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-541
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EM-629 | 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 | InVEST fisheries, lobster, South Africa | SolVES, Pike & San Isabel NF, WY | 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 | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa | SolVES, Social Values for Ecosystem Services, Pike and San Isabel National Forest, CO | Mourning dove abundance, Piedmont ecoregion, USA |
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EM Source or Collection
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US EPA | US EPA | InVEST | 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. |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
369 | 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 | Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | Riffel, S., Scognamillo, D., and L. W. Burger |
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Document Year
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2007 | 2015 | 2018 | 2014 | 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 | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | 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 | 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 | 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-541
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EM-629 | EM-843 |
| Not applicable | http://www.hexsim.net/ | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | |
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Contact Name
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Steve Ferraro | Theresa M. Nogeire | Michelle Ward | Benson Sherrouse | 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 | ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia | USGS, 5522 Research Park Dr., Baltimore, MD 21228, 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 | m.ward@uq.edu.au | bcsherrouse@usgs.gov | 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-541
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EM-629 | 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]." | 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: " "Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders.With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems. | 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 | Future rock lobster fisheries management | None | None reported |
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Biophysical Context
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benthic estuarine | No additional description provided | No additional description provided | Rocky mountain conifer forests | 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 | Fisheries exploitation; fishing vulnerability (of age classes) | N/A | 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-541
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EM-629 | 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 | Method + Application |
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New or Pre-existing EM?
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New or revised model | Application of existing 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-541
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EM-629 | EM-843 |
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Document ID for related EM
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None | Doc-328 | Doc-327 | Doc-2 | None | Doc-369 | Doc-405 |
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EM ID for related EM
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None | EM-403 | EM-98 | None | EM-626 | EM-628 | 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-541
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EM-629 | EM-843 |
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EM Temporal Extent
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1996,1998 | 60 yr | 1986-2115 | 2004-2008 | 2008 |
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EM Time Dependence
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time-stationary | time-dependent | time-dependent | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | future time | future time | 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 | 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-541
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EM-629 | EM-843 |
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Bounding Type
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Physiographic or Ecological | Physiographic or ecological | Geopolitical | Geopolitical | Physiographic or ecological |
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Spatial Extent Name
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Willapa Bay | San Joaquin Valley, CA | Table Mountain National Park Marine Protected Area | National Park | Piedmont Ecoregion |
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Spatial Extent Area (Magnitude)
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100-1000 km^2 | 10,000-100,000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 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-541
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EM-629 | 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 distributed (in at least some 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 | area, for pixel or radial feature | Not applicable |
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Spatial Grain Size
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Not applicable | 14 ha | Not applicable | 30m2 | 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-541
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EM-629 | EM-843 |
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EM Computational Approach
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Analytic | Numeric | Numeric | Numeric | 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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Model Checking Procedures Used
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EM ID
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EM-105 |
EM-422
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EM-541
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EM-629 | EM-843 |
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Model Calibration Reported?
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Yes | Unclear | No | No | Yes |
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Model Goodness of Fit Reported?
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Yes | No | No | Yes | No |
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Goodness of Fit (metric| value | unit)
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None | None |
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None |
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Model Operational Validation Reported?
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No | 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. |
No | No |
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Model Uncertainty Analysis Reported?
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Yes | No | No | No | No |
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Model Sensitivity Analysis Reported?
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No | Yes | No | No | Yes |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | No | Not applicable | Not applicable | 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-541
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EM-629 | EM-843 |
| None |
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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-541
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EM-629 | EM-843 |
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None |
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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-541
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EM-629 | EM-843 |
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Centroid Latitude
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46.24 | 36.13 | -34.18 | 38.7 | 36.23 |
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Centroid Longitude
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-124.06 | -120 | 18.35 | 105.89 | -81.9 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Provided | Estimated | Provided | 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-541
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EM-629 | 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) | Near Coastal Marine and Estuarine | Forests | Grasslands |
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Specific Environment Type
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Drowned river valley estuary | Agricultural region (converted desert) and terrestrial perimeter | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | Montain forest | 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 is finer than that of 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-541
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EM-629 | 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 | Not applicable | Species |
Taxonomic level and name of organisms or groups identified
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| EM-105 |
EM-422
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EM-541
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EM-629 | EM-843 |
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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.detail.cicesHelp
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| EM-105 |
EM-422
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EM-541
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EM-629 | 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>
fegs1Help
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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-541
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EM-629 | EM-843 |
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None |
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