the modelers suggest performing a sensitivity analysis with the observed runoff data to define the parameters that influence model outputs the most. The calibration can then focus on highly sensitive parameters followed by less sensitive ones. |
No |
Model Goodness of Fit Reported?
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Yes | Not applicable | No |
Goodness of Fit (metric| value | unit)
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None | None |
Model Operational Validation Reported?
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No | No |
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. |
Model Uncertainty Analysis Reported?
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Yes | No | No |
Model Sensitivity Analysis Reported?
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Yes | Not applicable | No |
Model Sensitivity Analysis Include Interactions?
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Unclear | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-106 | EM-368 |
EM-541 ![]() |
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None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-106 | EM-368 |
EM-541 ![]() |
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None |
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Centroid Lat/Long (Decimal Degree)
EM ID
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EM-106 | EM-368 |
EM-541 ![]() |
Centroid Latitude
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18.61 | -9999 | -34.18 |
Centroid Longitude
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-91.55 | -9999 | 18.35 |
Centroid Datum
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WGS84 | Not applicable | WGS84 |
Centroid Coordinates Status
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Estimated | Not applicable | Provided |
EM ID
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EM-106 | EM-368 |
EM-541 ![]() |
EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Rivers and Streams | Near Coastal Marine and Estuarine |
Specific Environment Type
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Mangrove | Watershed | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf |
EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-106 | EM-368 |
EM-541 ![]() |
EM Organismal Scale
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Guild or Assemblage | Not applicable | Individual or population, within a species |
Taxonomic level and name of organisms or groups identified
EM-106 | EM-368 |
EM-541 ![]() |
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None Available |
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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)
EM-106 | EM-368 |
EM-541 ![]() |
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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-106 | EM-368 |
EM-541 ![]() |
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