EcoService Models Library (ESML)
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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.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-71 | EM-195 | EM-260 | EM-656 |
EM-743 
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EM-998 |
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EM Short Name
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Community flowering date, Central French Alps | C Sequestration and De-N, Tampa Bay, FL, USA | Coral taxa and land development, St.Croix, VI, USA | P8 UCM | WESP: Irrigation water, ID, USA | CAESAR landscape evolution model |
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EM Full Name
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Community weighted mean flowering date, Central French Alps | Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | P8 Urban Catchment model method | WESP: Irrigation return water treatment, Idaho, USA | Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model |
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EM Source or Collection
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EU Biodiversity Action 5 | US EPA | US EPA | None | None | None |
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EM Source Document ID
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260 | 186 | 96 |
377 ?Comment:Published to the web. Previously versions prepared for EPA. |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
468 |
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Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Russell, M. and Greening, H. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Walker, W. Jr., and J.D. Walker | Murphy, C. and T. Weekley | Van De Wiel, M. J., Coulthard, T. J., Macklin, M. G., & Lewin, J. |
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Document Year
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2011 | 2013 | 2011 | 2015 | 2012 | 2007 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Estimating benefits in a recovering estuary: Tampa Bay, Florida | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | P8 Urban Catchment Model Version 3.5 | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published report | Published journal manuscript |
Software and Access
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EM ID
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EM-71 | EM-195 | EM-260 | EM-656 |
EM-743
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EM-998 |
| Not applicable | Not applicable | Not applicable | http://www.wwwalker.net/p8/v35/webhelp/splash.htm | Not applicable | http://www.coulthard.org.uk/ | |
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Contact Name
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Sandra Lavorel | M. Russell | Leah Oliver | William Walker Jr., PhD | Chris Murphy | Marco J. Van De Wiel |
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Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA | National Health and Environmental Research Effects Laboratory | Concord, Massachusetts | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Department of Geography, University of Western Ontario, London, Ontario, Canada |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | Russell.Marc@epamail.epa.gov | leah.oliver@epa.gov | bill@wwwalker.net | chris.murphy@idfg.idaho.gov | mvandew3@uwo.ca |
EM Description
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EM ID
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EM-71 | EM-195 | EM-260 | EM-656 |
EM-743
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EM-998 |
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Summary Description
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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." AUTHOR'S DESCRIPTION: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | AUTHOR'S DESCRIPTION: "...we examine the change in the production of ecosystem goods produced as a result of restoration efforts and potential relative cost savings for the Tampa Bay community from seagrass expansion (more than 3,100 ha) and coastal marsh and mangrove restoration (∼600 ha), since 1990… The objectives of this article are to explore the roles that ecological processes and resulting ecosystem goods have in maintaining healthy estuarine systems by (1) quantifying the production of specific ecosystem goods in a subtropical estuarine system and (2) determining potential cost savings of improved water quality and increased habitat in a recovering estuary." (pp. 2) | AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | Author description: " P8 simulates the generation and transport of stormwater runoff pollutants in urban watersheds. Continuous water-balance and mass-balance calculations are performed on a user-defined drainage system consisting of the following elements: - Watersheds (<= 250 nonpoint source areas) - Devices (<=75 runoff storage/treatment areas or BMP's) - Particles (<= 5 fractions with different settling velocities) - Water Quality Components (<= 10 associated with particles) Simulations are driven by hourly precipitation and daily air temperature time series. Runoff contributions from snowmelt are also simulated. 'P8' abbreviates "Program for Predicting Polluting Particle Passage Thru Pits, Puddles, and Ponds", which more or less captures the basic features and functions of the model. It has been developed for use by engineers and planners in designing and evaluating runoff treatment schemes for existing or proposed urban developments. Design objectives are typically expressed in terms of percentage reduction in suspended solids or other water quality component. Despite its limitations, P8 has been used by state and local regulatory agencies as a consistent framework for evaluating proposed developments. Depending on applications, other models could be either too simple (easily used, but ignoring important factors) or too complex (requiring considerable site-specific data and/or user expertise). P8 attempts to strike a balance to between those extremes. Predicted water quality components include total suspended solids (sum of the individual particle fractions), total phosphorus, total Kjeldahl nitrogen, copper, lead, zinc, and total hydrocarbons. Simulated BMP types include detention ponds (wet, dry, extended), infiltration basins, swales, buffer strips, or other devices with user-specified stage/discharge curves and infiltration rates. A simple water budget algorithm can be used to estimate groundwater storage and stream base flow in watershed-scale applications. Initial calibrations were based upon runoff quality and particle settling velocity data collected under the EPA's Nationwide Urban Runoff Program (Athayede et al., 1983). Calibrations to impervious area runoff parameters for Wisconsin watersheds have been subsequently developed. Inputs are structured in terms which should be familiar to planners and engineers involved in hydrologic evaluation. Several tabular and graphic output formats are provided. " | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | We introduce a new computational model designed to simulate and investigate reach-scale alluvial dynamics within a landscape evolution model. The model is based on the cellular automaton concept, whereby the continued iteration of a series of local process ‘rules’ governs the behaviour of the entire system. The model is a modified version of the CAESAR landscape evolution model, which applies a suite of physically based rules to simulate the entrainment, transport and deposition of sediments. The CAESAR model has been altered to improve the representation of hydraulic and geomorphic processes in an alluvial environment. In-channel and overbank flow, sediment entrainment and deposition, suspended load and bed load transport, lateral erosion and bank failure have all been represented as local cellular automaton rules. Although these rules are relatively simple and straightforward, their combined and repeatedly iterated effect is such that complex, non-linear geomorphological response can be simulated within the model. Examples of such larger-scale, emergent responses include channel incision and aggradation, terrace formation, channel migration and river meandering, formation of meander cutoffs, and transitions between braided and single-thread channel patterns. In the current study, the model is illustrated on a reach of the River Teifi, near Lampeter, Wales, UK. |
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Specific Policy or Decision Context Cited
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None identified | Restoration of seagrass | Not applicable | None identified | None identified | None identified |
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Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove | nearshore; <1.5 km offshore; <12 m depth | Urban setting | restored, enhanced and created wetlands | River Teifi, Lampeter, Wales |
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EM Scenario Drivers
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No scenarios presented | Habitat loss or restoration in Tampa Bay Estuary | Not applicable | N/A | Sites, function or habitat focus | Varying flow velocities and durations |
EM Relationship to Other EMs or Applications
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EM ID
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EM-71 | EM-195 | EM-260 | EM-656 |
EM-743
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EM-998 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs | Method Only |
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New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-71 | EM-195 | EM-260 | EM-656 |
EM-743
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EM-998 |
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Document ID for related EM
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Doc-260 | Doc-269 | None | None | None | Doc-390 | Doc-467 |
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EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | None | None | EM-718 | EM-734 | EM-760 | EM-761 | EM-763 | EM-764 | EM-766 | EM-767 | EM-768 | EM-997 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-71 | EM-195 | EM-260 | EM-656 |
EM-743
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EM-998 |
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EM Temporal Extent
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2007-2008 | 1982-2010 | 2006-2007 | Not applicable | 2010-2012 | Not applicable |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable | discrete | Not applicable | continuous |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Hour | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-71 | EM-195 | EM-260 | EM-656 |
EM-743
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EM-998 |
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Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Not applicable | Multiple unrelated locations (e.g., meta-analysis) | Watershed/Catchment/HUC |
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Spatial Extent Name
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Central French Alps | Tampa Bay Estuary | St.Croix, U.S. Virgin Islands | Not applicable | Wetlands in idaho | River Teifi |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 1000-10,000 km^2. | 10-100 km^2 | Not applicable | 100,000-1,000,000 km^2 | 1000-10,000 km^2. |
Spatial Distribution of Computations
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EM ID
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EM-71 | EM-195 | EM-260 | EM-656 |
EM-743
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EM-998 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable | Not applicable | Not applicable |
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Spatial Grain Size
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20 m x 20 m | 1 ha | Not applicable | Not applicable | Not applicable | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-71 | EM-195 | EM-260 | EM-656 |
EM-743
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EM-998 |
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EM Computational Approach
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Analytic | Analytic | Analytic | Numeric | Numeric | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-71 | EM-195 | EM-260 | EM-656 |
EM-743
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EM-998 |
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Model Calibration Reported?
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No | Yes | Yes | Yes | No | Not applicable |
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Model Goodness of Fit Reported?
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Yes | No | Yes | Not applicable | No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None |
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None | None | None |
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Model Operational Validation Reported?
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No | No | No | Not applicable | No | Not applicable |
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Model Uncertainty Analysis Reported?
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No | No | Yes | Not applicable | No | Not applicable |
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Model Sensitivity Analysis Reported?
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No | No | No | Not applicable | No | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | 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])
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| EM-71 | EM-195 | EM-260 | EM-656 |
EM-743
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EM-998 |
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None | None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-71 | EM-195 | EM-260 | EM-656 |
EM-743
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EM-998 |
| None |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-71 | EM-195 | EM-260 | EM-656 |
EM-743
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EM-998 |
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Centroid Latitude
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45.05 | 27.95 | 17.75 | Not applicable | 44.06 | 52.04 |
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Centroid Longitude
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6.4 | -82.47 | -64.75 | Not applicable | -114.69 | -4.39 |
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Centroid Datum
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WGS84 | WGS84 | NAD83 | Not applicable | WGS84 | WGS84 |
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Centroid Coordinates Status
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Provided | Estimated | Estimated | Not applicable | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-71 | EM-195 | EM-260 | EM-656 |
EM-743
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EM-998 |
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Rivers and Streams |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows. | Subtropical Estuary | stony coral reef | Urban catchments | created, restored and enhanced wetlands | River |
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EM Ecological Scale
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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 |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-71 | EM-195 | EM-260 | EM-656 |
EM-743
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EM-998 |
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EM Organismal Scale
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Community | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-71 | EM-195 | EM-260 | EM-656 |
EM-743
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EM-998 |
| None Available | None Available |
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None Available | None Available | None Available |
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)
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| EM-71 | EM-195 | EM-260 | EM-656 |
EM-743
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EM-998 |
| None |
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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>
fegs1Help
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-71 | EM-195 | EM-260 | EM-656 |
EM-743
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EM-998 |
| None |
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None | None |
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