EcoService Models Library (ESML)
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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.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-105 |
EM-422 
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EM-661
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EM-843 |
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EM Short Name
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Benthic habitat associations, Willapa Bay, OR, USA | HexSim v2.4, San Joaquin kit fox, CA, USA | Alwife phosphorus flux in lakes, Connecticut, USA | Mourning dove abundance, Piedmont region, USA |
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EM Full Name
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Benthic macrofaunal habitat associations, Willapa Bay, OR, USA | HexSim v2.4, San Joaquin kit fox rodenticide exposure, California, USA | Net phosphorus flux in freshwater lakes from alewives, Connecticut, USA | Mourning dove abundance, Piedmont ecoregion, USA |
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EM Source or Collection
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US EPA | US EPA | None | None |
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EM Source Document ID
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39 |
337 ?Comment:The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships. |
383 | 405 |
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Document Author
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Ferraro, S. P. and Cole, F. A. | Nogeire, T. M., J. J. Lawler, N. H. Schumaker, B. L. Cypher, and S. E. Phillips | West, D. C., A. W. Walters, S. Gephard, and D. M. Post | Riffel, S., Scognamillo, D., and L. W. Burger |
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Document Year
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2007 | 2015 | 2010 | 2008 |
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Document Title
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Benthic macrofauna–habitat associations in Willapa Bay, Washington, USA | Land use as a driver of patterns of rodenticide exposure in modeled kit fox populations | Nutrient loading by anadromous alewife (Alosa pseudoharengus): contemporary patterns and predictions for restoration efforts | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds |
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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 |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-105 |
EM-422
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EM-661
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EM-843 |
| Not applicable | http://www.hexsim.net/ | Not applicable | Not applicable | |
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Contact Name
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Steve Ferraro | Theresa M. Nogeire | Derek C. West | Sam Riffell |
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Contact Address
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U.S. EPA 2111 SE Marine Science Drive Newport, OR 97365 | School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA | Dept. of Ecology and Evolutionary Biology, Yale University, 165 Prospect Street, New Haven, CT 06511, USA | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA |
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Contact Email
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ferraro.steven@epa.gov | tnogeire@gmail.com | derek.west@yale.edu | sriffell@cfr.msstate.edu |
EM Description
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EM ID
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EM-105 |
EM-422
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EM-661
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EM-843 |
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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) | ABSTRACT: "...Here, we use an individual-based population model to assess potential population-wide effects of rodenticide exposures on the endangered San Joaquin kit fox (Vulpes macrotis mutica). We estimate likelihood of rodenticide exposure across the species range for each land cover type based on a database of reported pesticide use and literature…" AUTHOR'S DESCRIPTION: "We simulated individual kit foxes across their range using HexSim [33], a computer modeling platform for constructing spatially explicit population models. Our model integrated life history traits, repeated exposures to rodenticides, and spatial data layers describing habitat and locations of likely exposures. We modeled female kit foxes using yearly time steps in which each individual had the potential to disperse, establish a home range, acquire resources from their habitat, reproduce, accumulate rodenticide exposures, and die." "Simulated kit foxes assembled home ranges based on local habitat suitability, with range size inversely related to habitat suitability [34,35]. Kit foxes aimed to acquire a home range with a target score corresponding to the observed 544 ha home range size in the most suitable habitat [26]. Modeled home ranges varied in size from 170 ha to 1000 ha. Kit foxes were assigned to a resource class depending on the quality of the habitat in their acquired home range. The resource class then influenced rates of kit fox survival," "Juveniles and adults without ranges searched for a home range across 30 km2 outside of their natal range, using HexSim’s ‘adaptive’ exploration algorithm [33]." | ABSTRACT: "Anadromous alewives (Alosa pseudoharengus) have the potential to alter the nutrient budgets of coastal lakes as they migrate into freshwater as adults and to sea as juveniles. Alewife runs are generally a source of nutrients to the freshwater lakes in which they spawn, but juveniles may export more nutrients than adults import in newly restored populations. A healthy run of alewives in Connecticut imports substantial quantities of phosphorus; mortality of alewives contributes 0.68 g P_fish–1, while surviving fish add 0.18 g P, 67% of which is excretion. Currently, alewives contribute 23% of the annual phosphorus load to Bride Lake, but this input was much greater historically, with larger runs of bigger fish contributing 2.5 times more phosphorus in the 1960s..." AUTHOR'S DESCRIPTION: "Here, we evaluate the patterns of net nutrient loading by alewives over a range of population sizes. We concentrate on phosphorus, as it is generally the nutrient that limits production in the lake ecosystems in which alewives spawn (Schindler 1978). First, we estimate net alewife nutrient loading and parameterize an alewife nutrient loading model using data from an existing run of anadromous alewives in Bride Lake. We then compare the current alewife nutrient load to that in the 1960s when alewives were more numerous and larger. Next, since little is known about the actual patterns of nutrient loading during restoration, we predict the net nutrient loading for a newly restored population across a range of adult escapement… Anadromous fish move nutrients both into and out of freshwater ecosystems, although inputs are typically more obvious and much better studied (Moore and Schindler 2004). Net loading into freshwater ecosystems is fully described as inputs due to adult mortality, gametes, and direct excretion of nutrients minus the removal of nutrients from freshwater ecosystems by juvenile fish when they emigrate… Our research was conducted at Bride Lake and Linsley Pond in Connecticut. Bride Lake contains an anadromous alewife population that we used to both evaluate contemporary and historic net nutrient loading by an alewife population and parameterize our general alewife nutrient loading model." | 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. " |
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Specific Policy or Decision Context Cited
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None identified | None identified | Restoration and management of diadromous fish runs in coastal New England | None reported |
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Biophysical Context
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benthic estuarine | No additional description provided | Bride Lake is 28.7 ha and linked to Long Island Sound by the 3.3 km Bride Brook. | Conservation Reserve Program lands left to go fallow |
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EM Scenario Drivers
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No scenarios presented | Rodenticide exposure level, and rodenticide exposure on low intensity development land cover class | current and historical run size | N/A |
EM Relationship to Other EMs or Applications
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EM ID
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EM-105 |
EM-422
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EM-661
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EM-843 |
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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 ?Comment:The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships. |
Method + Application (multiple runs exist) View EM Runs | 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 |
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-422
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EM-661
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EM-843 |
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Document ID for related EM
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None | Doc-328 | Doc-327 | Doc-2 | None | Doc-405 |
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EM ID for related EM
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None | EM-403 | EM-98 | EM-667 | EM-672 | EM-674 | EM-673 | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-844 | EM-845 | EM-846 | EM-847 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-105 |
EM-422
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EM-661
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EM-843 |
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EM Temporal Extent
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1996,1998 | 60 yr | 1960"s and early 2000's | 2008 |
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EM Time Dependence
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time-stationary | time-dependent | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | future time | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | discrete | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Year | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-105 |
EM-422
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EM-661
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EM-843 |
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Bounding Type
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Physiographic or Ecological | Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological |
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Spatial Extent Name
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Willapa Bay | San Joaquin Valley, CA | Bride Lake and Linsley Pond | Piedmont Ecoregion |
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Spatial Extent Area (Magnitude)
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100-1000 km^2 | 10,000-100,000 km^2 | 10-100 ha | 100,000-1,000,000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-105 |
EM-422
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EM-661
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EM-843 |
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EM Spatial Distribution
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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 | area, for pixel or radial feature | Not applicable | Not applicable |
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Spatial Grain Size
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Not applicable | 14 ha | Not applicable | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-105 |
EM-422
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EM-661
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EM-843 |
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EM Computational Approach
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Analytic | Numeric | Analytic | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-105 |
EM-422
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EM-661
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EM-843 |
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Model Calibration Reported?
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Yes | Unclear | Yes | Yes |
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Model Goodness of Fit Reported?
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Yes | No | No | No |
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Goodness of Fit (metric| value | unit)
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None | None | None |
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Model Operational Validation Reported?
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No | No | No | No |
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Model Uncertainty Analysis Reported?
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Yes | No | No | No |
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Model Sensitivity Analysis Reported?
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No | Yes | Yes | Yes |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | No | Unclear | Unclear |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| EM-105 |
EM-422
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EM-661
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EM-843 |
| None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-105 |
EM-422
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EM-661
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EM-843 |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-105 |
EM-422
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EM-661
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EM-843 |
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Centroid Latitude
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46.24 | 36.13 | 41.33 | 36.23 |
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Centroid Longitude
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-124.06 | -120 | -72.24 | -81.9 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Provided | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-105 |
EM-422
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EM-661
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EM-843 |
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EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Lakes and Ponds | Grasslands |
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Specific Environment Type
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Drowned river valley estuary | Agricultural region (converted desert) and terrestrial perimeter | Coastal lakes and ponds and associated streams | grasslands |
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EM Ecological Scale
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Ecological scale is finer than that of 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 and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-105 |
EM-422
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EM-661
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EM-843 |
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EM Organismal Scale
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Species | Individual or population, within a species | Individual or population, within a species | Species |
Taxonomic level and name of organisms or groups identified
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| EM-105 |
EM-422
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EM-661
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EM-843 |
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EnviroAtlas URL
| EM-105 |
EM-422
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EM-661
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EM-843 |
| None Available | Stream Length Impaired by Pesticides | Waterbody area | GAP Ecological Systems, U.S. EPA (Omernik) ecoregions |
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)
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| EM-105 |
EM-422
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EM-661
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EM-843 |
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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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-105 |
EM-422
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EM-661
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EM-843 |
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None | None |
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