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-851 ![]() |
EM-960 | EM-964 |
EM Short Name
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InVEST Coastal Vulnerability, New York, USA | HAWQS model method | EcoSim II - method |
EM Full Name
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InVEST Coastal Vulnerability, Jamaica Bay, New York, USA | Hydrologic and water quality system (HAWQS) model v.1.1 user's guide methodology | EcoSim II - method |
EM Source or Collection
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InVEST | US EPA | None |
EM Source Document ID
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410 ?Comment:Sharp R, Tallis H, Ricketts T, Guerry A, Wood S, Chaplin-Kramer R, et al. InVEST User?s Guide. User Guide. Stanford (CA): The Natural Capital Project, Stanford University, University of Minnesota, The Nature Conservancy, World Wildlife Fund; 2015. |
445 | 448 |
Document Author
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Hopper T. and M. S. Meixler | United States Environmental Protection Agency | Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell |
Document Year
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2016 | 2019 | 2000 |
Document Title
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Modeling coastal vulnerability through space and time | HAWQS 1.0 (Hydrologic and Water Quality System) modeling framework | Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published EPA report | Published journal manuscript |
EM ID
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EM-851 ![]() |
EM-960 | EM-964 |
https://naturalcapitalproject.stanford.edu/software/invest-models/coastal-vulnerability | https://dataverse.tdl.org/dataset.xhtml?persistentId=doi:10.18738/T8/GDOPBA | https://ecopath.org/downloads/ | |
Contact Name
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Thomas Hopper | Raghavan Srinivasan | Carl Walters |
Contact Address
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Not reported | Spatial Sciences Laboratory, Dept. of ecology and conservatin Biology, Texas A&M university | Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4 |
Contact Email
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Tjhop1123@gmail.com | r-srinivasan@tamu.edu | c.walters@oceans.ubc.ca |
EM ID
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EM-851 ![]() |
EM-960 | EM-964 |
Summary Description
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ABSTRACT: "Coastal ecosystems experience a wide range of stressors including wave forces, storm surge, sea-level rise, and anthropogenic modification and are thus vulnerable to erosion. Urban coastal ecosystems are especially important due to the large populations these limited ecosystems serve. However, few studies have addressed the issue of urban coastal vulnerability at the landscape scale with spatial data that are finely resolved. The purpose of this study was to model and map coastal vulnerability and the role of natural habitats in reducing vulnerability in Jamaica Bay, New York, in terms of nine coastal vulnerability metrics (relief, wave exposure, geomorphology, natural habitats, exposure, exposure with no habitat, habitat role, erodible shoreline, and surge) under past (1609), current (2015), and future (2080) scenarios using InVEST 3.2.0. We analyzed vulnerability results both spatially and across all time periods, by stakeholder (ownership) and by distance to damage from Hurricane Sandy. We found significant differences in vulnerability metrics between past, current and future scenarios for all nine metrics except relief and wave exposure…" | Author overview: " The Hydrologic and Water Quality System (HAWQS) is a web-based interactive water quantity and water quality modeling system that employs the internationally-recognized public domain model Soil and Water Assessment Tool (SWAT) as its core modeling engine. HAWQS provides users with: 1) interactive web interfaces and maps and pre-loaded input data; 2) Output data includes tables, charts, graphs, and raw data; 3) A user guide; and 4) Online development, execution, and storage for users modeling projects. HAWQS enables use of SWAT to simulate the effects of management practices based on an extensive array of crops, soils, natural vegetation types, land uses, and climate change scenarios for hydrology and the following water quality parameters: Sediment pathogens, nutrients, biological oxygen demand, dissolved oxygen, pesticides, and water temperature. HAWQS users can select from three watershed scales, or hydrologic unit codes (HUCs)—small (HUC 12), medium (HUC 10), and large (HUC 8)—to run simulations. HAWQS allows for further aggregation and scalability of annual, monthly, and daily estimates of water quality across large geographic areas up to and including the continental United States. The United States Environmental Protection Agency (USEPA) Office of Water (OW) supports and provides project management and funding for HAWQS. The Texas A&M University Spatial Sciences Laboratory and EPA subject matter experts provide ongoing technical support including system design, modeling, and software development. The United States Department of Agriculture (USDA) and Texas A&M University jointly developed SWAT and have actively supported the model for more than 25 years. The system was developed to meet the needs of the USEPA Office of Water. It can also be employed by other Federal Agencies, State and local governments, academics, and contractors. " | ABSTRACT: " EcoSim II uses results from the Ecopath procedure for trophic mass-balance analysis to define biomass dynamics models for predicting temporal change in exploited ecosystems. Key populations can be repre- sented in further detail by using delay-difference models to account for both biomass and numbers dynamics. A major problem revealed by linking the population and biomass dynamics models is in representation of population responses to changes in food supply; simple proportional growth and reproductive responses lead to unrealistic predic- tions of changes in mean body size with changes in fishing mortality. EcoSim II allows users to specify life history mechanisms to avoid such unrealistic predictions: animals may translate changes in feed- ing rate into changes in reproductive rather than growth rates, or they may translate changes in food availability into changes in foraging time that in turn affects predation risk. These options, along with model relationships for limits on prey availabil- ity caused by predation avoidance tactics, tend to cause strong compensatory responses in modeled populations. It is likely that such compensatory responses are responsible for our inability to find obvious correlations between interacting trophic components in fisheries time-series data. But Eco- sim II does not just predict strong compensatory responses: it also suggests that large piscivores may be vulnerable to delayed recruitment collapses caused by increases in prey species that are in turn competitors/predators of juvenile piscivores " |
Specific Policy or Decision Context Cited
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None identified | None identified | None |
Biophysical Context
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Jamaica Bay, New York, situated on the southern shore of Long Island, and characterized by extensive coastal ecosystems in the central bay juxtaposed with a largely urbanized shoreline containing fragmented and fringing coastal habitat. | N/A | None, Ocean ecosystems |
EM Scenario Drivers
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Past (1609), current (2015), and future (2080) scenarios | N/A | N/A |
EM ID
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EM-851 ![]() |
EM-960 | EM-964 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method Only | Method Only |
New or Pre-existing EM?
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Application of existing model | 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-851 ![]() |
EM-960 | EM-964 |
Document ID for related EM
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Doc-408 | None | None |
EM ID for related EM
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EM-849 | None | EM-1055 |
EM Modeling Approach
EM ID
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EM-851 ![]() |
EM-960 | EM-964 |
EM Temporal Extent
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1609-2080 | Not applicable | Not applicable |
EM Time Dependence
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time-stationary | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | future time | both |
EM Time Continuity
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Not applicable |
discrete ?Comment:Time can be in day, month or year increments |
discrete ?Comment:Modeller dependent |
EM Temporal Grain Size Value
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Not applicable | 1 | 1 |
EM Temporal Grain Size Unit
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Not applicable | Year | Day |
EM ID
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EM-851 ![]() |
EM-960 | EM-964 |
Bounding Type
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Physiographic or ecological | Not applicable | Other |
Spatial Extent Name
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Jamaica Bay, Long Island, New York | Not applicable | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 km^2 | Not applicable | Not applicable |
EM ID
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EM-851 ![]() |
EM-960 | EM-964 |
EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:by coastal segment |
spatially lumped (in all cases) | spatially lumped (in all cases) |
Spatial Grain Type
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length, for linear feature (e.g., stream mile) | Not applicable | Not applicable |
Spatial Grain Size
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80 m | Not applicable | Not applicable |
EM ID
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EM-851 ![]() |
EM-960 | EM-964 |
EM Computational Approach
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Analytic | Numeric | Analytic |
EM Determinism
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deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-851 ![]() |
EM-960 | EM-964 |
Model Calibration Reported?
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No | No | No |
Model Goodness of Fit Reported?
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No | No | No |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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No | No | Not applicable |
Model Uncertainty Analysis Reported?
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No | No | Not applicable |
Model Sensitivity Analysis Reported?
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No | No | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-851 ![]() |
EM-960 | EM-964 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-851 ![]() |
EM-960 | EM-964 |
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None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-851 ![]() |
EM-960 | EM-964 |
Centroid Latitude
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40.61 | Not applicable | Not applicable |
Centroid Longitude
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-73.84 | Not applicable | Not applicable |
Centroid Datum
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WGS84 | Not applicable | Not applicable |
Centroid Coordinates Status
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Provided | Not applicable | Not applicable |
EM ID
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EM-851 ![]() |
EM-960 | EM-964 |
EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Agroecosystems | Open Ocean and Seas |
Specific Environment Type
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Coastal | HUCs | Pelagic |
EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class | 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-851 ![]() |
EM-960 | EM-964 |
EM Organismal Scale
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Not applicable | Not applicable |
Other (Comment) ?Comment:Varied levels of taxonomic order |
Taxonomic level and name of organisms or groups identified
EM-851 ![]() |
EM-960 | EM-964 |
None Available | None Available |
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EnviroAtlas URL
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-851 ![]() |
EM-960 | EM-964 |
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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-851 ![]() |
EM-960 | EM-964 |
None | None |
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