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
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EM ID
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EM-105 |
EM-541 |
EM-592 | EM-843 | EM-904 |
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EM Short Name
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Benthic habitat associations, Willapa Bay, OR, USA | InVEST fisheries, lobster, South Africa | APEX v1501 | Mourning dove abundance, Piedmont region, USA | Drag coefficient Laminaria hyperborea |
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EM Full Name
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Benthic macrofaunal habitat associations, Willapa Bay, OR, USA | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa | APEX (Agricultural Policy/Environmental eXtender Model) v1501 | Mourning dove abundance, Piedmont ecoregion, USA | Drag coefficient Laminaria hyperborea |
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EM Source or Collection
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US EPA | InVEST | None | None | None |
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EM Source Document ID
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39 |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
357 | 405 | 424 |
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Document Author
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Ferraro, S. P. and Cole, F. A. | Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby | Steglich, E. M., J. Jeong and J. R. Williams | Riffel, S., Scognamillo, D., and L. W. Burger | Mendez, F. J. and I. J. Losada |
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Document Year
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2007 | 2018 | 2016 | 2008 | 2004 |
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Document Title
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Benthic macrofauna–habitat associations in Willapa Bay, Washington, USA | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals | Agricultural Policy/Environmental eXtender Model User's Manual Version 1501 | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | An empirical model to estimate the propagation of random breaking and nonbreaking waves over vegetation fields |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript |
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EM ID
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EM-105 |
EM-541 |
EM-592 | EM-843 | EM-904 |
| Not applicable | https://www.naturalcapitalproject.org/invest/ | https://epicapex.tamu.edu/manuals-and-publications/ | Not applicable | Not applicable | |
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Contact Name
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Steve Ferraro | Michelle Ward | E. M. Steglich | Sam Riffell | F. J. Mendez |
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Contact Address
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U.S. EPA 2111 SE Marine Science Drive Newport, OR 97365 | ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia | Blackland Research and Extension Center, 720 East Blackland Road, Temple, TX 76502 | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Not reported |
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Contact Email
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ferraro.steven@epa.gov | m.ward@uq.edu.au | epicapex@brc.tamus.edu | sriffell@cfr.msstate.edu | mendezf@unican.es |
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EM ID
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EM-105 |
EM-541 |
EM-592 | EM-843 | EM-904 |
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Summary Description
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AUTHOR'S DESCRIPTION: "In this paper we report the results of 2 estuary-wide studies of benthic macrofaunal habitat associations in Willapa Bay, Washington, USA. This research is part of an effort to develop empirical models of biota-habitat associations that can be used to help identify critical habitats, prioritize habitats for environmental protection, index habitat suitability (U.S. Fish and Wildlife Service, 1980; Kapustka, 2003), perform habitat equivalency and compensatory restoration analyses (Fonseca et al., 2002; Kirsch et al., 2005), and as habitat value criteria in ecological risk assessments (Obery and Landis, 2002; Ferraro and Cole, 2004; Landis et al., 2004)." (491) | AUTHOR'S DESCRIPTION: "Here we develop a method for assessing future scenarios of environmental management change that improve coastal ecosystem services and thereby, support the success of the SDGs. We illustrate application of the method using a case study of South Africa’s West Coast Rock Lobster fishery within the Table Mountain National Park (TMNP) Marine Protected Area...We calculated the retrospective and current value of the West Coast Rock Lobster fishery using published and unpublished data from various sources and combined the market worth of landed lobster from recreational fishers, small-scale fisheries (SSF), large-scale fisheries (LSF) and poachers. Then using the InVEST tool, we combined data to build scenarios that describe possible futures for the West Coast Rock Lobster fishery (see Table 1). The first scenario, entitled ‘Business as Usual’ (BAU), takes the current situation and most up-to-date data to model the future if harvest continues at the existing rate. The second scenario is entitled ‘Redirect the Poachers’ (RP), which attempts to model implementation of strict management, whereby poaching is minimised from the Marine Protected Area and other economic and nutritional sources are made available through government initiatives. The third scenario, entitled ‘Large Scale Cutbacks’ (LSC), excludes large-scale fisheries from harvesting West Coast Rock Lobster within the TMNP Marine Protected Area." | ABSTRACT: "APEX is a tool for managing whole farms or small watersheds to obtain sustainable production efficiency and maintain environmental quality. APEX operates on a daily time step and is capable of performing long term simulations (1-4000 years) at the whole farm or small watershed level. The watershed may be divided into many homogeneous (soils, land use, topography, etc.) subareas (<4000). The routing component simulates flow from one subarea to another through channels and flood plains to the watershed outlet and transports sediment, nutrients, and pesticides. This allows evaluation of interactions between fields in respect to surface run-on, sediment deposition and degradation, nutrient and pesticide transport and subsurface flow. Effects of terrace systems, grass waterways, strip cropping, buffer strips/vegetated filter strips, crop rotations, plant competition, plant burning, grazing patterns of multiple herds, fertilizer, irrigation, liming, furrow diking, drainage systems, and manure management (feed yards and dairies with or without lagoons) can be simulated and assessed. Most recent developments in APEX1501 include: • Flexible grazing schedule of multiple owners and herds across landscape and paddocks. • Wind dust distribution from feedlots. • Manure erosion from feedlots and grazing fields. • Optional pipe and crack flow in soil due to tree root growth. • Enhanced filter strip consideration. • Extended lagoon pumping and manure scraping options. • Enhanced burning operation. • Carbon pools and transformation equations similar to those in the Century model with the addition of the Phoenix C/N microbial biomass model. • Enhanced water table monitoring. • Enhanced denitrification methods. • Variable saturation hydraulic conductivity method. • Irrigation using reservoir and well reserves. • Paddy module for use with rice or wetland areas." | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. " | ABSTRACT: "In this work, a model for wave transformation on vegetation fields is presented. The formulation includes wave damping and wave breaking over vegetation fields at variable depths. Based on a nonlinear formulation of the drag force, either the transformation of monochromatic waves or irregular waves can be modelled considering geometric and physical characteristics of the vegetation field. The model depends on a single parameter similar to the drag coefficient, which is parameterized as a function of the local Keulegan–Carpenter number for a specific type of plant. Given this parameterization, determined with laboratory experiments for each plant type, the model is able to reproduce the root-mean-square wave height transformation observed in experimental data with reasonable accuracy." AUTHOR'S DESCRIPTION: "Therefore, a relation between C˜D and some nondimensional flow parameters is desirable to characterize hydrodynamically the L. hyperborea model plants for predictable purposes." |
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Specific Policy or Decision Context Cited
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None identified | Future rock lobster fisheries management | None identified | None reported | None identified |
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Biophysical Context
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benthic estuarine | No additional description provided | No additional description provided | Conservation Reserve Program lands left to go fallow | No additional description provided |
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EM Scenario Drivers
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No scenarios presented | Fisheries exploitation; fishing vulnerability (of age classes) | No scenarios presented | N/A | No scenarios presented |
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EM ID
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EM-105 |
EM-541 |
EM-592 | EM-843 | EM-904 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application | Method + Application |
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New or Pre-existing EM?
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New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-105 |
EM-541 |
EM-592 | EM-843 | EM-904 |
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Document ID for related EM
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None | None | None | Doc-405 | Doc-424 |
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EM ID for related EM
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None | None | None | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-844 | EM-845 | EM-846 | EM-847 | EM-896 | EM-897 |
EM Modeling Approach
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EM ID
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EM-105 |
EM-541 |
EM-592 | EM-843 | EM-904 |
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EM Temporal Extent
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1996,1998 | 1986-2115 | Not applicable | 2008 | Not applicable |
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EM Time Dependence
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time-stationary | time-dependent | time-dependent | time-stationary | Not applicable |
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EM Time Reference (Future/Past)
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Not applicable | future time | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | discrete | discrete | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | 1 | 1 | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Year | Day | Not applicable | Not applicable |
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EM ID
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EM-105 |
EM-541 |
EM-592 | EM-843 | EM-904 |
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Bounding Type
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Physiographic or Ecological | Geopolitical | Not applicable | Physiographic or ecological | Not applicable |
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Spatial Extent Name
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Willapa Bay | Table Mountain National Park Marine Protected Area | Not applicable | Piedmont Ecoregion | Not applicable |
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Spatial Extent Area (Magnitude)
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100-1000 km^2 | 100-1000 km^2 | Not applicable | 100,000-1,000,000 km^2 | Not applicable |
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EM ID
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EM-105 |
EM-541 |
EM-592 | EM-843 | EM-904 |
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EM Spatial Distribution
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spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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Not applicable | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable |
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Spatial Grain Size
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Not applicable | Not applicable | homogenous subareas | Not applicable | Not applicable |
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EM ID
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EM-105 |
EM-541 |
EM-592 | EM-843 | EM-904 |
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EM Computational Approach
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Analytic | Numeric | Numeric | Analytic | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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EM ID
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EM-105 |
EM-541 |
EM-592 | EM-843 | EM-904 |
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Model Calibration Reported?
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Yes | No | Not applicable | Yes | Yes |
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Model Goodness of Fit Reported?
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Yes | No | Not applicable | No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None | None | None |
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Model Operational Validation Reported?
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No |
Yes ?Comment:A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data. |
Not applicable | No | Unclear |
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Model Uncertainty Analysis Reported?
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Yes | No | Not applicable | No | No |
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Model Sensitivity Analysis Reported?
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No | No | Not applicable | Yes | No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Unclear | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-105 |
EM-541 |
EM-592 | EM-843 | EM-904 |
| None | None | None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-105 |
EM-541 |
EM-592 | EM-843 | EM-904 |
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None | None |
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Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-105 |
EM-541 |
EM-592 | EM-843 | EM-904 |
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Centroid Latitude
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46.24 | -34.18 | Not applicable | 36.23 | Not applicable |
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Centroid Longitude
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-124.06 | 18.35 | Not applicable | -81.9 | Not applicable |
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Centroid Datum
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WGS84 | WGS84 | Not applicable | WGS84 | Not applicable |
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Centroid Coordinates Status
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Provided | Provided | Not applicable | Estimated | Not applicable |
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EM ID
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EM-105 |
EM-541 |
EM-592 | EM-843 | EM-904 |
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EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine |
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Specific Environment Type
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Drowned river valley estuary | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | Terrestrial environment associated with agroecosystems | grasslands | Near Coastal Marine and Estuarine |
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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 | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
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EM ID
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EM-105 |
EM-541 |
EM-592 | EM-843 | EM-904 |
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EM Organismal Scale
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Species | Individual or population, within a species | Not applicable | Species | Species |
Taxonomic level and name of organisms or groups identified
| EM-105 |
EM-541 |
EM-592 | EM-843 | EM-904 |
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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-105 |
EM-541 |
EM-592 | EM-843 | EM-904 |
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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-105 |
EM-541 |
EM-592 | EM-843 | EM-904 |
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None |
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