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-65 | EM-193 | EM-943 | EM-970 |
EM Short Name
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Green biomass production, Central French Alps | Cultural ecosystem services, Bilbao, Spain | Visitation to natural areas, New England, USA | Air quality regulation, Lisbon |
EM Full Name
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Green biomass production, Central French Alps | Cultural ecosystem services, Bilbao, Spain | Estimating natural area use with cell phone data, Narragansett Beach, New England, USA | Modeling the provision of air-quality regulation ecosystem service provided by urban green spaces using lichens as ecological indicators |
EM Source or Collection
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EU Biodiversity Action 5 |
None ?Comment:EU Mapping Studies |
US EPA | None |
EM Source Document ID
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260 | 191 | 436 | 454 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H. | Merrill, N.H., Atkinson, S.F., Mulvaney, K.K., Mazzotta, K.K., and J. Bousquin | Matos, P., Vieira, J., Rocha, B., Branquinho, C., & Pinho, P. |
Document Year
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2011 | 2013 | 2020 | 2019 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning | Using data derived from cellular phone locations to estimate visitation to natural areas: An application to water recreation in New England, USA | Modeling the provision of air-quality regulation ecosystem service provided by urban green spaces using lichens as ecological indicators |
Document Status
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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 |
EM ID
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EM-65 | EM-193 | EM-943 | EM-970 |
Not applicable | Not applicable | https://github.com/USEPA/Recreation_Benefits.git | Not applicable | |
Contact Name
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Sandra Lavorel | Izaskun Casado-Arzuaga | Nathaniel Merrill | Pedro Pinho |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain | Atlantic Coastal Environmental Sciences Division, U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Narragansett, Rhode Island, United States of America, | N/A |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | izaskun.casado@ehu.es | merrill.nathaniel@epa.gov | ppinho@fc.ul.pt |
EM ID
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EM-65 | EM-193 | EM-943 | EM-970 |
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 "This paper presents a method to quantify cultural ecosystem services (ES) and their spatial distribution in the landscape based on ecological structure and social evaluation approaches. The method aims to provide quantified assessments of ES to support land use planning decisions. A GIS-based approach was used to estimate and map the provision of recreation and aesthetic services supplied by ecosystems in a peri-urban area located in the Basque Country, northern Spain. Data of two different public participation processes (frequency of visits to 25 different sites within the study area and aesthetic value of different landscape units) were used to validate the maps. Three maps were obtained as results: a map showing the provision of recreation services, an aesthetic value map and a map of the correspondences and differences between both services. The data obtained in the participation processes were found useful for the validation of the maps. A weak spatial correlation was found between aesthetic quality and recreation provision services, with an overlap of the highest values for both services only in 7.2 % of the area. A consultation with decision-makers indicated that the results were considered useful to identify areas that can be targeted for improvement of landscape and recreation management." | ABSTRACT: "We introduce and validate the use of commercially available human mobility datasets based on cell phone locations to estimate visitation to natural areas. By combining this data with on-the-ground observations of visitation to water recreation areas in New England, we fit a model to estimate daily visitation for four months to more than 500 sites. The results show the potential for this new big data source of human mobility to overcome limitations in traditional methods of estimating visitation and to provide consistent information at policy-relevant scales. However, the data providers’ opaque and rapidly developing methods for processing locational information required a calibration and validation against data collected by traditional means to confidently reproduce the desired estimates of visitation. We found that with this calibration, the high-resolution information in both space and time provided by cell phone location-derived data creates opportunities for developing next-generation models of human interactions with the natural environment. " | The UN Sustainable Development Goals states that urban air pollution must be tackled to create more inclusive, safe, resilient and sustainable cities. Urban green infrastructures can mitigate air pollution, but a crucial step to use this knowledge into urban management is to quantify how much air-quality regulation can green spaces provide and to understand how the provision of this ecosystem service is affected by other environmental factors. Considering the insufficient number of air quality monitoring stations in cities to monitor the wide range of natural and anthropic sources of pollution with high spatial resolution, ecological indicators of air quality are an alternative cost-effective tool. The aim of this work was to model the supply of air-quality regulation based on urban green spaces characteristics and other environmental factors. For that, we sampled lichen diversity in the centroids of 42 urban green spaces in Lisbon, Portugal. Species richness was the best biodiversity metric responding to air pollution, considering its simplicity and its significative response to the air pollutants concentration data measured in the existent air quality monitoring stations. Using that metric, we then created a model to estimate the supply of air quality regulation provided by green spaces in all green spaces of Lisbon based on the response to the following environmental drivers: the urban green spaces size and its vegetation density. We also used the unexplained variance of this model to map the background air pollution. Overall, we suggest that management should target the smallest urban green spaces by increasing green space size or tree density. The use of ecological indicators, very flexible in space, allow the understanding and the modeling of the provision of air-quality regulation by urban green spaces, and how urban green spaces can be managed to improve air quality and thus improve human well-being and cities resilience. |
Specific Policy or Decision Context Cited
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None identified | Land management, ecosystem management, response to EU 2020 Biodiversity Strategy | None identified | None identified |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Northern Spain; Bizkaia region | Natural area water bodies | Green spaces in Lisbon, Portugal |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | N/A | No scenarios presented |
EM ID
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EM-65 | EM-193 | EM-943 | EM-970 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application |
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 |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-65 | EM-193 | EM-943 | EM-970 |
Document ID for related EM
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Doc-260 | None | None | None |
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 | None | None | None |
EM Modeling Approach
EM ID
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EM-65 | EM-193 | EM-943 | EM-970 |
EM Temporal Extent
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2007-2009 | 2000 - 2007 | 2017 | 2015-2018 |
EM Time Dependence
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time-stationary | time-stationary | time-dependent | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | past time | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | discrete | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable | 1 | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Day | Not applicable |
EM ID
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EM-65 | EM-193 | EM-943 | EM-970 |
Bounding Type
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Physiographic or Ecological | Geopolitical | Point or points | Physiographic or ecological |
Spatial Extent Name
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Central French Alps | Bilbao Metropolitan Greenbelt | Cape Cod | Urban green spaces in Lisbon |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 |
EM ID
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EM-65 | EM-193 | EM-943 | EM-970 |
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 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 | other (specify), for irregular (e.g., stream reach, lake basin) | map scale, for cartographic feature |
Spatial Grain Size
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20 m x 20 m | 2 m x 2 m | water feature edge (beach) | N/A |
EM ID
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EM-65 | EM-193 | EM-943 | EM-970 |
EM Computational Approach
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Analytic | Analytic | Numeric | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-65 | EM-193 | EM-943 | EM-970 |
Model Calibration Reported?
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No | No | Yes | Yes |
Model Goodness of Fit Reported?
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Yes | No |
Yes ?Comment:Random forest model performance statistics |
Yes |
Goodness of Fit (metric| value | unit)
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None |
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Model Operational Validation Reported?
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Yes | Yes | Yes | No |
Model Uncertainty Analysis Reported?
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No | No | Unclear | No |
Model Sensitivity Analysis Reported?
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No | No | Yes | Unclear |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Unclear | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-65 | EM-193 | EM-943 | EM-970 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-65 | EM-193 | EM-943 | EM-970 |
None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-65 | EM-193 | EM-943 | EM-970 |
Centroid Latitude
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45.05 | 43.25 | 41.72 | 38.75 |
Centroid Longitude
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6.4 | -2.92 | -70.29 | 9.8 |
Centroid Datum
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WGS84 | WGS84 | WGS84 | None provided |
Centroid Coordinates Status
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Provided | Provided | Estimated | Estimated |
EM ID
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EM-65 | EM-193 | EM-943 | EM-970 |
EM Environmental Sub-Class
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Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Lakes and Ponds | Near Coastal Marine and Estuarine | Created Greenspace |
Specific Environment Type
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Subalpine terraces, grasslands, and meadows | none | beaches | Green spaces in Lisbon, Portugal |
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 corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-65 | EM-193 | EM-943 | EM-970 |
EM Organismal Scale
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Community | Not applicable | Not applicable | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
EM-65 | EM-193 | EM-943 | EM-970 |
None Available | None Available | None Available |
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EnviroAtlas URL
EM-65 | EM-193 | EM-943 | EM-970 |
GAP Ecological Systems | Percent IUCN Status II, Percent GAP Status 1 & 2 | Average Annual Precipitation | Green Space per Capita |
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-65 | EM-193 | EM-943 | EM-970 |
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-65 | EM-193 | EM-943 | EM-970 |
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