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-79 | EM-340 | EM-985 |
EM Short Name
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Divergence in flowering date, Central French Alps | InVEST crop pollination, Costa Rica | Atlantis ecosystem assessment submodel |
EM Full Name
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Functional divergence in flowering date, Central French Alps | InVEST crop pollination, Costa Rica | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
EM Source or Collection
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EU Biodiversity Action 5 | InVEST | None |
EM Source Document ID
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260 | 279 | 463 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | Fulton, E.A., Link, J.S., Kaplan, I.C., Savina‐Rolland, M., Johnson, P., Ainsworth, C., Horne, P., Gorton, R., Gamble, R.J., Smith, A.D. and Smith, D.C. |
Document Year
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2011 | 2009 | 2011 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Modelling pollination services across agricultural landscapes | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
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-79 | EM-340 | EM-985 |
Not applicable | http://www.naturalcapitalproject.org/models/crop_pollination.html | https://noaa-fisheries-integrated-toolbox.github.io/Atlantis | |
Contact Name
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Sandra Lavorel | Eric Lonsdorf | Elizabeth Fulton |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | CSIRO Wealth from Oceans Flagship, Division of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas. 7001, Australia |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | ericlonsdorf@lpzoo.org | beth.fulton@csiro.au |
EM ID
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EM-79 | EM-340 | EM-985 |
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, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Functional divergence of flowering date 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." | 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. ABSTRACT: "Background and Aims: Crop pollination by bees and other animals is an essential ecosystem service. Ensuring the maintenance of the service requires a full understanding of the contributions of landscape elements to pollinator populations and crop pollination. Here, the first quantitative model that predicts pollinator abundance on a landscape is described and tested. Methods: Using information on pollinator nesting resources, floral resources and foraging distances, the model predicts the relative abundance of pollinators within nesting habitats. From these nesting areas, it then predicts relative abundances of pollinators on the farms requiring pollination services. Model outputs are compared with data from coffee in Costa Rica, watermelon and sunflower in California and watermelon in New Jersey–Pennsylvania (NJPA). Key Results: Results from Costa Rica and California, comparing field estimates of pollinator abundance, richness or services with model estimates, are encouraging, explaining up to 80 % of variance among farms. However, the model did not predict observed pollinator abundances on NJPA, so continued model improvement and testing are necessary. The inability of the model to predict pollinator abundances in the NJPA landscape may be due to not accounting for fine-scale floral and nesting resources within the landscapes surrounding farms, rather than the logic of our model. Conclusions: The importance of fine-scale resources for pollinator service delivery was supported by sensitivity analyses indicating that the model's predictions depend largely on estimates of nesting and floral resources within crops. Despite the need for more research at the finer-scale, the approach fills an important gap by providing quantitative and mechanistic model from which to evaluate policy decisions and develop land-use plans that promote pollination conservation and service delivery." AUTHOR'S DESCRIPTION: "…Lacking information on seasonality, a single flight season was assumed for all species..." | Models are key tools for integrating a wide range of system information in a common framework. Attempts to model exploited marine ecosystems can increase understanding of system dynamics; identify major processes, drivers and responses; highlight major gaps in knowledge; and provide a mechanism to ‘road test’ management strategies before implementing them in reality. The Atlantis modelling framework has been used in these roles for a decade and is regularly being modified and applied to new questions (e.g. it is being coupled to climate, biophysical and economic models to help consider climate change impacts, monitoring schemes and multiple use management). This study describes some common lessons learned from its implementation, particularly in regard to when these tools are most effective and the likely form of best practices for ecosystem-based management (EBM). Most importantly, it highlighted that no single management lever is sufficient to address the many trade-offs associated with EBM and that the mix of measures needed to successfully implement EBM will differ between systems and will change through time. Although it is doubtful that any single management action will be based solely on Atlantis, this modelling approach continues to provide important insights for managers when making natural resource management decisions. |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified |
Biophysical Context
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Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | No additional description provided | N/A |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented |
EM ID
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EM-79 | EM-340 | EM-985 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method Only |
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-79 | EM-340 | EM-985 |
Document ID for related EM
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Doc-260 | Doc-269 | Doc-279 | Doc-456 | Doc-459 | Doc-461 |
EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83 | EM-338 | EM-339 | EM-978 | EM-981 | EM-983 | EM-990 | EM-991 |
EM Modeling Approach
EM ID
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EM-79 | EM-340 | EM-985 |
EM Temporal Extent
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2007-2008 | 2001-2002 | Not applicable |
EM Time Dependence
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time-stationary | time-stationary | time-dependent |
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-79 | EM-340 | EM-985 |
Bounding Type
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Physiographic or Ecological | Other | Not applicable |
Spatial Extent Name
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Central French Alps | Large coffee farm, Valle del General | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | Not applicable |
EM ID
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EM-79 | EM-340 | EM-985 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | Not applicable |
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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20 m x 20 m | 30 m x 30 m | Not applicable |
EM ID
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EM-79 | EM-340 | EM-985 |
EM Computational Approach
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Analytic | Analytic | 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-79 | EM-340 | EM-985 |
Model Calibration Reported?
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No | Unclear | Not applicable |
Model Goodness of Fit Reported?
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Yes | No | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None |
Model Operational Validation Reported?
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No | Yes | Not applicable |
Model Uncertainty Analysis Reported?
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No | No | Not applicable |
Model Sensitivity Analysis Reported?
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No | Yes | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | No | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-79 | EM-340 | EM-985 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-79 | EM-340 | EM-985 |
None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-79 | EM-340 | EM-985 |
Centroid Latitude
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45.05 | 9.13 | Not applicable |
Centroid Longitude
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6.4 | -83.37 | Not applicable |
Centroid Datum
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WGS84 | WGS84 | Not applicable |
Centroid Coordinates Status
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Provided | Estimated | Not applicable |
EM ID
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EM-79 | EM-340 | EM-985 |
EM Environmental Sub-Class
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Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas |
Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Cropland and surrounding landscape | Multiple |
EM Ecological Scale
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Ecological scale is coarser 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 of differentiation of organisms modeled
EM ID
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EM-79 | EM-340 | EM-985 |
EM Organismal Scale
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Community | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-79 | EM-340 | EM-985 |
None Available |
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None Available |
EnviroAtlas URL
EM-79 | EM-340 | EM-985 |
None Available | GAP Ecological Systems | Big game hunting recreation demand, Percent GAP Status 1 & 2, Total Employment |
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-79 | EM-340 | EM-985 |
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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-79 | EM-340 | EM-985 |
None |
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None |