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-70 | EM-79 | EM-374 |
EM-593 
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EM-1017 |
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EM Short Name
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Plant species diversity, Central French Alps | Divergence in flowering date, Central French Alps | InVEST carbon storage and sequestration (v3.2.0) | DayCent N2O flux simulation, Ireland | N-SPECT, Sediment and runoff, Isfahan, Iran |
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EM Full Name
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Plant species diversity, Central French Alps | Functional divergence in flowering date, Central French Alps | InVEST v3.2.0 Carbon storage and sequestration | DayCent simulation N2O flux and climate change, Ireland | Investigation of runoff and sediment yield using N-SPECT model in Pelasjan (Eskandari), Isfahan, Iran |
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EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | InVEST | None | None |
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EM Source Document ID
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260 | 260 | 315 | 358 | 480 |
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Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | The Natural Capital Project | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Khalili, S., Jamali, A.A., Hasanzadeh, M. and Morovvati, A., |
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Document Year
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2011 | 2011 | 2015 | 2010 | 2015 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Carbon storage and sequestration - InVEST (v3.2.0) | Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture | Investigation of runoff and sediment yield using N-SPECT model in Pelasjan (Eskandari), Isfahan, 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 | Website | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-70 | EM-79 | EM-374 |
EM-593
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EM-1017 |
| Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | https://coast.noaa.gov/digitalcoast/tools/qnspect.html | |
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Contact Name
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Sandra Lavorel | Sandra Lavorel | The Natural Capital Project | M. Abdalla | Ali Akbar Jamali |
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Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | 371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Department of Watershed MGT, Maybod Branch, Islamic Azad University, Maybod, Iran |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | invest@naturalcapitalproject.org | abdallm@tcd.ie | jamaliaa@maybodiau.ac.ir |
EM Description
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EM ID
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EM-70 | EM-79 | EM-374 |
EM-593
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EM-1017 |
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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: "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. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Functional divergence of flowering date was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. ABSTRACT: "Terrestrial ecosystems, which store more carbon than the atmosphere, are vital to influencing carbon dioxide-driven climate change. The InVEST model uses maps of land use and land cover types and data on wood harvest rates, harvested product degradation rates, and stocks in four carbon pools (aboveground biomass, belowground biomass, soil, dead organic matter) to estimate the amount of carbon currently stored in a landscape or the amount of carbon sequestered over time. Additional data on the market or social value of sequestered carbon and its annual rate of change, and a discount rate can be used in an optional model that estimates the value of this environmental service to society. Limitations of the model include an oversimplified carbon cycle, an assumed linear change in carbon sequestration over time, and potentially inaccurate discounting rates." AUTHOR'S DESCRIPTION: "A fifth optional pool included in the model applies to parcels that produce harvested wood products (HWPs) such as firewood or charcoal or more long-lived products such as house timbers or furniture. Tracking carbon in this pool is useful because it represents the amount of carbon kept from the atmosphere by a given product." | Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios. | Identifying and quantifying the runoff and Sediment yield are the necessary measures in the issues of soil erosion in a watershed. Pelasjan watershed located in West of Isfahan and it is one of the sub basins of Zayanderud which is taken as the study area. In this study the amount of runoff and Sediment yield has been evaluated using the Nonpoint-Source Pollution and Erosion Comparison Tools (N-SPECT) model which is an extension to ArcGIS software. The input layer maps in the GIS environment, including land use, the rain erosion, vegetation, soil erodibility, contour map and watershed boundary map were prepared. By entering the input data and running N-SPECT model, runoff and Sediment yield raster maps of the study area were obtained. To evaluate the model and data comparing, the values obtained from the model and the actual data values of runoff and Sediment yield were converted to the eigenvalues. Special amount of runoff from the model equals 1483 m3/ha/year and the actual runoff is equivalent to 1253 m3/ha/year for 21 water years ,from 1991 to 2012. From the values obtained by the model and the actual data it can be concluded that the model is sufficiently accurate for estimating runoff since the actual runoff value and the value obtained from the model are close to each other and statistically, there is no significant difference between them during this 21 water year. In relation to a Sediment yield, the amount obtained from the model was 7.8 ton/ha/year and the average amount of Sediment yield for 21 water years is 2.1 ton/ha/year, which by comparing with the values obtained for Sediment yield it can be concluded that the model overestimates about three times from the actual amount and there is a significant difference between the real data and data obtained by model so the model has not been very successful in Sediment yield estimating. One of the advantages of this model for estimating runoff and Sediment yield is point to point estimation of runoff and Sediment yield in output maps of the region. This model is particularly recommended for harsh and difficult access regions of the watershed. |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified | climate change | None provided |
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Biophysical Context
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Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Not applicable | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Pelasjan watershed, Zagros mountain range |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | Optional future scenarios for changed LULC and wood harvest | air temperature, precipitation, Atmospheric CO2 concentrations | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-70 | EM-79 | EM-374 |
EM-593
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EM-1017 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application |
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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 | Application of existing 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-70 | EM-79 | EM-374 |
EM-593
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EM-1017 |
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Document ID for related EM
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Doc-260 | Doc-260 | Doc-269 | Doc-309 | None | Doc-473 |
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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 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83 | EM-349 | EM-598 | EM-1007 | EM-1003 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-70 | EM-79 | EM-374 |
EM-593
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EM-1017 |
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EM Temporal Extent
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2007-2009 | 2007-2008 | Not applicable | 1961-1990 | 1991-2012 |
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EM Time Dependence
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time-stationary | time-stationary | time-dependent | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | future time | both | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | discrete | discrete | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | 1 | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Year | Day | Not applicable |
EM Spatial Extent
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EM ID
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EM-70 | EM-79 | EM-374 |
EM-593
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EM-1017 |
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Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Not applicable | Point or points | Watershed/Catchment/HUC |
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Spatial Extent Name
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Central French Alps | Central French Alps | Not applicable | Oak Park Research centre | Pelasjan watershed |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | Not applicable | 1-10 ha | 1000-10,000 km^2. |
Spatial Distribution of Computations
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EM ID
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EM-70 | EM-79 | EM-374 |
EM-593
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EM-1017 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable |
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Spatial Grain Size
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20 m x 20 m | 20 m x 20 m | application specific | Not applicable | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-70 | EM-79 | EM-374 |
EM-593
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EM-1017 |
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EM Computational Approach
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Analytic | Analytic | Analytic | Numeric | Analytic |
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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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Model Checking Procedures Used
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EM ID
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EM-70 | EM-79 | EM-374 |
EM-593
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EM-1017 |
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Model Calibration Reported?
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No | No | Not applicable | No | Unclear |
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Model Goodness of Fit Reported?
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Yes | Yes | Not applicable |
Yes ?Comment:for N2O fluxes |
No |
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Goodness of Fit (metric| value | unit)
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None |
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None |
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Model Operational Validation Reported?
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No | No | Not applicable | Yes | Unclear |
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Model Uncertainty Analysis Reported?
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No | No | Not applicable | No | Unclear |
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Model Sensitivity Analysis Reported?
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No | No | Not applicable | No | Unclear |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| EM-70 | EM-79 | EM-374 |
EM-593
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EM-1017 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-70 | EM-79 | EM-374 |
EM-593
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EM-1017 |
| None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-70 | EM-79 | EM-374 |
EM-593
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EM-1017 |
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Centroid Latitude
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45.05 | 45.05 | -9999 | 52.86 | 32.26 |
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Centroid Longitude
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6.4 | 6.4 | -9999 | 6.54 | 50.22 |
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Centroid Datum
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WGS84 | WGS84 | Not applicable | None provided | WGS84 |
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Centroid Coordinates Status
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Provided | Provided | Not applicable | Provided | Provided |
Environments and Scales Modeled
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EM ID
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EM-70 | EM-79 | EM-374 |
EM-593
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EM-1017 |
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Not applicable | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows | Terrestrial environments, but not specified for methods | farm pasture | Desert mountains watershed |
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EM Ecological Scale
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Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to 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-70 | EM-79 | EM-374 |
EM-593
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EM-1017 |
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EM Organismal Scale
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Community | Community | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-70 | EM-79 | EM-374 |
EM-593
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EM-1017 |
| None Available | None Available | None Available | None Available | 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)
em.detail.cicesHelp
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| EM-70 | EM-79 | EM-374 |
EM-593
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EM-1017 |
| 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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-70 | EM-79 | EM-374 |
EM-593
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EM-1017 |
| None | None | None |
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None |