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-65 | EM-71 | EM-890 | EM-941 | EM-964 | EM-998 |
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EM Short Name
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Green biomass production, Central French Alps | Community flowering date, Central French Alps | HWB Blood pressure, Great Lakes waterfront, USA | ESTIMAP - Pollination potential, Iran | EcoSim II - method | CAESAR landscape evolution model |
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EM Full Name
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Green biomass production, Central French Alps | Community weighted mean flowering date, Central French Alps | Human well being indicator- Blood pressure, Great Lakes waterfront, USA | ESTIMAP - Pollination potential, Iran | EcoSim II - method | 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 | EU Biodiversity Action 5 | None | None | None | None |
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EM Source Document ID
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260 | 260 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
434 | 448 | 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. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | Rahimi, E., Barghjelveh, S., and P. Dong | Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell | Van De Wiel, M. J., Coulthard, T. J., Macklin, M. G., & Lewin, J. |
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Document Year
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2011 | 2011 | None | 2020 | 2000 | 2007 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran) | Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II | 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 but unpublished (explain in Comment) | Peer reviewed and published | 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 | Journal manuscript submitted or in review | Published journal manuscript | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-65 | EM-71 | EM-890 | EM-941 | EM-964 | EM-998 |
| Not applicable | Not applicable | Not applicable | Not applicable | https://ecopath.org/downloads/ | http://www.coulthard.org.uk/ | |
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Contact Name
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Sandra Lavorel | Sandra Lavorel | Ted Angradi | Ehsan Rahini | Carl Walters | 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 | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran | Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4 | 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 | sandra.lavorel@ujf-grenoble.fr | tedangradi@gmail.com | ehsanrahimi666@gmail.com | c.walters@oceans.ubc.ca | mvandew3@uwo.ca |
EM Description
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EM ID
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EM-65 | EM-71 | EM-890 | EM-941 | EM-964 | 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. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)." | 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." | ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context." | Abstract: ". ..we used the ESTIMAP model to improve the results of the Lonsdorf model. For this, we included the effects of roads, railways, rivers, wetlands, lakes, altitude, climate, and ecosystem boundaries in the ESTIMAP modeling and compared the results with the Lonsdorf model. The results of the Lonsdorf model showed that the majority of Iran had a very low potential for providing pollination service and only three percent of the northern and western parts of Iran had high potential. However, the results of the ESTIMAP model showed that 16% of Iran had a high potential to provide pollination that covers most of the northern and southern parts of the country. The results of the ESTIMAP model for pollination mapping in Iran showed the Lonsdorf model of estimating pollination service can be improved through considering other relevant factors." | ABSTRACT: " EcoSim II uses results from the Ecopath procedure for trophic mass-balance analysis to define biomass dynamics models for predicting temporal change in exploited ecosystems. Key populations can be repre- sented in further detail by using delay-difference models to account for both biomass and numbers dynamics. A major problem revealed by linking the population and biomass dynamics models is in representation of population responses to changes in food supply; simple proportional growth and reproductive responses lead to unrealistic predic- tions of changes in mean body size with changes in fishing mortality. EcoSim II allows users to specify life history mechanisms to avoid such unrealistic predictions: animals may translate changes in feed- ing rate into changes in reproductive rather than growth rates, or they may translate changes in food availability into changes in foraging time that in turn affects predation risk. These options, along with model relationships for limits on prey availabil- ity caused by predation avoidance tactics, tend to cause strong compensatory responses in modeled populations. It is likely that such compensatory responses are responsible for our inability to find obvious correlations between interacting trophic components in fisheries time-series data. But Eco- sim II does not just predict strong compensatory responses: it also suggests that large piscivores may be vulnerable to delayed recruitment collapses caused by increases in prey species that are in turn competitors/predators of juvenile piscivores " | 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 | None identified | None identified | None reported | None | None identified |
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Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Waterfront districts on south Lake Michigan and south lake Erie | None additional | None, Ocean ecosystems | River Teifi, Lampeter, Wales |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | N/A | N/A | N/A | Varying flow velocities and durations |
EM Relationship to Other EMs or Applications
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EM ID
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EM-65 | EM-71 | EM-890 | EM-941 | EM-964 | 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 + Application | Method Only | 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 | Application of existing 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-65 | EM-71 | EM-890 | EM-941 | EM-964 | EM-998 |
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Document ID for related EM
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Doc-260 | Doc-260 | Doc-269 | Doc-422 | Doc-432 | None | Doc-467 |
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EM ID for related EM
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EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-886 | EM-888 | EM-889 | EM-891 | EM-893 | EM-894 | EM-895 | EM-939 | None | EM-997 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-65 | EM-71 | EM-890 | EM-941 | EM-964 | EM-998 |
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EM Temporal Extent
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2007-2009 | 2007-2008 | 2022 | 2020 | Not applicable | Not applicable |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | 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 | both | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable |
discrete ?Comment:Modeller dependent |
continuous |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable |
EM Spatial Extent
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EM ID
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EM-65 | EM-71 | EM-890 | EM-941 | EM-964 | EM-998 |
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Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Geopolitical | Other | Watershed/Catchment/HUC |
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Spatial Extent Name
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Central French Alps | Central French Alps | Great Lakes waterfront | Iran | Not applicable | River Teifi |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 1000-10,000 km^2. | >1,000,000 km^2 | Not applicable | 1000-10,000 km^2. |
Spatial Distribution of Computations
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EM ID
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EM-65 | EM-71 | EM-890 | EM-941 | EM-964 | 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 distributed (in at least some cases) ?Comment:Varies by inputs, but results are for areas of country |
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 | area, for pixel or radial feature | Not applicable | Not applicable |
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Spatial Grain Size
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20 m x 20 m | 20 m x 20 m | Not applicable | ha^2 | Not applicable | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-65 | EM-71 | EM-890 | EM-941 | EM-964 | EM-998 |
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EM Computational Approach
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Analytic | Analytic | Numeric | Numeric | Analytic | 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-65 | EM-71 | EM-890 | EM-941 | EM-964 | EM-998 |
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Model Calibration Reported?
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No | No | No | No | No | Not applicable |
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Model Goodness of Fit Reported?
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Yes | Yes | No | No | No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None | None | None |
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Model Operational Validation Reported?
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Yes | No | No | No | Not applicable | Not applicable |
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Model Uncertainty Analysis Reported?
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No | No | No | No | Not applicable | Not applicable |
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Model Sensitivity Analysis Reported?
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No | No | Yes | No | Not applicable | 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-65 | EM-71 | EM-890 | EM-941 | EM-964 | EM-998 |
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Comment:Model for Iran - no form preset id for country |
None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-65 | EM-71 | EM-890 | EM-941 | EM-964 | EM-998 |
| None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-65 | EM-71 | EM-890 | EM-941 | EM-964 | EM-998 |
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Centroid Latitude
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45.05 | 45.05 | 42.26 | 32.29 | Not applicable | 52.04 |
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Centroid Longitude
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6.4 | 6.4 | -87.84 | 53.68 | Not applicable | -4.39 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 |
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Centroid Coordinates Status
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Provided | Provided | Estimated | Estimated | Not applicable | Estimated |
Environments and Scales Modeled
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EM ID
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EM-65 | EM-71 | EM-890 | EM-941 | EM-964 | EM-998 |
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Open Ocean and Seas | Rivers and Streams |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Lake Michigan & Lake Erie waterfront | terrestrial land types | Pelagic | River |
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EM Ecological Scale
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Not applicable | Not applicable | Ecological scale corresponds to 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
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EM ID
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EM-65 | EM-71 | EM-890 | EM-941 | EM-964 | EM-998 |
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EM Organismal Scale
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Community | Community | Not applicable | Not applicable |
Other (Comment) ?Comment:Varied levels of taxonomic order |
Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-65 | EM-71 | EM-890 | EM-941 | EM-964 | EM-998 |
| None Available | None Available | None Available |
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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-65 | EM-71 | EM-890 | EM-941 | EM-964 | EM-998 |
| None | None | 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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-65 | EM-71 | EM-890 | EM-941 | EM-964 | EM-998 |
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None | None |
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