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-367 |
EM-784 ![]() |
EM Short Name
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InVEST Coastal Blue Carbon | Wildflower mix supporting bees, Florida, USA |
EM Full Name
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InVEST v3.0 Coastal Blue Carbon | Wildflower planting mix supporting bees in agricultural landscapes, Florida, USA |
EM Source or Collection
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InVEST | None |
EM Source Document ID
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310 | 400 |
Document Author
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Natural Capital Project | Williams, N.M., Ward, K.L., Pope, N., Isaacs, R., Wilson, J., May, E.A., Ellis, J., Daniels, J., Pence, A., Ullmann, K., and J. Peters |
Document Year
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2014 | 2015 |
Document Title
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Blue Carbon model - InVEST (v3.0) | Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States |
Document Status
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Documented, not peer reviewed | Peer reviewed and published |
Comments on Status
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other | Published journal manuscript |
EM ID
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EM-367 |
EM-784 ![]() |
http://ncp-dev.stanford.edu/~dataportal/invest-releases/documentation/current_release/blue_carbon.html#running-the-model | Not applicable | |
Contact Name
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Gregg Verutes | Neal Williams |
Contact Address
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Stanford University | Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616 |
Contact Email
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gverutes@stanford.edu | nmwilliams@ucdavis.edu |
EM ID
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EM-367 |
EM-784 ![]() |
Summary Description
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Please note: This ESML entry describes an InVEST model version that was current as of 2014. More recent versions may be available at the InVEST website. "InVEST Coastal Blue Carbon models the carbon cycle through a bookkeeping-type approach (Houghton, 2003). This approach simplifies the carbon cycle by accounting for storage in four main pools (aboveground biomass, belowground biomass, standing dead carbon and sediment carbon… Accumulation of carbon in coastal habitats occurs primarily in sediments (Pendleton et al., 2012). The model requires users to provide maps of coastal ecosystems that store carbon, such as mangroves and seagrasses. Users must also provide data on the amount of carbon stored in the four carbon pools and the rate of annual carbon accumulation in the sediments. If local information is not available, users can draw on the global database of values for carbon stocks and accumulation rates sourced from the peer-reviewed literature that is included in the model. If data from field studies or other local sources are available, these values should be used instead of those in the global database. The model requires land cover maps, which represent changes in human use patterns in coastal areas or changes to sea level, to estimate the amount of carbon lost or gained over a specified period of time. The model quantifies carbon storage across the land or seascape by summing the carbon stored in these four carbon pools. | Abstract: " Global trends in pollinator-dependent crops have raised awareness of the need to support managed and wild bee populations to ensure sustainable crop production. Provision of sufficient forage resources is a key element for promoting bee populations within human impacted landscapes, particularly those in agricultural lands where demand for pollination service is high and land use and management practices have reduced available flowering resources. Recent government incentives in North America and Europe support the planting of wildflowers to benefit pollinators; surprisingly, in North America there has been almost no rigorous testing of the performance of wildflower mixes, or their ability to support wild bee abundance and diversity. We tested different wildflower mixes in a spatially replicated, multiyear study in three regions of North America where production of pollinatordependent crops is high: Florida, Michigan, and California. In each region, we quantified flowering among wildflower mixes composed of annual and perennial species, and with high and low relative diversity. We measured the abundance and species richness of wild bees, honey bees, and syrphid flies at each mix over two seasons. In each region, some but not all wildflower mixes provided significantly greater floral display area than unmanaged weedy control plots. Mixes also attracted greater abundance and richness of wild bees, although the identity of best mixes varied among regions. By partitioning floral display size from mix identity we show the importance of display size for attracting abundant and diverse wild bees. Season-long monitoring also revealed that designing mixes to provide continuous bloom throughout the growing season is critical to supporting the greatest pollinator species richness. Contrary to expectation, perennials bloomed in their first season, and complementarity in attraction of pollinators among annuals and perennials suggests that inclusion of functionally diverse species may provide the greatest benefit. Wildflower mixes may be particularly important for providing resources for some taxa, such as bumble bees, which are known to be in decline in several regions of North America. No mix consistently attained the full diversity that was planted. Further study is needed on how to achieve the desired floral display and diversity from seed mixes. " Additional information in supplemental Appendices online: http://dx.doi.org/10.1890/14-1748.1.sm |
Specific Policy or Decision Context Cited
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None identified | None identrified |
Biophysical Context
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Land use land class; habitat type | field plots near agricultural fields (mixed row crop, almond, walnuts), central valley, Ca |
EM Scenario Drivers
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Land use land cover changes; habitat disturbance | Varied wildflower planting mixes of annuals and perennials |
EM ID
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EM-367 |
EM-784 ![]() |
Method Only, Application of Method or Model Run
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Method Only | Method + Application (multiple runs exist) View EM Runs |
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-367 |
EM-784 ![]() |
Document ID for related EM
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None | None |
EM ID for related EM
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None | EM-796 | EM-797 | EM-804 | EM-805 | EM-806 | EM-812 | EM-814 |
EM Modeling Approach
EM ID
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EM-367 |
EM-784 ![]() |
EM Temporal Extent
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Not applicable | 2011-2012 |
EM Time Dependence
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time-dependent | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | past time |
EM Time Continuity
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discrete | discrete |
EM Temporal Grain Size Value
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1 | 1 |
EM Temporal Grain Size Unit
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Year | Year |
EM ID
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EM-367 |
EM-784 ![]() |
Bounding Type
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Not applicable |
Point or points ?Comment:This is a guess based on information in the document. 3 field sites were separated by up to 9km |
Spatial Extent Name
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Not applicable | Agricultural plots |
Spatial Extent Area (Magnitude)
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Not applicable | 10-100 km^2 |
EM ID
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EM-367 |
EM-784 ![]() |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all cases) |
Spatial Grain Type
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volume, for 3-D feature | Not applicable |
Spatial Grain Size
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user-specified | Not applicable |
EM ID
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EM-367 |
EM-784 ![]() |
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-367 |
EM-784 ![]() |
Model Calibration Reported?
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Not applicable | No |
Model Goodness of Fit Reported?
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Not applicable | No |
Goodness of Fit (metric| value | unit)
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None | None |
Model Operational Validation Reported?
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Not applicable | No |
Model Uncertainty Analysis Reported?
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Not applicable | No |
Model Sensitivity Analysis Reported?
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Not applicable | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-367 |
EM-784 ![]() |
None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-367 |
EM-784 ![]() |
None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-367 |
EM-784 ![]() |
Centroid Latitude
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-9999 | 29.4 |
Centroid Longitude
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-9999 | -82.18 |
Centroid Datum
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Not applicable | WGS84 |
Centroid Coordinates Status
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Not applicable | Provided |
EM ID
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EM-367 |
EM-784 ![]() |
EM Environmental Sub-Class
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Inland Wetlands | Near Coastal Marine and Estuarine | Agroecosystems |
Specific Environment Type
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user specified | Agricultural landscape |
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-367 |
EM-784 ![]() |
EM Organismal Scale
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Not applicable | Species |
Taxonomic level and name of organisms or groups identified
EM-367 |
EM-784 ![]() |
None Available |
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EnviroAtlas URL
EM-367 |
EM-784 ![]() |
Carbon Storage by Tree Biomass | None Available |
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-367 |
EM-784 ![]() |
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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-367 |
EM-784 ![]() |
None |
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