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-339 |
EM Short Name
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InVEST crop pollination, NJ and PA, USA |
EM Full Name
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InVEST crop pollination, New Jersey and Pennsylvania, USA |
EM Source or Collection
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InVEST |
EM Source Document ID
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279 |
Document Author
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Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf |
Document Year
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2009 |
Document Title
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Modelling pollination services across agricultural landscapes |
Document Status
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Peer reviewed and published |
Comments on Status
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Published journal manuscript |
EM ID
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EM-339 |
http://www.naturalcapitalproject.org/models/crop_pollination.html | |
Contact Name
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Eric Lonsdorf |
Contact Address
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Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA |
Contact Email
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ericlonsdorf@lpzoo.org |
EM ID
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EM-339 |
Summary Description
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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." |
Specific Policy or Decision Context Cited
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None identified |
Biophysical Context
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No additional description provided |
EM Scenario Drivers
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No scenarios presented |
EM ID
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EM-339 |
Method Only, Application of Method or Model Run
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Method + Application |
New or Pre-existing EM?
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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-339 |
Document ID for related EM
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Doc-279 |
EM ID for related EM
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EM-340 | EM-338 |
EM Modeling Approach
EM ID
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EM-339 |
EM Temporal Extent
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2000-2002 |
EM Time Dependence
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time-stationary |
EM Time Reference (Future/Past)
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Not applicable |
EM Time Continuity
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Not applicable |
EM Temporal Grain Size Value
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Not applicable |
EM Temporal Grain Size Unit
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Not applicable |
EM ID
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EM-339 |
Bounding Type
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Other |
Spatial Extent Name
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Central New Jersey and east-central Pennsylvania |
Spatial Extent Area (Magnitude)
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1000-10,000 km^2. |
EM ID
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EM-339 |
EM Spatial Distribution
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spatially distributed (in at least some cases) |
Spatial Grain Type
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area, for pixel or radial feature |
Spatial Grain Size
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30 m x 30 m |
EM ID
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EM-339 |
EM Computational Approach
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Analytic |
EM Determinism
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deterministic |
Statistical Estimation of EM
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EM ID
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EM-339 |
Model Calibration Reported?
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Unclear |
Model Goodness of Fit Reported?
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No |
Goodness of Fit (metric| value | unit)
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None |
Model Operational Validation Reported?
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Yes ?Comment:Aggregate native bee abundance on watermelon flowers was measured at 23 sites in 2005. Species richness was measured using the specimens collected from watermelon flowers at the end of the sampling period. |
Model Uncertainty Analysis Reported?
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No |
Model Sensitivity Analysis Reported?
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No |
Model Sensitivity Analysis Include Interactions?
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Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-339 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-339 |
None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-339 |
Centroid Latitude
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40.2 |
Centroid Longitude
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-74.8 |
Centroid Datum
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WGS84 |
Centroid Coordinates Status
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Estimated |
EM ID
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EM-339 |
EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Agroecosystems |
Specific Environment Type
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Cropland and surrounding landscape |
EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-339 |
EM Organismal Scale
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Species |
Taxonomic level and name of organisms or groups identified
EM-339 |
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EnviroAtlas URL
EM-339 |
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-339 |
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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-339 |
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