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-71 |
EM-148 ![]() |
EM-195 | EM-260 | EM-303 |
EM Short Name
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Community flowering date, Central French Alps | InVEST - Water provision, Francoli River, Spain | C Sequestration and De-N, Tampa Bay, FL, USA | Coral taxa and land development, St.Croix, VI, USA | Biological pest control, Uppland Province, Sweden |
EM Full Name
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Community weighted mean flowering date, Central French Alps | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) v2.4.2 - Water provision, Francoli River, Spain | Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | Biological control of agricultural pests by natural predators, Uppland Province, Sweden |
EM Source or Collection
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EU Biodiversity Action 5 | InVEST | US EPA | US EPA | None |
EM Source Document ID
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260 | 280 | 186 | 96 | 299 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Marques, M., Bangash, R.F., Kumar, V., Sharp, R., and Schuhmacher, M. | Russell, M. and Greening, H. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Jonsson, M., Bommarco, R., Ekbom, B., Smith, H.G., Bengtsson, J., Caballero-Lopez, B., Winqvist, C., and Olsson, O. |
Document Year
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2011 | 2013 | 2013 | 2011 | 2014 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | The impact of climate change on water provision under a low flow regime: A case study of the ecosystems services in the Francoli river basin | 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 | Ecological production functions for biological control services in agricultural landscapes |
Document Status
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Peer reviewed and published | Peer reviewed and published | 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 | Published journal manuscript | Published journal manuscript |
EM ID
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EM-71 |
EM-148 ![]() |
EM-195 | EM-260 | EM-303 |
Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable | |
Contact Name
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Sandra Lavorel | Montse Marquès | M. Russell | Leah Oliver | Mattias Jonsson |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Environmental Analysis and Management Group, Department d'Enginyeria Qimica, Universitat Rovira I Virgili, Tarragona, Catalonia, Spain | US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA | National Health and Environmental Research Effects Laboratory | Department of Ecology, Swedish University of Agricultural Sciences, PO Box 7044, SE-750 07 Uppsala, Sweden |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | montserrat.marques@fundacio.urv.cat | Russell.Marc@epamail.epa.gov | leah.oliver@epa.gov | mattias.jonsson@slu.se |
EM ID
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EM-71 |
EM-148 ![]() |
EM-195 | EM-260 | EM-303 |
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." | 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: "InVEST 2.4.2 model runs as script tool in the ArcGIS 10 ArcTool-Box on a gridded map at an annual average time step, and its results can be reported in either biophysical or monetary terms, depending on the needs and the availability of information. It is most effectively used within a decision making process that starts with a series of stakeholder consultations to identify questions and services of interest to policy makers, communities, and various interest groups. These questions may concern current service delivery and how services may be affected by new programmes, policies, and conditions in the future. For questions regarding the future, stakeholders develop scenarios of management interventions or natural changes to explore the consequences of potential changes on natural resources [21]. This tool informs managers and policy makers about the impacts of alternative resource management choices on the economy, human well-being, and the environment, in an integrated way [22]. The spatial resolution of analyses is flexible, allowing users to address questions at the local, regional or global scales. | 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) | ABSTRACT: "We develop a novel, mechanistic landscape model for biological control of cereal aphids, explicitly accounting for the influence of landscape composition on natural enemies varying in mobility, feeding rates and other life history traits. Finally, we use the model to map biological control services across cereal fields in a Swedish agricultural region with varying landscape complexity. The model predicted that biological control would reduce crop damage by 45–70% and that the biological control effect would be higher in complex landscapes. In a validation with independent data, the model performed well and predicted a significant proportion of biological control variation in cereal fields. However, much variability remains to be explained, and we propose that the model could be improved by refining the mechanistic understanding of predator dynamics and accounting for variation in aphid colonization." |
Specific Policy or Decision Context Cited
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None identified | None identified | Restoration of seagrass | Not applicable | None identified |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Mediteranean coastal mountains | Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove | nearshore; <1.5 km offshore; <12 m depth | Spring-sown cereal croplands, where the bird chearry-oat aphid is a key aphid pest. The aphid colonizes the crop during late May and early June, depending on weather and location. The colonization phase is followed by a brief phase of rapid exponential population growth by wingless aphids, continuing until about the time of crop heading, in late June or early July. After heading, aphid populations usually decline rapidly in the crop due to decreased plant quality and migration to grasslands. The aphids are attacked by a complex of arthropod natural enemies, but parasitism is not important in the region and therefore not modelled here. |
EM Scenario Drivers
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No scenarios presented | IPPC scenarios A2- severe changes in temperature and precipitation, B1 - more moderate variations in temperature and precipitation schemes from the present | Habitat loss or restoration in Tampa Bay Estuary | Not applicable | No scenarios presented |
EM ID
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EM-71 |
EM-148 ![]() |
EM-195 | EM-260 | EM-303 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application |
New or Pre-existing EM?
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New or revised model | Application of existing 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
EM ID
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EM-71 |
EM-148 ![]() |
EM-195 | EM-260 | EM-303 |
Document ID for related EM
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Doc-260 | Doc-269 | Doc-307 | Doc-311 | Doc-338 | Doc-205 | None | None | None |
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 | EM-344 | EM-368 | EM-437 | EM-111 | None | None | None |
EM Modeling Approach
EM ID
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EM-71 |
EM-148 ![]() |
EM-195 | EM-260 | EM-303 |
EM Temporal Extent
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2007-2008 | 1971-2100 | 1982-2010 | 2006-2007 | 2009 |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
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EM-71 |
EM-148 ![]() |
EM-195 | EM-260 | EM-303 |
Bounding Type
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Physiographic or Ecological | Watershed/Catchment/HUC | Physiographic or Ecological | Physiographic or Ecological | Geopolitical |
Spatial Extent Name
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Central French Alps | Francoli River | Tampa Bay Estuary | St.Croix, U.S. Virgin Islands | Uppland province |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 10-100 km^2 | 10,000-100,000 km^2 |
EM ID
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EM-71 |
EM-148 ![]() |
EM-195 | EM-260 | EM-303 |
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) | spatially lumped (in all 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 | Not applicable | area, for pixel or radial feature |
Spatial Grain Size
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20 m x 20 m | 30m x 30m | 1 ha | Not applicable | 25 m x 25 m |
EM ID
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EM-71 |
EM-148 ![]() |
EM-195 | EM-260 | EM-303 |
EM Computational Approach
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Analytic | Numeric | Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-71 |
EM-148 ![]() |
EM-195 | EM-260 | EM-303 |
Model Calibration Reported?
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No | No | Yes | Yes | No |
Model Goodness of Fit Reported?
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Yes | No | No | Yes | No |
Goodness of Fit (metric| value | unit)
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None | None |
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None |
Model Operational Validation Reported?
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No |
Yes ?Comment:Used Nash-Sutcliffe model efficiency index |
No | No | Yes |
Model Uncertainty Analysis Reported?
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No | No | No | Yes | No |
Model Sensitivity Analysis Reported?
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No | No | No | No |
Yes ?Comment:AUTHOR'S NOTE: "Varying aphid fecundity, overall predator abundances and attack rates affected the biological control effect, but had little influence on the relative differences between landscapes with high and low levels of biological control. The model predictions were more sensitive to changing the predators' landscape relations, but, with few exceptions, did not dramatically alter the overall patterns generated by the model." |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | No |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-71 |
EM-148 ![]() |
EM-195 | EM-260 | EM-303 |
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None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-71 |
EM-148 ![]() |
EM-195 | EM-260 | EM-303 |
None | None |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-71 |
EM-148 ![]() |
EM-195 | EM-260 | EM-303 |
Centroid Latitude
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45.05 | 41.26 | 27.95 | 17.75 | 59.52 |
Centroid Longitude
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6.4 | 1.18 | -82.47 | -64.75 | 17.9 |
Centroid Datum
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WGS84 | WGS84 | WGS84 | NAD83 | WGS84 |
Centroid Coordinates Status
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Provided | Estimated | Estimated | Estimated | Estimated |
EM ID
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EM-71 |
EM-148 ![]() |
EM-195 | EM-260 | EM-303 |
EM Environmental Sub-Class
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Agroecosystems | Grasslands | Rivers and Streams | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Agroecosystems | Grasslands |
Specific Environment Type
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Subalpine terraces, grasslands, and meadows. | Coastal mountains | Subtropical Estuary | stony coral reef | Spring-sown cereal croplands and surrounding grassland and non-arable land |
EM Ecological Scale
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Not applicable | 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 | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-71 |
EM-148 ![]() |
EM-195 | EM-260 | EM-303 |
EM Organismal Scale
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Community | Not applicable | Not applicable | Guild or Assemblage | Individual or population, within a species |
Taxonomic level and name of organisms or groups identified
EM-71 |
EM-148 ![]() |
EM-195 | EM-260 | EM-303 |
None Available | None Available | None Available |
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EnviroAtlas URL
EM-71 |
EM-148 ![]() |
EM-195 | EM-260 | EM-303 |
None Available | Average Annual Precipitation, The Watershed Boundary Dataset (WBD) | Carbon Storage by Tree Biomass | None Available | GAP Ecological Systems |
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-71 |
EM-148 ![]() |
EM-195 | EM-260 | EM-303 |
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-71 |
EM-148 ![]() |
EM-195 | EM-260 | EM-303 |
None |
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