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-184 | EM-306 | EM-428 |
EM-821 
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EM-846 | EM-862 |
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EM Short Name
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ROS (Recreation Opportunity Spectrum), Europe | Urban Temperature, Baltimore, MD, USA | Retained rainwater, Guánica Bay, Puerto Rico | Aquatic vertebrate IBI for Western streams, USA | Indigo bunting abund, Piedmont region, USA | Recreational fishery index, USA |
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EM Full Name
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ROS (Recreation Opportunity Spectrum), Europe | Urban Air Temperature Change, Baltimore, MD, USA | Retained rainwater, Guánica Bay, Puerto Rico, USA | Development of an aquatic vertebrate index of biotic integrity (IBI) for Western streams, USA | Indigo bunting abundance, Piedmont ecoregion, USA | Recreational fishery index for streams and rivers, USA |
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EM Source or Collection
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EU Biodiversity Action 5 | i-Tree | USDA Forest Service | US EPA | None | None | US EPA |
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EM Source Document ID
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293 | 217 | 338 | 404 | 405 | 414 |
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Document Author
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Paracchini, M.L., Zulian, G., Kopperoinen, L., Maes, J., Schägner, J.P., Termansen, M., Zandersen, M., Perez-Soba, M., Scholefield, P.A., and Bidoglio, G. | Heisler, G. M., Ellis, A., Nowak, D. and Yesilonis, I. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Pont, D., Hughes, R.M., Whittier, T.R., and S. Schmutz. | Riffel, S., Scognamillo, D., and L. W. Burger | Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold |
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Document Year
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2014 | 2016 | 2017 | 2009 | 2008 | 2021 |
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Document Title
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Mapping cultural ecosystem services: A framework to assess the potential for outdoor recreation across the EU | Modeling and imaging land-cover influences on air-temperature in and near Baltimore, MD | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | A Predictive Index of Biotic Integrity Model for A predictive index of biotic integrity model foraquatic-vertebrate assemblages of Western U.S. Streams | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Correspondence between a recreational fishery index and ecological condition for U.S.A. streams and rivers. |
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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 | 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 | Published journal manuscript |
Software and Access
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EM ID
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EM-184 | EM-306 | EM-428 |
EM-821
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EM-846 | EM-862 |
| Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | |
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Contact Name
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Maria Luisa Paracchini | Gordon M. Heisler | Susan H. Yee | Didier Pont | Sam Riffell | Gregg Lomnicky |
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Contact Address
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Joint Research Centre, Institute for Environment and Sustainability, Via E.Fermi, 2749, I-21027 Ispra (VA), Italy | 5 Moon Library, c/o SUNY-ESF, Syracuse, NY 13210 | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Centre d’E´ tude du Machinisme Agricole et du Genie Rural, des Eaux et Foreˆts (Cemagref), Unit HYAX Hydrobiologie, 3275 Route de Ce´zanne, Le Tholonet, 13612 Aix en Provence, France | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | 200 SW 35th St., Corvallis, OR, 97333 |
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Contact Email
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luisa.paracchini@jrc.ec.europa.eu | gheisler@fs.fed.us | yee.susan@epa.gov | didier.pont@cemagref.fr | sriffell@cfr.msstate.edu | lomnicky.gregg@epa.gov |
EM Description
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EM ID
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EM-184 | EM-306 | EM-428 |
EM-821
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EM-846 | EM-862 |
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Summary Description
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ABSTRACT: "Research on ecosystem services mapping and valuing has increased significantly in recent years. However, compared to provisioning and regulating services, cultural ecosystem services have not yet beenfully integrated into operational frameworks. One reason for this is that transdisciplinarity is required toaddress the issue, since by definition cultural services (encompassing physical, intellectual, spiritual inter-actions with biota) need to be analysed from multiple perspectives (i.e. ecological, social, behavioural).A second reason is the lack of data for large-scale assessments, as detailed surveys are a main sourceof information. Among cultural ecosystem services, assessment of outdoor recreation can be based ona large pool of literature developed mostly in social and medical science, and landscape and ecologystudies. This paper presents a methodology to include recreation in the conceptual framework for EUwide ecosystem assessments (Maes et al., 2013), which couples existing approaches for recreation man-agement at country level with behavioural data derived from surveys and population distribution data.The proposed framework is based on three components: the ecosystem function (recreation potential),the adaptation of the Recreation Opportunity Spectrum framework to characterise the ecosystem serviceand the distribution of potential demand in the EU." | An empirical model for predicting below-canopy air temperature differences is developed for evaluating urban structural and vegetation influences on air temperature in and near Baltimore, MD. AUTHOR'S DESCRIPTION: "The study . . . Developed an equation for predicting air temperature at the 1.5m height as temperature difference, T, between a reference weather station and other stations in a variety of land uses. Predictor variables were derived from differences in land cover and topography along with forcing atmospheric conditions. The model method was empirical multiple linear regression analysis.. . Independent variables included remotely sensed tree cover, impervious cover, water cover, descriptors of topography, an index of thermal stability, vapor pressure deficit, and antecedent precipitation." | AUTHOR'S DESCRIPTION: "In total, 19 ecosystem services metrics were identified as relevant to stakeholder objectives in the Guánica Bay watershed identified during the 2013 Public Values Forum (Table 2)...Ecological production functions were applied to translate LULC measures of ecosystem condition to supply of ecosystem services…The volume of retained rainwater per unit area (in^3/in^2) includes both the maximum soil moisture retention and the initial abstraction of water before runoff due to infiltration, evaporation, or interception by vegetation…" | ABSTRACT: "Because of natural environmental and faunal differences and scientific perspectives, numerous indices of biological integrity (IBIs) have been developed at local, state, and regional scales in the USA. These multiple IBIs, plus different criteria for judging impairment, hinder rigorous national and multistate assessments. Many IBI metrics are calibrated for water body size, but none are calibrated explicitly for other equally important natural variables such as air temperature, channel gradient, or geology. We developed a predictive aquatic-vertebrate IBI model using a total of 871 stream sites (including 162 least-disturbed and 163 most-disturbed sites) sampled as part of the U.S. Environmental Protection Agency’s Environmental Monitoring and Assessment Program survey of 12 conterminous western U.S. states. The selected IBI metrics (calculated from both fish and aquatic amphibians) were vertebrate species richness, benthic native species richness, assemblage tolerance index, proportion of invertivore–piscivore species, and proportion of lithophilic-reproducing species. Mean model IBI scores differed significantly between least-disturbed and most-disturbed sites as well as among ecoregions. Based on a model IBI impairment criterion of 0.44 (risks of type I and II errors balanced), an estimated 34.7% of stream kilometers in the western USA were deemed impaired, compared with 18% for a set of traditional IBIs. Also, the model IBI usually displayed less variability than the traditional IBIs, presumably because it was better calibrated for natural variability. " | 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: [Sport fishing is an important recreational and economic activity, especially in Australia, Europe and North America, and the condition of sport fish populations is a key ecological indicator of water body condition for millions of anglers and the public. Despite its importance as an ecological indicator representing the status of sport fish populations, an index for measuring this ecosystem service has not been quantified by analyzing actual fish taxa, size and abundance data across the U.S.A. Therefore, we used game fish data collected from 1,561 stream and river sites located throughout the conterminous U.S.A. combined with specific fish species and size dollar weights to calculate site-specific recreational fishery index (RFI) scores. We then regressed those scores against 38 potential site-specific environmental predictor variables, as well as site-specific fish assemblage condition (multimetric index; MMI) scores based on entire fish assemblages, to determine the factors most associated with the RFI scores. We found weak correlations between RFI and MMI scores and weak to moderate correlations with environmental variables, which varied in importance with each of 9 ecoregions. We conclude that the RFI is a useful indicator of a stream ecosystem service, which should be of greater interest to the U.S.A. public and traditional fishery management agencies than are MMIs, which tend to be more useful for ecologists, environmentalists and environmental quality agencies.] |
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Specific Policy or Decision Context Cited
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None identified | None identified | Meeting water demands for agriculture and domestic purposes. | None reported | None reported | None identified |
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Biophysical Context
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No additional description provided | One airport site, one urban site, one site in deciduous leaf litter, and four sites in short grass ground cover. Measured sky view percentages ranged from 6% at the woods site, to 96% at the rural open site. | No additional descriptions provided | Wadeable and boatable streams in 12 western USA states | Conservation Reserve Program lands left to go fallow | None |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | not applicable | N/A | N/A |
EM Relationship to Other EMs or Applications
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EM ID
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EM-184 | EM-306 | EM-428 |
EM-821
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EM-846 | EM-862 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application View EM Runs | Method + Application | Method + Application |
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New or Pre-existing EM?
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Application of existing model | 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-184 | EM-306 | EM-428 |
EM-821
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EM-846 | EM-862 |
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Document ID for related EM
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Doc-290 | Doc-291 | Doc-289 | Doc-220 | Doc-219 | Doc-218 | None | Doc-403 | Doc-405 | None |
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EM ID for related EM
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None | None | None | EM-820 | EM-826 | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-843 | EM-844 | EM-845 | EM-847 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-184 | EM-306 | EM-428 |
EM-821
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EM-846 | EM-862 |
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EM Temporal Extent
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Not reported | May 5-Sept 30 2006 | 2006 - 2012 | 2004-2005 | 2008 | 2013-2014 |
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EM Time Dependence
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time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | future time | Not applicable | past time | Not applicable | past time |
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EM Time Continuity
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Not applicable | discrete | Not applicable | Not applicable | Not applicable | discrete |
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EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable | Not applicable | Not applicable | 1 |
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EM Temporal Grain Size Unit
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Not applicable | Hour | Not applicable | Not applicable | Not applicable | Year |
EM Spatial Extent
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EM ID
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EM-184 | EM-306 | EM-428 |
EM-821
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EM-846 | EM-862 |
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Bounding Type
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Geopolitical | Geopolitical | Watershed/Catchment/HUC | Geopolitical | Physiographic or ecological | Geopolitical |
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Spatial Extent Name
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European Union countries | Baltimore, MD | Guanica Bay watershed | Western 12 states | Piedmont Ecoregion | United States |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | >1,000,000 km^2 | 100,000-1,000,000 km^2 | >1,000,000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-184 | EM-306 | EM-428 |
EM-821
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EM-846 | EM-862 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:871 total sites surveyed for this work |
spatially lumped (in all cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | length, for linear feature (e.g., stream mile) |
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Spatial Grain Size
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100 m x 100 m | 10m x 10m | 30 m x 30 m | stream reach | Not applicable | stream reach (site) |
EM Structure and Computation Approach
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EM ID
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EM-184 | EM-306 | EM-428 |
EM-821
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EM-846 | EM-862 |
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EM Computational Approach
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Analytic | Analytic | Analytic | Analytic | Analytic | Analytic |
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EM Determinism
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deterministic | 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-184 | EM-306 | EM-428 |
EM-821
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EM-846 | EM-862 |
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Model Calibration Reported?
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No | Yes | No | No | Yes | No |
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Model Goodness of Fit Reported?
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No | Yes | No | No | No | No |
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Goodness of Fit (metric| value | unit)
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None |
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None | None | None | None |
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Model Operational Validation Reported?
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No | No | No |
Yes ?Comment:Compared to another journal manuscript IBI scores (Whittier et al) |
No | No |
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Model Uncertainty Analysis Reported?
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No | No | No | No | No | No |
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Model Sensitivity Analysis Reported?
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No | No | No | Yes | Yes | No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Yes | Not applicable | Not applicable |
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-184 | EM-306 | EM-428 |
EM-821
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EM-846 | EM-862 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-184 | EM-306 | EM-428 |
EM-821
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EM-846 | EM-862 |
| None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-184 | EM-306 | EM-428 |
EM-821
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EM-846 | EM-862 |
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Centroid Latitude
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48.2 | 39.28 | 17.96 | 44.2 | 36.23 | 36.21 |
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Centroid Longitude
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16.35 | -76.62 | -67.02 | -113.07 | -81.9 | -113.76 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-184 | EM-306 | EM-428 |
EM-821
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EM-846 | EM-862 |
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EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Atmosphere | Inland Wetlands | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Rivers and Streams | Grasslands | Rivers and Streams |
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Specific Environment Type
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Not applicable | Urban landscape and surrounding area | 13 LULC were used | wadeable and boatable streams | grasslands | reach |
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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 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 |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-184 | EM-306 | EM-428 |
EM-821
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EM-846 | EM-862 |
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EM Organismal Scale
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Not applicable | Not applicable | Not applicable | Guild or Assemblage | Species | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
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| EM-184 | EM-306 | EM-428 |
EM-821
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EM-846 | EM-862 |
| None Available | None Available | None Available |
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None Available |
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)
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| EM-184 | EM-306 | EM-428 |
EM-821
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EM-846 | EM-862 |
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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-184 | EM-306 | EM-428 |
EM-821
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EM-846 | EM-862 |
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None |
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