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-449 |
EM-779 ![]() |
EM Short Name
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Decrease in erosion (shoreline), St. Croix, USVI | Arthropod flower preference, CA, USA |
EM Full Name
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Decrease in erosion (shoreline) by reef, St. Croix, USVI | Arthropod flower type preference, California, USA |
EM Source or Collection
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US EPA | None |
EM Source Document ID
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335 | 399 |
Document Author
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Yee, S. H., Dittmar, J. A., and L. M. Oliver | Lundin, O., Ward, K.L., and N.M. Williams |
Document Year
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2014 | 2018 |
Document Title
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Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Indentifying native plants for coordinated hanbitat manegement of arthroppod pollinators, herbivores and natural enemies |
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-449 |
EM-779 ![]() |
Not applicable | Not applicable | |
Contact Name
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Susan H. Yee | Ola Lundin |
Contact Address
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US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Department of Ecology, Swedish Univ. of Agricultural Sciences, Uppsala, Sweden |
Contact Email
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yee.susan@epa.gov | ola.lundin@slu.se |
EM ID
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EM-449 |
EM-779 ![]() |
Summary Description
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ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef." | ABSTRACT: " Plant species differed in attractiveness for each arthropod functional group. Floral area of the focal plant species positively affected honeybee, predator, and parasitic wasp attractiveness. Later bloom period was associated with lower numbers of parasitic wasps. Flower type (actinomorphic, composite, or zygomorphic) predicted attractiveness for honeybees, which preferred actinomorphic over composite flowers and for parasitic wasps, which preferred composite flowers over actinomorphic flowers. 4. Across plant species, herbivore, predator, and parasitic wasp abundances were positively correlated, and honeybee abundance correlated negatively to herbivore abundance. 5. Synthesis and applications. We use data from our common garden experiment to inform evidence-based selection of plants that support pollinators and natural enemies without enhancing potential pests. We recommend selecting plant species with a high floral area per ground area unit, as this metric predicts the abundances of several groups of beneficial arthropods. Multiple correlations between functionally important arthropod groups across plant species stress the importance of a multifunctional approach to arthropod habitat management. " Changes in arthropod abundance were estimated for flower type (entered as separate runs); Actinomorphic, Composite, Zygomorphic. 43 plant species evaluated included Amsinckia intermedia, Calandrinia menziesii, Nemophila maculata, Nemophila menziesii, Phacelia ciliata, Achillea millefolium, Collinsia heterophylla, Fagopyrum esculentum, Lasthenia fremontii, Lasthenia glabrata, Limnanthes alba, Lupinus microcarpus densiflorus, Lupinus succelentus, Phacelia californica, Phacelia campanularia, Phacelia tanacetifolia, Salvia columbariae, Sphaeralcea ambigua, Trifolium fucatum, Trifolium gracilentum, Antirrhinum conutum, Clarkia purpurea, Clarkia unguiculata, Clarkia williamsonii, Eriophyllum lanatum, Eschscholzia californica, Monardella villosa, Scrophularia californica, Asclepia eriocarpa, Asclepia fascicularis, Camissoniopsis Cheiranthifolia, Eriogonum fasciculatum, Gilia capitata, Grindelia camporum, Helianthus annuus, Lupinus formosus, Malacothrix saxatilis, Oenothera elata, Helianthus bolanderi, Helianthus californicus, Madia elegans, Trichostema lanceolatum, Heterotheca grandiflora." |
Specific Policy or Decision Context Cited
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None identified | None reported |
Biophysical Context
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No additional description provided | Mediteranean climate |
EM Scenario Drivers
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No scenarios presented | Arthropod groups |
EM ID
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EM-449 |
EM-779 ![]() |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs |
New or Pre-existing EM?
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Application of existing 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-449 |
EM-779 ![]() |
Document ID for related EM
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Doc-335 | None |
EM ID for related EM
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EM-447 | EM-448 | None |
EM Modeling Approach
EM ID
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EM-449 |
EM-779 ![]() |
EM Temporal Extent
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2006-2007, 2010 | 2015-2016 |
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-449 |
EM-779 ![]() |
Bounding Type
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Physiographic or ecological | Point or points |
Spatial Extent Name
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Coastal zone surrounding St. Croix | Harry Laidlaw Jr. Honey Bee Research facility |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | <1 ha |
EM ID
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EM-449 |
EM-779 ![]() |
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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area, for pixel or radial feature | Not applicable |
Spatial Grain Size
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10 m x 10 m | Not applicable |
EM ID
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EM-449 |
EM-779 ![]() |
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-449 |
EM-779 ![]() |
Model Calibration Reported?
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Yes | Not applicable |
Model Goodness of Fit Reported?
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No | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None |
Model Operational Validation Reported?
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Yes | Not applicable |
Model Uncertainty Analysis Reported?
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No | No |
Model Sensitivity Analysis Reported?
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No | 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-449 |
EM-779 ![]() |
None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-449 |
EM-779 ![]() |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-449 |
EM-779 ![]() |
Centroid Latitude
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17.73 | 38.54 |
Centroid Longitude
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-64.77 | -121.79 |
Centroid Datum
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WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Provided |
EM ID
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EM-449 |
EM-779 ![]() |
EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Agroecosystems |
Specific Environment Type
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Coral reefs | Agricultural fields |
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-449 |
EM-779 ![]() |
EM Organismal Scale
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Not applicable | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
EM-449 |
EM-779 ![]() |
None Available |
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EnviroAtlas URL
EM-449 |
EM-779 ![]() |
National Hydrography Dataset Plus (NHD PlusV2) | 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-449 |
EM-779 ![]() |
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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-449 |
EM-779 ![]() |
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