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-590 | EM-970 |
EM Short Name
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Fish species richness, Puerto Rico, USA | Air quality regulation, Lisbon |
EM Full Name
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Fish species richness, Puerto Rico, 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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None | None |
EM Source Document ID
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355 | 454 |
Document Author
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Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Matos, P., Vieira, J., Rocha, B., Branquinho, C., & Pinho, P. |
Document Year
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2007 | 2019 |
Document Title
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Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | 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 |
Comments on Status
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Published journal manuscript | Published journal manuscript |
EM ID
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EM-590 | EM-970 |
Not applicable | Not applicable | |
Contact Name
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Simon Pittman | Pedro Pinho |
Contact Address
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1305 East-West Highway, Silver Spring, MD 20910, USA | N/A |
Contact Email
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simon.pittman@noaa.gov | ppinho@fc.ul.pt |
EM ID
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EM-590 | EM-970 |
Summary Description
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ABSTRACT: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems." | 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 provided | None identified |
Biophysical Context
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Hard and soft benthic habitat types approximately to the 33m isobath | Green spaces in Lisbon, Portugal |
EM Scenario Drivers
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No scenarios presented | No scenarios presented |
EM ID
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EM-590 | EM-970 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application |
New or Pre-existing EM?
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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-590 | EM-970 |
Document ID for related EM
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Doc-355 | None |
EM ID for related EM
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EM-698 | EM-699 | None |
EM Modeling Approach
EM ID
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EM-590 | EM-970 |
EM Temporal Extent
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2000-2005 | 2015-2018 |
EM Time Dependence
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time-stationary | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable |
EM ID
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EM-590 | EM-970 |
Bounding Type
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Physiographic or ecological | Physiographic or ecological |
Spatial Extent Name
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SW Puerto Rico, | Urban green spaces in Lisbon |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | 100-1000 km^2 |
EM ID
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EM-590 | EM-970 |
EM Spatial Distribution
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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 | map scale, for cartographic feature |
Spatial Grain Size
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not reported | N/A |
EM ID
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EM-590 | EM-970 |
EM Computational Approach
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Analytic | Analytic |
EM Determinism
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deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-590 | EM-970 |
Model Calibration Reported?
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No | Yes |
Model Goodness of Fit Reported?
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Yes | Yes |
Goodness of Fit (metric| value | unit)
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Model Operational Validation Reported?
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Yes | No |
Model Uncertainty Analysis Reported?
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No | No |
Model Sensitivity Analysis Reported?
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Yes | Unclear |
Model Sensitivity Analysis Include Interactions?
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No | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-590 | EM-970 |
None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-590 | EM-970 |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-590 | EM-970 |
Centroid Latitude
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17.9 | 38.75 |
Centroid Longitude
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67.11 | 9.8 |
Centroid Datum
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WGS84 | None provided |
Centroid Coordinates Status
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Estimated | Estimated |
EM ID
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EM-590 | EM-970 |
EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Created Greenspace |
Specific Environment Type
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shallow coral reefs | Green spaces in Lisbon, Portugal |
EM Ecological Scale
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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-590 | EM-970 |
EM Organismal Scale
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Guild or Assemblage | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
EM-590 | EM-970 |
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EnviroAtlas URL
EM-590 | EM-970 |
None Available | 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-590 | EM-970 |
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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-590 | EM-970 |
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