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-340 | EM-653 |
EM Short Name
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InVEST crop pollination, Costa Rica | Natural amenities and population migration, USA |
EM Full Name
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InVEST crop pollination, Costa Rica | Natural amenities and rural population migration, USA |
EM Source or Collection
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InVEST | USDA Forest Service |
EM Source Document ID
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279 | 375 |
Document Author
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Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | Cordell H. K., V. Heboyan, F. Santos, J. C. Bergstrom |
Document Year
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2009 | 2011 |
Document Title
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Modelling pollination services across agricultural landscapes | Natural amenities and rural population migration |
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 report |
EM ID
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EM-340 | EM-653 |
http://www.naturalcapitalproject.org/models/crop_pollination.html | Not applicable | |
Contact Name
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Eric Lonsdorf | Ken Cordell |
Contact Address
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Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | U.S. Department of Agriculture, Forest Service, Southern Research Station, Athens, GA 30602 |
Contact Email
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ericlonsdorf@lpzoo.org | Not reported |
EM ID
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EM-340 | EM-653 |
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." AUTHOR'S DESCRIPTION: "…Lacking information on seasonality, a single flight season was assumed for all species..." | ABSTRACT: "Research suggests that significant relationships exist between rural population change and natural amenities. Thus, understanding and predicting domestic migration trends as a function of changes in natural amenities is important for effective regional growth and development policies and strategies. In this study, we first estimated an econometric model which showed the effects of natural amenities, such as climate and landscape variables, on rural population migration patterns in the United States between 1990 and 2007. The estimated model was then used to predict the effects of changes in these variables on rural county net migration and population growth to 2060 under alternative future climate and land use projections. Results suggest that people prefer rural areas with mild winters and cooler summers; thus we can expect a direct impact of climate change on population migration when areas associated with these conditions change. Results also suggest preference for varied landscapes that feature a mix of forest land and open space (e g , pasture and range land). During the projection period from 2010 to 2060 in the United States, changes in natural amenities were predicted to have positive effects on rural population migration trends in most parts of the Intermountain and Pacific Northwest regions, and some parts of the Southeastern, South Central, and Northeastern U S regions (e g , Southern Appalachian Mountains, Ozark Mountains, northern New England). Changes in natural amenities were predicted to have negative effects on rural population migration trends during the projection period in Midwestern regions (e g , Great Plains and North Central regions)." AUTHOR'S DESCRIPTION: "This model was estimated for 2,014 rural counties in the continental United States using various national data bases and sources. The estimated model was then used to predict the effects of changes in these variables on rural county net migration and population growth to 2060 under alternative future climate and land use projections." |
Specific Policy or Decision Context Cited
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None identified | None identified |
Biophysical Context
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No additional description provided | No additional description provided |
EM Scenario Drivers
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No scenarios presented | Climate projections based on the CGCM 3 1 general circulation model of moderate warming (IPCC). The A1B scenario assumes a growing world population that peaks in the mid-century and balanced technological growth. |
EM ID
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EM-340 | EM-653 |
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-340 | EM-653 |
Document ID for related EM
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Doc-279 | None |
EM ID for related EM
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EM-338 | EM-339 | None |
EM Modeling Approach
EM ID
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EM-340 | EM-653 |
EM Temporal Extent
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2001-2002 | 1982-2060 |
EM Time Dependence
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time-stationary | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | future time |
EM Time Continuity
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Not applicable | discrete |
EM Temporal Grain Size Value
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Not applicable | 1 |
EM Temporal Grain Size Unit
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Not applicable | Year |
EM ID
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EM-340 | EM-653 |
Bounding Type
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Other | Geopolitical |
Spatial Extent Name
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Large coffee farm, Valle del General | continental United States |
Spatial Extent Area (Magnitude)
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10-100 km^2 | >1,000,000 km^2 |
EM ID
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EM-340 | EM-653 |
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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30 m x 30 m | varies |
EM ID
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EM-340 | EM-653 |
EM Computational Approach
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Analytic | Numeric |
EM Determinism
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deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-340 | EM-653 |
Model Calibration Reported?
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Unclear | Yes |
Model Goodness of Fit Reported?
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No | No |
Goodness of Fit (metric| value | unit)
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None | None |
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 | No |
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-340 | EM-653 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-340 | EM-653 |
None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-340 | EM-653 |
Centroid Latitude
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9.13 | 39.8 |
Centroid Longitude
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-83.37 | -98.55 |
Centroid Datum
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WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated |
EM ID
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EM-340 | EM-653 |
EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren |
Specific Environment Type
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Cropland and surrounding landscape | Terrestrial environments including water bodies and coastlines |
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-340 | EM-653 |
EM Organismal Scale
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Species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-340 | EM-653 |
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None Available |
EnviroAtlas URL
EM-340 | EM-653 |
GAP Ecological Systems | Dasymetric Allocation of Population, GAP Ecological Systems, Average Annual Precipitation, Total Employment, Employment Rate |
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-340 | EM-653 |
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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-340 | EM-653 |
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None |