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-133 | EM-419 | EM-654 |
EM-668 
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EM-843 | EM-972 |
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EM Short Name
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Flood regulation supply-demand, Etropole, Bulgaria | ARIES viewsheds, Puget Sound Region, USA | Forest recreation, Wisconsin, USA | Fish nutrient cycling , Ohio, USA | Mourning dove abundance, Piedmont region, USA | NC HUC-12 conservation prioritization tool |
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EM Full Name
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Flood regulation supply vs. demand, Municipality of Etropole, Bulgaria | ARIES (Artificial Intelligence for Ecosystem Services) Scenic viewsheds for homeowners, Puget Sound Region, Washington, USA | Forest recreation, Wisconsin, USA | Nutrient Cycling by gizzard shad, Ohio, USA | Mourning dove abundance, Piedmont ecoregion, USA | NC HUC-12 conservation prioritization tool v. 1.0, North Carolina, USA |
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EM Source or Collection
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EU Biodiversity Action 5 | ARIES | None | None | None | None |
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EM Source Document ID
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248 | 302 | 376 | 385 | 405 |
443 ?Comment:Doc 444 is an additional source for this EM |
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Document Author
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Nedkov, S., Burkhard, B. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Qiu, J. and M. G. Turner | Vanni, M.J., Bowling, A.M., Dickman, E.M., Hale, R.S., Higgins, K.A., Horgan, M.J., Knoll, L.B., Renwick, W.H., and R.A. Stein | Riffel, S., Scognamillo, D., and L. W. Burger | Warnell, K., I. Golden, and C. Canfield |
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Document Year
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2012 | 2014 | 2013 | 2006 | 2008 | 2023 |
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Document Title
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Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Spatial interactions among ecosystem services in an urbanizing agricultural watershed | Nutrient cycling by fish supports relatively more primary production as lake productivity increases | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Conservation planning tools for NC's people & nature |
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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 | Webpage |
Software and Access
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EM ID
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EM-133 | EM-419 | EM-654 |
EM-668
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EM-843 | EM-972 |
| Not applicable | http://aries.integratedmodelling.org/ | Not applicable | Not applicable | Not applicable | https://prioritizationcobenefitstool.users.earthengine.app/view/nc-huc-12-conservation-prioritizer | |
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Contact Name
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Stoyan Nedkov | Ken Bagstad | Monica G. Turner | Michael Vanni | Sam Riffell | Katie Warnell |
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Contact Address
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National Institute of Geophysics, Geodesy and Geography, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, bl.3, 1113 Sofia, Bulgaria | Geosciences and Environmental Change Science Center, US Geological Survey | Not reported | Dept of Environmental toxocology, C.emson Univ. Pendleton, South Carolina 29670, USA | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Not reported |
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Contact Email
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snedkov@abv.bg | kjbagstad@usgs.gov | turnermg@wisc.edu | vannimj@muohio.edu | sriffell@cfr.msstate.edu | katie.warnell@duke.edu |
EM Description
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EM ID
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EM-133 | EM-419 | EM-654 |
EM-668
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EM-843 | EM-972 |
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Summary Description
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ABSTRACT: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. Maps of demands for flood regulating ecosystem services in the study region were compiled based on a digital elevation model, land use information and accessibility data. Finally, the flood regulating ecosystem service supply and demand data were merged in order to produce a map showing regional supply-demand balances.The flood regulation ecosystem service demand map shows that areas of low or no relevant demands far exceed the areas of high and very high demands, which comprise only 0.6% of the municipality’s area. According to the flood regulation supply-demand balance map, areas of high relevant demands are located in places of low relevant supply capacities" AUTHOR'S DESCRIPTION: "A similar relative scale ranging from 0 to 5 was applied to assess the demands for flood regulation. A 0-value indicates that there is no relevant demand for flood regulation and 5 would indicate the highest demand for flood regulation within the case study region. Values of 2, 3 and 4 represent respective intermediate demands. The calculations were based on the assumption that the most vulnerable areas would have the highest demand for flood regulation. The vulnerability, defined as “the characteristics and circumstances of a community, system or asset that make it susceptible to the damaging effects of a hazard” (UN/ISDR, 2009), has different dimensions (e.g. social, economic, environmental, institutional). The most vulnerable places in the case study area were defined by using different sources of demographic, statistical, topographic and economic data (Nikolova et al., 2009). These areas will have the highest (5-value) demand for flood regulation…For analyzing source and sink dynamics and to identify flows of ecosystem services, the information in the matrixes and in the maps of ecosystem service supply and demand can be merged (Burkhard et al., 2012). As the landscapes’ flood regulation supply and demand are not analyzed and modeled in the same units it is not possible to calculate the balance between them quantitatively. Using the relative scale (0–5) it becomes possible to compare them and to calculate supply-demand budgets. Although this does not providea clear indication of whether there is excess supply or demand, the resulting map shows where areas of qualitatively high demand correspond with low supply and vice versa." | ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "Within a given viewshed, our models quantified the contribution of viewshed source features such as mountains and water bodies and sinks that detract from view quality, including obstructions or visual blight such as industrial or commercial development. Source, sink, and use locations were linked by a flow model that computed visibility along lines of sight from use locations to scenic viewshed features. The model includes a distance decay function that accounts for changes with distance in the value of views. We then computed the ratio of actual to theoretical provision of scenic views to compare the values accruing to homeowners relative to those for the entire landscape." | AUTHOR'S DESCRIPTION (from Supporting Information): "Forest recreation service as a function of the amount of forest habitat, recreational opportunities provided, proximity to population center, and accessibility of the area. Several assumptions were made for this assessment approach: larger areas and places with more recreational opportunities would provide more recreational service; areas near large population centers would be visited and used more than remote areas; and proximity to major roads would increase access and thus recreational use of an area… we quantified forest recreation service for each 30-m grid cells using the equation below: FRSi = Ai Σ(Oppti + Popi +Roadi), where FRS is forest recreation score, A is the area of forest habitat, Oppt represents the recreation opportunities, Pop is the proximity to population centers, and Road stands for the distance to major roads. To simplify interpretation, we rescaled the original forest recreation score (ranging from 0 to 5,200) to a range of 0–100, with 0 representing no forest recreation service and 100 representing highest service. | ABSTRACT: "Animals can be important in nutrient cycling in particular ecosystems, but few studies have examined how this importance varies along environmental gradients. In this study we quantified the nutrient cycling role of an abundant detritivorous fish species, the gizzard shad (Dorosoma cepedianum), in reservoir ecosystems along a gradient of ecosystem productivity. Gizzard shad feed mostly on sediment detritus and excrete sediment-derived nutrients into the water column, thereby mediating a cross-habitat translocation of nutrients to phytoplankton. We quantified nitrogen and phosphorus cycling (excretion) rates of gizzard shad, as well as nutrient demand by phytoplankton, in seven lakes over a four-year period (16 lake-years). The lakes span a gradient of watershed land use (the relative amounts of land used for agriculture vs. forest) and productivity. As the watersheds of these lakes became increasingly dominated by agricultural land, primary production rates, lake trophic state indicators (total phosphorus and chlorophyll concentrations), and nutrient flux through gizzard shad populations all increased. Nutrient cycling by gizzard shad supported a substantial proportion of primary production in these ecosystems, and this proportion increased as watershed agriculture (and ecosystem productivity) increased. In the four productive lakes with agricultural watersheds (.78% agricultural land), gizzard shad supported on average 51% of phytoplankton primary production (range 27–67%). In contrast, in the three relatively unproductive lakes in forested or mixed-land-use watersheds (.47% forest, ,52% agricultural land), gizzard shad supported 18% of primary production (range 14–23%). Thus, along a gradient of forested to agricultural landscapes, both watershed nutrient inputs and nutrient translocation by gizzard shad increase, but our data indicate that the importance of nutrient translocation by gizzard shad increases more rapidly. Our results therefore support the hypothesis that watersheds and gizzard shad jointly regulate primary production in reservoir 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. " | ABSTRACT: "Conservation organizations and land trusts in North Carolina are increasingly focused on how their work can contribute to both human and ecosystem resilience and adaptation to climate change, as well as directly mitigate climate change through carbon storage and sequestration. Recent state executive and legislative actions also underscore the importance of natural systems for climate adaptation and mitigation, and may provide additional funding for conservation and restoration for those purposes in the near term. To make it more efficient for conservation organizations working in North Carolina to consider a broad suite of conservation benefits in their work, the Conservation Trust for North Carolina and the Nicholas Institute for Energy, Environment & Sustainability at Duke University have developed two online tools for identifying priority areas for conservation action and estimating benefit metrics for specific properties. The conservation prioritization tool finds the sub-watersheds in North Carolina with the greatest potential to provide a set of user-selected conservation benefits. It allows users to identify priority areas for future conservation work within the entire state or a defined region. This high-level tool allows for quick and easy exploration without the need for spatial analysis expertise." |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified | None reported | Allows users to prioritize HUCs within their area of interest based on their conservation goals. |
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Biophysical Context
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Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak. | No additional description provided | No additional description provided | Lakes | 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 | No scenarios presented | No scenarios presented | Lake productivity | N/A | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-133 | EM-419 | EM-654 |
EM-668
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EM-843 | EM-972 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only |
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New or Pre-existing EM?
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New or revised model | New or revised model | New or revised 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-133 | EM-419 | EM-654 |
EM-668
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EM-843 | EM-972 |
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Document ID for related EM
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Doc-248 | Doc-303 | Doc-305 | None | None | Doc-405 |
Doc-444 ?Comment:The secondary source, document 444, is the website for running the tool. |
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EM ID for related EM
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EM-130 | EM-132 | None | None | None | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-844 | EM-845 | EM-846 | EM-847 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-133 | EM-419 | EM-654 |
EM-668
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EM-843 | EM-972 |
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EM Temporal Extent
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Not reported | 1992-2006 | 2000-2006 | 2000-2003 | 2008 | Not applicable |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-133 | EM-419 | EM-654 |
EM-668
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EM-843 | EM-972 |
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Bounding Type
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Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Not applicable |
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Spatial Extent Name
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Municipality of Etropole | Puget Sound Region | Yahara Watershed, Wisconsin | Lakes in Ohio | Piedmont Ecoregion | Not applicable |
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Spatial Extent Area (Magnitude)
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100-1000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | Not applicable |
Spatial Distribution of Computations
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EM ID
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EM-133 | EM-419 | EM-654 |
EM-668
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EM-843 | EM-972 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:Distributed by land cover and soil type polygons |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable | map scale, for cartographic feature |
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Spatial Grain Size
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Distributed by irregular land cover and soil type polygons | 200m x 200m | 30m x 30m | Not applicable | Not applicable | HUC 12 |
EM Structure and Computation Approach
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EM ID
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EM-133 | EM-419 | EM-654 |
EM-668
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EM-843 | EM-972 |
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EM Computational Approach
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Analytic | Analytic | Analytic | Numeric | Analytic | Other or unclear (comment) |
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EM Determinism
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deterministic | stochastic | 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-133 | EM-419 | EM-654 |
EM-668
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EM-843 | EM-972 |
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Model Calibration Reported?
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No | No | No |
Yes ?Comment:Nitrogen and Phosphorus excretion rates were calibrated by lake and fish size class. |
Yes | Not applicable |
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Model Goodness of Fit Reported?
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No | No | No | No | No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None | None | None | None | None |
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Model Operational Validation Reported?
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No | No | No | No | No | Not applicable |
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Model Uncertainty Analysis Reported?
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No | No | No | No | No | Not applicable |
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Model Sensitivity Analysis Reported?
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No | No | No | No | Yes | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Unclear | 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-133 | EM-419 | EM-654 |
EM-668
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EM-843 | EM-972 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-133 | EM-419 | EM-654 |
EM-668
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EM-843 | EM-972 |
| None |
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None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-133 | EM-419 | EM-654 |
EM-668
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EM-843 | EM-972 |
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Centroid Latitude
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42.8 | 48 | 43.1 | 40.15 | 36.23 | Not applicable |
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Centroid Longitude
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24 | -123 | -89.4 | -82.95 | -81.9 | Not applicable |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable |
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Centroid Coordinates Status
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Estimated | Estimated | Provided | Estimated | Estimated | Not applicable |
Environments and Scales Modeled
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EM ID
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EM-133 | EM-419 | EM-654 |
EM-668
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EM-843 | EM-972 |
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EM Environmental Sub-Class
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Rivers and Streams | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Lakes and Ponds | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Lakes and Ponds | Grasslands | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Mountainous flood-prone region | Terrestrial environment surrounding a large estuary | Mixed environment watershed of prairie converted to predominantly agriculture and urban landscape | Reservoirs | grasslands | Terrestrial and freshwater aquatic |
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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 corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is coarser 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-133 | EM-419 | EM-654 |
EM-668
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EM-843 | EM-972 |
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EM Organismal Scale
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Not applicable | Not applicable | Not applicable | Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-133 | EM-419 | EM-654 |
EM-668
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EM-843 | EM-972 |
| 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-133 | EM-419 | EM-654 |
EM-668
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EM-843 | EM-972 |
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None |
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None |
<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-133 | EM-419 | EM-654 |
EM-668
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EM-843 | EM-972 |
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None | None |
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None |