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-99 | EM-319 | EM-630 | EM-845 | EM-941 |
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EM Short Name
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Landscape importance for crops, Europe | Redfish and cold water coral (EFH), Norway | WaterWorld v2, Santa Basin, Peru | Red-winged blackbird abun, Piedmont region, USA | ESTIMAP - Pollination potential, Iran |
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EM Full Name
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Landscape importance for crop-based production, Europe | Linkage between redfish and cold water coral, Norway (essential fish habitat model) | WaterWorld v2, Santa Basin, Peru | Red-winged blackbird abundance, Piedmont ecoregion, USA | ESTIMAP - Pollination potential, Iran |
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EM Source or Collection
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EU Biodiversity Action 5 | None | None | None | None |
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EM Source Document ID
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228 | 259 | 368 | 405 | 434 |
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Document Author
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Haines-Young, R., Potschin, M. and Kienast, F. | Foley N.S., Kahui V.K., Armstrong C.W., Van Rensburg T.M | Van Soesbergen, A. and M. Mulligan | Riffel, S., Scognamillo, D., and L. W. Burger | Rahimi, E., Barghjelveh, S., and P. Dong |
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Document Year
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2012 | 2010 | 2018 | 2008 | 2020 |
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Document Title
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Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Estimating linkages between redfish and cold water coral on the Norwegian coast | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran) |
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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-99 | EM-319 | EM-630 | EM-845 | EM-941 |
| Not applicable | Not applicable | www.policysupport.org/waterworld | Not applicable | Not applicable | |
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Contact Name
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Marion Potschin | Naomi S. Foley | Arnout van Soesbergen | Sam Riffell | Ehsan Rahini |
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Contact Address
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Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Dept. of Economics and Management, Univeristy of Tromso, Norway | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran |
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Contact Email
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marion.potschin@nottingham.ac.uk | naomifoley@gmail.com | arnout.van_soesbergen@kcl.ac.uk | sriffell@cfr.msstate.edu | ehsanrahimi666@gmail.com |
EM Description
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EM ID
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EM-99 | EM-319 | EM-630 | EM-845 | EM-941 |
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Summary Description
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ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Crop-based production” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain." AUTHOR'S DESCRIPTION: "The analysis for "Crop-based production" maps all the areas that are important for food crops produced through commercial agriculture." | ABSTRACT: "…This paper applies the production function approach to estimate the link between cold water corals and redfish in Norway. Both the carrying capacity and growth rate of redfish are found to be functions of cold water coral habitat and thus cold water corals can be considered an essential fish habitat…The essential habitat model shows the best fit to the data…" AUTHOR'S DESCRIPTION: "…the EFH model presented by Barbier and Strand (1998), in which the habitat is considered essential to the stock; i.e., if the habitat declines to zero the fish stock will perish…based on the Gordon-Schaefer model, which is a single-species biomass model, where effort is the control variable and fish stock is the state variable. In the case of habitat-fisheries interactions, such as in our case, a second state variable is introduced, the habitat (CWC)…Scientists have stimated that 30-50% of CWC habitat has been damaged (Fossa, Mortensen, and Furevik 2002. Working within these bounds, we empirically estimate the relationship between CWC as a habitat and a fish stock..." | ABSTRACT: "Water resources in the Santa basin in the Peruvian Andes are increasingly under pressure from climate change and population increases. Impacts of temperature-driven glacier retreat on stream flow are better studied than those from precipitation changes, yet present and future water resources are mostly dependent on precipitation which is more difficult to predict with climate models. This study combines a broad range of projections from climate models with a hydrological model (WaterWorld), showing a general trend towards an increase in water availability due to precipitation increases over the basin. However, high uncertainties in these projections necessitate the need for basin-wide policies aimed at increased adaptability." AUTHOR'S DESCRIPTION: "WaterWorld is a fully distributed, process-based hydrological model that utilises remotely sensed and globally available datasets to support hydrological analysis and decision-making at national and local scales globally, with a particular focus on un-gauged and/or data-poor environments, which makes it highly suited to this study. The model (version 2) currently runs on either 10 degree tiles, large river basins or countries at 1-km2 resolution or 1 degree tiles at 1-ha resolution utilising different datasets. It simulates a hydrological baseline as a mean for the period 1950-2000 and can be used to calculate the hydrological impact of scenarios of climate change, land use change, land management options, impacts of extractives (oil & gas and mining) and impacts of changes in population and demography as well as combinations of these. The model is ‘self parameterising’ (Mulligan, 2013a) in the sense that all data required for model application anywhere in the world is provided with the model, removing a key barrier to model application. However, if users have better data than those provided, it is possible to upload these to WaterWorld as GIS files and use them instead. Results can be viewed visually within the web browser or downloaded as GIS maps. The model’s equations and processes are described in more detail in Mulligan and Burke (2005) and Mulligan (2013b). The model parameters are not routinely calibrated to observed flows as it is designed for hydrological scenario analysis in which the physical basis of its parameters must be retained and the model is also often used in un-gauged basins. Calibration is inappropriate under these circumstances (Sivapalan et al., 2003). The freely available nature of the model means that anyone can apply it and replicate the results shown here. WaterWorld’s (V2) snow and ice module is capable of simulating the processes of melt water production, snow fall and snow pack, making this version highly suited to the current application. The model component is based on a full energy-balance for snow accumulation and melting based on Walter et al., (2005) with input data provided globally by the SimTerra database (Mulligan, 2011) upon which the model r | 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: ". ..we used the ESTIMAP model to improve the results of the Lonsdorf model. For this, we included the effects of roads, railways, rivers, wetlands, lakes, altitude, climate, and ecosystem boundaries in the ESTIMAP modeling and compared the results with the Lonsdorf model. The results of the Lonsdorf model showed that the majority of Iran had a very low potential for providing pollination service and only three percent of the northern and western parts of Iran had high potential. However, the results of the ESTIMAP model showed that 16% of Iran had a high potential to provide pollination that covers most of the northern and southern parts of the country. The results of the ESTIMAP model for pollination mapping in Iran showed the Lonsdorf model of estimating pollination service can be improved through considering other relevant factors." |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None reported | None reported |
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Biophysical Context
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No additional description provided | Continental slope | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | Conservation Reserve Program lands left to go fallow | None additional |
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EM Scenario Drivers
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No scenarios presented | Estimated impact differences due to fishing effort; minimum (30%), and maximum (50%) degredation (reduction) in coral reef area. | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | N/A | N/A |
EM Relationship to Other EMs or Applications
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EM ID
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EM-99 | EM-319 | EM-630 | EM-845 | EM-941 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application (multiple runs exist) | 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 | Application of existing 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-99 | EM-319 | EM-630 | EM-845 | EM-941 |
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Document ID for related EM
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Doc-231 | Doc-228 | Doc-227 | None | Doc-405 | Doc-432 |
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EM ID for related EM
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EM-119 | EM-120 | EM-121 | EM-162 | EM-164 | EM-165 | EM-122 | EM-123 | EM-124 | EM-125 | EM-166 | EM-170 | EM-171 | EM-106 | None | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-843 | EM-844 | EM-846 | EM-847 | EM-939 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-99 | EM-319 | EM-630 | EM-845 | EM-941 |
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EM Temporal Extent
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2000 | 1986-2002 | 1950-2071 | 2008 | 2020 |
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EM Time Dependence
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time-stationary | time-stationary | time-dependent | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | both | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | discrete | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | 1 | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Month | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-99 | EM-319 | EM-630 | EM-845 | EM-941 |
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Bounding Type
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Geopolitical | Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical |
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Spatial Extent Name
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The EU-25 plus Switzerland and Norway | Norwegian Sea (ICES areas I and II) | Santa Basin | Piedmont Ecoregion | Iran |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 1000-10,000 km^2. | 10,000-100,000 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-99 | EM-319 | EM-630 | EM-845 | EM-941 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
spatially distributed (in at least some cases) ?Comment:Varies by inputs, but results are for areas of country |
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Spatial Grain Type
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area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature |
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Spatial Grain Size
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1 km x 1 km | Not applicable | 1 km2 | Not applicable | ha^2 |
EM Structure and Computation Approach
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EM ID
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EM-99 | EM-319 | EM-630 | EM-845 | EM-941 |
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EM Computational Approach
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Logic- or rule-based | Analytic | * | Analytic | Numeric |
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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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None |
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Model Checking Procedures Used
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EM ID
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EM-99 | EM-319 | EM-630 | EM-845 | EM-941 |
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Model Calibration Reported?
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No | Yes | No | Yes | No |
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Model Goodness of Fit Reported?
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No | Yes | 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 |
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Model Operational Validation Reported?
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Yes | No | Yes | No | No |
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Model Uncertainty Analysis Reported?
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No | No | No | No | No |
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Model Sensitivity Analysis Reported?
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No | Yes | No | Yes | No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Yes | 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-99 | EM-319 | EM-630 | EM-845 | EM-941 |
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None | None |
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Comment:Model for Iran - no form preset id for country |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-99 | EM-319 | EM-630 | EM-845 | EM-941 |
| None |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-99 | EM-319 | EM-630 | EM-845 | EM-941 |
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Centroid Latitude
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50.53 | 70 | -9.05 | 36.23 | 32.29 |
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Centroid Longitude
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7.6 | 10 | -77.81 | -81.9 | 53.68 |
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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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Estimated | Estimated | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-99 | EM-319 | EM-630 | EM-845 | EM-941 |
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EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Open Ocean and Seas | None | Grasslands | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Not applicable | cold water coral reefs | tropical, coastal to montane | grasslands | terrestrial land types |
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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 | Other or unclear (comment) | 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-99 | EM-319 | EM-630 | EM-845 | EM-941 |
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EM Organismal Scale
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Not applicable | Guild or Assemblage | Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-99 | EM-319 | EM-630 | EM-845 | EM-941 |
| None Available |
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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)
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| EM-99 | EM-319 | EM-630 | EM-845 | EM-941 |
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None |
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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-99 | EM-319 | EM-630 | EM-845 | EM-941 |
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None |
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