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-66 | EM-87 | EM-93 | EM-113 |
EM-593 
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EM Short Name
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Litter biomass production, Central French Alps | Area & hotspots of soil accumulation, South Africa | Stream nitrogen removal, Mississippi R. basin, USA | Wetland conservation for birds, Midwestern USA | DayCent N2O flux simulation, Ireland |
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EM Full Name
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Litter biomass production, Central French Alps | Area and hotspots of soil accumulation, South Africa | Stream nitrogen removal, Upper Mississippi, Ohio and Missouri River sub-basins, USA | Prioritizing wetland conservation for birds, Midwestern USA | DayCent simulation N2O flux and climate change, Ireland |
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EM Source or Collection
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EU Biodiversity Action 5 | None | US EPA | None | None |
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EM Source Document ID
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260 | 271 | 52 | 122 | 358 |
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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. | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Hill, B. and Bolgrien, D. | Thogmartin, W. A., Potter, B. A. and Soulliere, G. J. | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. |
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Document Year
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2011 | 2008 | 2011 | 2011 | 2010 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Mapping ecosystem services for planning and management | Nitrogen removal by streams and rivers of the Upper Mississippi River basin | Bridging the conservation design and delivery gap for wetland bird habitat maintenance and restoration in the midwestern United States | 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 |
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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-66 | EM-87 | EM-93 | EM-113 |
EM-593
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| Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | |
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Contact Name
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Sandra Lavorel | Benis Egoh | Brian Hill | Wayne Thogmartin, USGS | M. Abdalla |
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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 | Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | Mid-Continent Ecology Division NHEERL, ORD. USEPA 6201 Congdon Blvd. Duluth, MN 55804, USA | Upper Midwest Environmental Sciences Center, 2630 Fanta Reed Road, La Crosse, WI 54603 | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | Not reported | hill.brian@epa.gov | wthogmartin@usgs.gov | abdallm@tcd.ie |
EM Description
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EM ID
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EM-66 | EM-87 | EM-93 | EM-113 |
EM-593
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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. 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 (e.g., litter biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in litter biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy…Litter biomass production for each pixel was calculated and mapped using model estimates...This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on litter mass. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use." | AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil scientists often use soil depth to model soil production potential (soil formation) (Heimsath et al., 1997; Yuan et al., 2006). The accumulation of soil organic matter is an important process of soil formation which can be badly affected by habitat degradation and transformation (de Groot et al., 2002). Soil depth and leaf litter were used as proxies for soil accumulation. Soil depth is positively correlatedwith soil organic matter (Yuan et al., 2006); deep soils have the capacity to hold more nutrients. Litter cover was described above. Data on soil depth were obtained from the land capability map of South Africa and thresholds were based on the literature (Schoeman et al., 2002; Tekle, 2004). Areas with at least 0.4 m depth and 30% litter cover were mapped as important areas for soil accumulation, i.e. its geographic range. The hotspot was mapped as areas with at least 0.8 m depth and a 70% litter cover." | ABSTRACT: "We used stream chemistry and hydrogeomorphology data from 549 stream and 447 river sites to estimate NO3–N removal in the Upper Mississippi, Missouri, and Ohio Rivers. We used two N removal models to predict NO3–N input and removal. NO3–N input ranged from 0.01 to 338 kg/km*d in the Upper Mississippi River to 0.01–54 kg/ km*d in the Missouri River. Cumulative river network NO3–N input was 98700–101676 Mg/year in the Ohio River, 85,961–89,288 Mg/year in the Upper Mississippi River, and 59,463–61,541 Mg/year in the Missouri River. NO3–N output was highest in the Upper Mississippi River (0.01–329 kg/km*d ), followed by the Ohio and Missouri Rivers (0.01–236 kg/km*d ) sub-basins. Cumulative river network NO3–N output was 97,499 Mg/year for the Ohio River, 84,361 Mg/year for the Upper Mississippi River, and 59,200 Mg/year for the Missouri River. Proportional NO3–N removal (PNR) based on the two models ranged from 0.01 to 0.28. NO3–N removal was inversely correlated with stream order, and ranged from 0.01 to 8.57 kg/km*d in the Upper Mississippi River to 0.001–1.43 kg/km*d in the Missouri River. Cumulative river network NO3–N removal predicted by the two models was: Upper Mississippi River 4152 and 4152 Mg/year, Ohio River 3743 and 378 Mg/year, and Missouri River 2,277 and 197 Mg/year. PNR removal was negatively correlated with both stream order (r = −0.80–0.87) and the percent of the catchment in agriculture (r = −0.38–0.76)." | ABSTRACT: "The U.S. Fish and Wildlife Service’s adoption of Strategic Habitat Conservation is intended to increase the effectiveness and efficiency of conservation delivery by targeting effort in areas where biological benefits are greatest. Conservation funding has not often been allocated in accordance with explicit biological endpoints, and the gap between conservation design (the identification of conservation priority areas) and delivery needs to be bridged to better meet conservation goals for multiple species and landscapes. We introduce a regional prioritization scheme for North American Wetlands Conservation Act funding which explicitly addresses Midwest regional goals for wetland-dependent birds. We developed decision-support maps to guide conservation of breeding and non-breeding wetland bird habitat. This exercise suggested ~55% of the Midwest consists of potential wetland bird habitat, and areas suited for maintenance (protection) were distinguished from those most suited to restoration. Areas with greater maintenance focus were identified for central Minnesota, southeastern Wisconsin, the Upper Mississippi and Illinois rivers, and the shore of western Lake Erie and Saginaw Bay. The shores of Lakes Michigan and Superior accommodated fewer waterbird species overall, but were also important for wetland bird habitat maintenance. Abundant areas suited for wetland restoration occurred in agricultural regions of central Illinois, western Iowa, and northern Indiana and Ohio. Use of this prioritization scheme can increase effectiveness, efficiency, transparency, and credibility to land and water conservation efforts for wetland birds in the Midwestern United States." | 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. |
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Specific Policy or Decision Context Cited
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None identified | None identified | Not applicable | Strategic habitat conservation by USFW for Wetland Conservation Act funding | climate change |
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Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | Agricultural landuse , 1st-10th order streams | Boreal Hardwood Transition, Eastern Tallgrass Prairie, Prairie Hardwood Transition, Central Hardwoods | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | Not applicable | Conservation efforts for: marsh-wetland breeding birds, regional marsh and open-water for non-breeding birds, mudflat/shallows for birds during non-breeding period. | air temperature, precipitation, Atmospheric CO2 concentrations |
EM Relationship to Other EMs or Applications
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EM ID
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EM-66 | EM-87 | EM-93 | EM-113 |
EM-593
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs |
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New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | New or revised model | 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-66 | EM-87 | EM-93 | EM-113 |
EM-593
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Document ID for related EM
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Doc-260 | Doc-271 | Doc-154 | Doc-155 | Doc-169 | Doc-170 | Doc-171 | Doc-172 | Doc-173 | Doc-174 | Doc-175 | None |
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EM ID for related EM
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EM-65 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-85 | EM-86 | EM-88 | None | None | EM-598 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-66 | EM-87 | EM-93 | EM-113 |
EM-593
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EM Temporal Extent
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Not reported | Not reported | 2000-2008 | 2007 | 1961-1990 |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | both |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | discrete |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | 1 |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Day |
EM Spatial Extent
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EM ID
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EM-66 | EM-87 | EM-93 | EM-113 |
EM-593
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Bounding Type
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Physiographic or Ecological | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Point or points |
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Spatial Extent Name
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Central French Alps | South Africa | Upper Mississippi, Ohio and Missouri River sub-basins | Upper Mississippi River and Great Lakes Region | Oak Park Research centre |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 1-10 ha |
Spatial Distribution of Computations
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EM ID
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EM-66 | EM-87 | EM-93 | EM-113 |
EM-593
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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 distributed (in at least some cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | Not applicable |
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Spatial Grain Size
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20 m x 20 m | Distributed across catchments with average size of 65,000 ha | 1 km | 1 ha | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-66 | EM-87 | EM-93 | EM-113 |
EM-593
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EM Computational Approach
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Analytic | Analytic | 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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Model Checking Procedures Used
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EM ID
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EM-66 | EM-87 | EM-93 | EM-113 |
EM-593
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Model Calibration Reported?
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No | No | No | No | No |
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Model Goodness of Fit Reported?
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Yes | No | No | No |
Yes ?Comment:for N2O fluxes |
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Goodness of Fit (metric| value | unit)
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None | None | None |
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Model Operational Validation Reported?
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Yes | No | No | No | Yes |
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Model Uncertainty Analysis Reported?
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No | No | Yes | No | No |
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Model Sensitivity Analysis Reported?
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No | No | Unclear | No | No |
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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-66 | EM-87 | EM-93 | EM-113 |
EM-593
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-66 | EM-87 | EM-93 | EM-113 |
EM-593
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| None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-66 | EM-87 | EM-93 | EM-113 |
EM-593
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Centroid Latitude
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45.05 | -30 | 36.98 | 42.05 | 52.86 |
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Centroid Longitude
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6.4 | 25 | -89.13 | -88.6 | 6.54 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | None provided |
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Centroid Coordinates Status
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Provided | Estimated | Estimated | Estimated | Provided |
Environments and Scales Modeled
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EM ID
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EM-66 | EM-87 | EM-93 | EM-113 |
EM-593
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Agroecosystems |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Not applicable | Not applicable | Not reported | farm pasture |
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EM Ecological Scale
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Not applicable | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of 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-66 | EM-87 | EM-93 | EM-113 |
EM-593
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EM Organismal Scale
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Community | Not applicable | Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-66 | EM-87 | EM-93 | EM-113 |
EM-593
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| None Available | None Available | None Available |
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None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
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| EM-66 | EM-87 | EM-93 | EM-113 |
EM-593
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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-66 | EM-87 | EM-93 | EM-113 |
EM-593
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| None | None |
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