EcoService Models Library (ESML)
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Compare EMs
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.
EM Identity and Description
EM ID
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EM-70 | EM-630 | EM-653 |
EM Short Name
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Plant species diversity, Central French Alps | WaterWorld v2, Santa Basin, Peru | Natural amenities and population migration, USA |
EM Full Name
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Plant species diversity, Central French Alps | WaterWorld v2, Santa Basin, Peru | Natural amenities and rural population migration, USA |
EM Source or Collection
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EU Biodiversity Action 5 | None | USDA Forest Service |
EM Source Document ID
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260 | 368 | 375 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Van Soesbergen, A. and M. Mulligan | Cordell H. K., V. Heboyan, F. Santos, J. C. Bergstrom |
Document Year
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2011 | 2018 | 2011 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | Natural amenities and rural population migration |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published report |
EM ID
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EM-70 | EM-630 | EM-653 |
Not applicable | www.policysupport.org/waterworld | Not applicable | |
Contact Name
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Sandra Lavorel | Arnout van Soesbergen | Ken Cordell |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | U.S. Department of Agriculture, Forest Service, Southern Research Station, Athens, GA 30602 |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | arnout.van_soesbergen@kcl.ac.uk | Not reported |
EM ID
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EM-70 | EM-630 | EM-653 |
Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Simpson species diversity was modelled using the LU + abiotic [land use and all abiotic variables] model given that functional diversity should be a consequence of species diversity rather than the reverse (Lepsˇ et al. 2006)…Species diversity for each pixel was calculated and mapped using model estimates for effects of land use types, and for regression coefficients on abiotic variables. For each pixel these calculations were applied to mapped estimates of abiotic variables." | 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: "Research suggests that significant relationships exist between rural population change and natural amenities. Thus, understanding and predicting domestic migration trends as a function of changes in natural amenities is important for effective regional growth and development policies and strategies. In this study, we first estimated an econometric model which showed the effects of natural amenities, such as climate and landscape variables, on rural population migration patterns in the United States between 1990 and 2007. The estimated model was then used to predict the effects of changes in these variables on rural county net migration and population growth to 2060 under alternative future climate and land use projections. Results suggest that people prefer rural areas with mild winters and cooler summers; thus we can expect a direct impact of climate change on population migration when areas associated with these conditions change. Results also suggest preference for varied landscapes that feature a mix of forest land and open space (e g , pasture and range land). During the projection period from 2010 to 2060 in the United States, changes in natural amenities were predicted to have positive effects on rural population migration trends in most parts of the Intermountain and Pacific Northwest regions, and some parts of the Southeastern, South Central, and Northeastern U S regions (e g , Southern Appalachian Mountains, Ozark Mountains, northern New England). Changes in natural amenities were predicted to have negative effects on rural population migration trends during the projection period in Midwestern regions (e g , Great Plains and North Central regions)." AUTHOR'S DESCRIPTION: "This model was estimated for 2,014 rural counties in the continental United States using various national data bases and sources. The estimated model was then used to predict the effects of changes in these variables on rural county net migration and population growth to 2060 under alternative future climate and land use projections." |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | No additional description provided |
EM Scenario Drivers
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No scenarios presented | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | Climate projections based on the CGCM 3 1 general circulation model of moderate warming (IPCC). The A1B scenario assumes a growing world population that peaks in the mid-century and balanced technological growth. |
EM ID
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EM-70 | EM-630 | EM-653 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) | Method + Application |
New or Pre-existing EM?
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New or revised model | Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-70 | EM-630 | EM-653 |
Document ID for related EM
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Doc-260 | None | None |
EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | None |
EM Modeling Approach
EM ID
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EM-70 | EM-630 | EM-653 |
EM Temporal Extent
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2007-2009 | 1950-2071 | 1982-2060 |
EM Time Dependence
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time-stationary | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | both | future time |
EM Time Continuity
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Not applicable | discrete | discrete |
EM Temporal Grain Size Value
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Not applicable | 1 | 1 |
EM Temporal Grain Size Unit
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Not applicable | Month | Year |
EM ID
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EM-70 | EM-630 | EM-653 |
Bounding Type
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Physiographic or Ecological | Watershed/Catchment/HUC | Geopolitical |
Spatial Extent Name
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Central French Alps | Santa Basin | continental United States |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 10,000-100,000 km^2 | >1,000,000 km^2 |
EM ID
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EM-70 | EM-630 | EM-653 |
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) |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | map scale, for cartographic feature |
Spatial Grain Size
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20 m x 20 m | 1 km2 | varies |
EM ID
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EM-70 | EM-630 | EM-653 |
EM Computational Approach
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Analytic | * | Numeric |
EM Determinism
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deterministic | deterministic | deterministic |
Statistical Estimation of EM
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None |
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EM ID
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EM-70 | EM-630 | EM-653 |
Model Calibration Reported?
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No | No | Yes |
Model Goodness of Fit Reported?
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Yes | No | No |
Goodness of Fit (metric| value | unit)
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None | None |
Model Operational Validation Reported?
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No | Yes | No |
Model Uncertainty Analysis Reported?
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No | No | No |
Model Sensitivity Analysis Reported?
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No | No | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-70 | EM-630 | EM-653 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-70 | EM-630 | EM-653 |
None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-70 | EM-630 | EM-653 |
Centroid Latitude
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45.05 | -9.05 | 39.8 |
Centroid Longitude
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6.4 | -77.81 | -98.55 |
Centroid Datum
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WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Provided | Estimated | Estimated |
EM ID
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EM-70 | EM-630 | EM-653 |
EM Environmental Sub-Class
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Agroecosystems | Grasslands | None | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren |
Specific Environment Type
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Subalpine terraces, grasslands, and meadows | tropical, coastal to montane | Terrestrial environments including water bodies and coastlines |
EM Ecological Scale
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Not applicable | Other or unclear (comment) | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-70 | EM-630 | EM-653 |
EM Organismal Scale
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Community | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-70 | EM-630 | EM-653 |
None Available | None Available | None Available |
EnviroAtlas URL
EM-70 | EM-630 | EM-653 |
None Available | Average Annual Precipitation | Dasymetric Allocation of Population, GAP Ecological Systems, Average Annual Precipitation, Total Employment, Employment Rate |
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-70 | EM-630 | EM-653 |
None | 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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-70 | EM-630 | EM-653 |
None | None | None |