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-327 | EM-437 | EM-856 |
EM Short Name
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ARIES sediment regulation, Puget Sound Region, USA | InVESTv3.0 Water yield, Guánica Bay, Puerto Rico | ARIES: Crop pollination in Santa Fe, NM, USA |
EM Full Name
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ARIES (Artificial Intelligence for Ecosystem Services) Sediment Regulation for Reservoirs, Puget Sound Region, Washington, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs) v3.0 Water yield, Guánica Bay, Puerto Rico, USA | Artificial intelligence for Ecosystem Services (ARIES); Crop pollination, Santa Fe, New Mexico, USA |
EM Source or Collection
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ARIES | US EPA | InVEST | ARIES |
EM Source Document ID
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302 | 338 | 411 |
Document Author
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Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. |
Document Year
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2014 | 2017 | 2018 |
Document Title
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From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Towards globally customizable ecosystem service models |
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 | Published journal manuscript | Published journal manuscript |
EM ID
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EM-327 | EM-437 | EM-856 |
http://aries.integratedmodelling.org/ | http://www.naturalcapitalproject.org/invest/ | https://github.com/integratedmodelling/im.aries.global | |
Contact Name
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Ken Bagstad | Susan H. Yee | Javier Martinez |
Contact Address
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Geosciences and Environmental Change Science Center, US Geological Survey | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | BC3-Basque Centre for Climate Chan ge, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain |
Contact Email
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kjbagstad@usgs.gov | yee.susan@epa.gov | javier.martinez@bc3research.org |
EM ID
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EM-327 | EM-437 | EM-856 |
Summary Description
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ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "We mapped sediment regulation as the location of sediment sinks (depositional areas in floodplains), which can absorb sediment transported by hydrologic flows from upstream sources (erosionprone areas) prior to reaching users. In this case the benefit of avoided sedimentation is provided to 29 major reservoirs. Avoided sedimentation helps maintain the ability of reservoirs to provide benefits including hydroelectric power generation, flood control, recreation, and water supply to beneficiaries through the region. Avoided reservoir sedimentation likely helps to protect each of these benefits in different ways, i.e., increased turbidity or the loss of reservoir storage capacity may have a greater impact on some provision of some benefit types than others. For our purposes we ended the modeling and mapping exercise at the reservoirs. Reservoir sedimentation reduces their storage capacity, typically decreasing their ability to provide these benefits without costly dredging. We thus used a probabilistic Bayesian model of soil erosion incorporating vegetation, soils, and rainfall influences and calibrated using regional data from coarser scale and/or RUSLE derived erosion models (Bagstad et al. 2011). We probabilistically modeled sediment deposition in floodplains using data for floodplain vegetation, floodplain width, and stream gradient, which can influence rates of deposition. We calculated the ratio of actual to theoretical sediment regulation using the aggregated sink values upstream of reservoirs in the Puget Sound region, divided by aggregated theoretical sink values for the entire landscape." | 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: "Stakeholders identified an objective of meeting water demands for agriculture and domestic purposes, including irrigation, drinking water, or hydropower production…Geomorphology, climate, and vegetation determine the amount of water runoff from the landscape that could be available for consumptive uses. Long-term average water yield was estimated for each HUC12 sub-watershed as the difference between total precipitation and the amount absorbed by the different land cover classes using a reservoir hydropower production model (InVEST 3.0.0; Tallis et al., 2013)." | [Abstract:Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The Artificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multicriteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed.] |
Specific Policy or Decision Context Cited
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None identified | Meeting water demands | None identified |
Biophysical Context
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No additional description provided | No additional description provided | Fire watersheds near Albuquerque, NM. |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | N/A |
EM ID
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EM-327 | EM-437 | EM-856 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application |
New or Pre-existing EM?
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New or revised model | Application of existing 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-327 | EM-437 | EM-856 |
Document ID for related EM
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Doc-303 | Doc-305 | Doc-311 | Doc-307 | Doc-280 | Doc-205 |
Doc-411 ?Comment:Supplemental Information to this article can be found online at https://doi.org/10.1016/j.scitotenv.2018.09.371. |
EM ID for related EM
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None | EM-368 | EM-148 | EM-344 | EM-111 | EM-859 |
EM Modeling Approach
EM ID
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EM-327 | EM-437 | EM-856 |
EM Temporal Extent
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1971-2005 | 2006 - 2012 | 2010 |
EM Time Dependence
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time-stationary | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable |
EM ID
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EM-327 | EM-437 | EM-856 |
Bounding Type
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Physiographic or ecological | Watershed/Catchment/HUC | Geopolitical |
Spatial Extent Name
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Puget Sound Region | Guanica Bay watershed | Rwanda and Burndi |
Spatial Extent Area (Magnitude)
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10,000-100,000 km^2 | 1000-10,000 km^2. | 10,000-100,000 km^2 |
EM ID
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EM-327 | EM-437 | EM-856 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature |
Spatial Grain Size
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200m x 200m | 30 m x 30 m | 1km |
EM ID
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EM-327 | EM-437 | EM-856 |
EM Computational Approach
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Analytic | Numeric | Analytic |
EM Determinism
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deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-327 | EM-437 | EM-856 |
Model Calibration Reported?
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Yes | No | Unclear |
Model Goodness of Fit Reported?
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No | No | No |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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No | No | No |
Model Uncertainty Analysis Reported?
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No | No | No |
Model Sensitivity Analysis Reported?
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No | No | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-327 | EM-437 | EM-856 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-327 | EM-437 | EM-856 |
None |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-327 | EM-437 | EM-856 |
Centroid Latitude
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48 | 17.96 | -2.59 |
Centroid Longitude
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-123 | -67.02 | 29.97 |
Centroid Datum
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WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated | Estimated |
EM ID
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EM-327 | EM-437 | EM-856 |
EM Environmental Sub-Class
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Rivers and Streams | Lakes and Ponds | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Near Coastal Marine and Estuarine | Open Ocean and Seas | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Terrestrial environment surrounding a large estuary | 13 LULC were used | varied |
EM Ecological Scale
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Ecological scale corresponds to 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 |
Scale of differentiation of organisms modeled
EM ID
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EM-327 | EM-437 | EM-856 |
EM Organismal Scale
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Not applicable | Not applicable | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
EM-327 | EM-437 | EM-856 |
None Available | 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-327 | EM-437 | EM-856 |
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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-327 | EM-437 | EM-856 |
None | None | None |