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-122 ![]() |
EM-466 |
EM Short Name
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Land-use change and crop-based production, Europe | Yasso 15 - soil carbon model |
EM Full Name
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Land-use change effects on crop-based production, Europe | Yasso 15 - soil carbon |
EM Source or Collection
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EU Biodiversity Action 5 | None |
EM Source Document ID
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228 |
342 ?Comment:Webpage pdf users manual for model. |
Document Author
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Haines-Young, R., Potschin, M. and Kienast, F. | Repo, A., Jarvenpaa, M., Kollin, J., Rasinmaki, J. and Liski, J. |
Document Year
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2012 | 2016 |
Document Title
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Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Yasso 15 graphical user-interface manual |
Document Status
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Peer reviewed and published | Other or unclear (explain in Comment) |
Comments on Status
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Published journal manuscript | Not applicable |
EM ID
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EM-122 ![]() |
EM-466 |
Not applicable |
http://en.ilmatieteenlaitos.fi/yasso-download-and-support ?Comment:User's manual states that the software will be downloadable at this site. |
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Contact Name
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Marion Potschin | Jari Liski |
Contact Address
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Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki |
Contact Email
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marion.potschin@nottingham.ac.uk | jari.liski@ymparisto.fi |
EM ID
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EM-122 ![]() |
EM-466 |
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 novel aspect of this work is an analysis of whether the historical and the projected land use changes for the periods 1990–2000, 2000–2006, and 2000–2030 are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Crop-based production); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000 and 2000–2006 (LEAC, EEA, 2006) and EURURALIS 2.0 land use scenarios for 2000–2030. The results are reported at three spatial reporting units, i.e. (1) the NUTS-X regions, (2) the bioclimatic regions, and (3) the dominant landscape types." AUTHOR'S DESCRIPTION: "The analysis for “Crop-based production” maps all the areas that are important for food crops produced through commercial agriculture….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers…The futures component of the work was based on EURURALIS 2.0 land use scenarios for 2000–2030, which are based on the four IPCC SRES land use scenarios." | AUTHOR'S DESCRIPTION: "The Yasso15 calculates the stock of soil organic carbon, changes in the stock of soil organic carbon and heterotrophic soil respiration. Applications the model include, for example, simulations of land use change, ecosystem management, climate change, greenhouse gas inventories and education. The Yasso15 is a relatively simple soil organic carbon model requiring information only on climate and soil carbon input to operate... In the Yasso15 model litter is divided into five soil organic carbon compound groups (Fig. 1). These groups are compounds hydrolysable in acid (denoted with A), compounds soluble in water (W) or in a non-polar solvent, e.g. ethanol or dichloromethane (E), compounds neither soluble nor hydrolysable (N) and humus (H). The AWEN form the group of labile fractions whereas H fraction contains humus, which is more recalcitrant to decomposition. Decomposition of the fractions results in carbon flux out of soil and carbon fluxes between the compartments (Fig. 1). The basic idea of Yasso15 is that the decomposition of different types of soil carbon input depends on the chemical composition of the input types and climate conditions. The effects of the chemical composition are taken into account by dividing carbon input to soil between the four labile compartments explicitly according to the chemical composition (Fig. 1). Decomposition of woody litter depends additionally on the size of the litter. The effects of climate conditions are modelled by adjusting the decomposition rates of the compartments according to air temperature and precipitation. In the Yasso15 model separate decomposition rates are applied to fast-decomposing A, W and E compartments, more slowly decomposing N and very slowly decomposing humus compartment H. The Yasso is a global-level model meaning that the same parameter values are suitable for all applications for accurate predictions. However, the current GUI version also includes possibility to use earlier parameterizations. The parameter values of Yasso15 are based on measurements related to cycling of organic carbon in soil (Table 1). An extensive set of litter decomposition measurements was fundamental in developing the model (Fig. 2). This data set covered, firstly, most of the global climate conditions in terms of temperature precipitation and seasonality (Fig 3.), secondly, different ecosystem types from forests to grasslands and agricultural fields and, thirdly, a wide range of litter types. In addition, a large set of data giving information on decomposition of woody litter (including branches, stems, trunks, roots with different size classes) was used for fitting. In addition to woody and non-woody litter decomposition measurements, a data set on accumulation of soil carbon on the Finnish coast and a large, global steady state data sets were used in the parameterization of the model. These two data sets contain information on the formation and slow decomposition of humus." |
Specific Policy or Decision Context Cited
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None identified | None identified |
Biophysical Context
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No additional description provided | Not applicable |
EM Scenario Drivers
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Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use changes (2000-2030) | No scenarios presented |
EM ID
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EM-122 ![]() |
EM-466 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method Only |
New or Pre-existing EM?
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New or revised 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-122 ![]() |
EM-466 |
Document ID for related EM
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Doc-238 | Doc-239 | Doc-240 | Doc-241 | Doc-242 | Doc-228 | Doc-343 | Doc-344 |
EM ID for related EM
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EM-123 | EM-124 | EM-125 | EM-162 | EM-164 | EM-165 | EM-166 | EM-170 | EM-171 | EM-99 | EM-119 | EM-120 | EM-121 | EM-467 | EM-469 | EM-480 | EM-485 |
EM Modeling Approach
EM ID
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EM-122 ![]() |
EM-466 |
EM Temporal Extent
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1990-2030 | Not applicable |
EM Time Dependence
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time-dependent | time-dependent |
EM Time Reference (Future/Past)
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future time | Not applicable |
EM Time Continuity
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discrete | discrete |
EM Temporal Grain Size Value
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6, 10, and 30 | 1 |
EM Temporal Grain Size Unit
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Year | Year |
EM ID
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EM-122 ![]() |
EM-466 |
Bounding Type
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Geopolitical | Not applicable |
Spatial Extent Name
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The EU-25 plus Switzerland and Norway | Not applicable |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | Not applicable |
EM ID
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EM-122 ![]() |
EM-466 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all cases) |
Spatial Grain Type
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area, for pixel or radial feature | Not applicable |
Spatial Grain Size
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1 km x 1 km | Not applicable |
EM ID
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EM-122 ![]() |
EM-466 |
EM Computational Approach
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Logic- or rule-based | Numeric |
EM Determinism
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deterministic | stochastic |
Statistical Estimation of EM
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EM ID
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EM-122 ![]() |
EM-466 |
Model Calibration Reported?
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No | Not applicable |
Model Goodness of Fit Reported?
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No | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None |
Model Operational Validation Reported?
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No | Not applicable |
Model Uncertainty Analysis Reported?
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No | Not applicable |
Model Sensitivity Analysis Reported?
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No | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-122 ![]() |
EM-466 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-122 ![]() |
EM-466 |
None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-122 ![]() |
EM-466 |
Centroid Latitude
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50.53 | Not applicable |
Centroid Longitude
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7.6 | Not applicable |
Centroid Datum
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WGS84 | Not applicable |
Centroid Coordinates Status
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Estimated | Not applicable |
EM ID
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EM-122 ![]() |
EM-466 |
EM Environmental Sub-Class
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Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Forests | Grasslands | Scrubland/Shrubland | Tundra |
Specific Environment Type
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Not applicable | Not applicable |
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 |
Scale of differentiation of organisms modeled
EM ID
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EM-122 ![]() |
EM-466 |
EM Organismal Scale
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Not applicable | Species |
Taxonomic level and name of organisms or groups identified
EM-122 ![]() |
EM-466 |
None Available | None Available |
EnviroAtlas URL
EM-122 ![]() |
EM-466 |
Hectares of Vegetable Crops | Average Annual Precipitation, Carbon storage by tree biomass (kg/m2), Carbon Storage by Tree Biomass |
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-122 ![]() |
EM-466 |
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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-122 ![]() |
EM-466 |
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None |