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-79 | EM-449 |
EM-467 
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EM-593
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EM-965
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EM Short Name
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Divergence in flowering date, Central French Alps | Decrease in erosion (shoreline), St. Croix, USVI | Yasso07 v1.0.1, Switzerland | DayCent N2O flux simulation, Ireland | Ecopath Model Narragansett Bay, USA |
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EM Full Name
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Functional divergence in flowering date, Central French Alps | Decrease in erosion (shoreline) by reef, St. Croix, USVI | Yasso07 v1.0.1 forest litter decomposition, Switzerland | DayCent simulation N2O flux and climate change, Ireland | Ecopath Model Narragansett Bay, USA |
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EM Source or Collection
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EU Biodiversity Action 5 | US EPA | None | None | None |
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EM Source Document ID
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260 | 335 | 343 | 358 | 449 |
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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. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Didion, M., B. Frey, N. Rogiers, and E. Thurig | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Anne Innes-Gold, Margaret Heinichen, Kelvin Gorospe, Corinne Truesdale, Jeremy Collie, Austin Humphries |
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Document Year
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2011 | 2014 | 2014 | 2010 | 2020 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Validating tree litter decomposition in the Yasso07 carbon model | 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 | Modeling 25 years of food web changes in Narragansett Bay (USA) as a tool for ecosystem-based management |
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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-79 | EM-449 |
EM-467
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EM-593
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EM-965
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| Not applicable | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | Not applicable | https://ecopath.org/downloads/ | |
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Contact Name
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Sandra Lavorel | Susan H. Yee |
Markus Didion ?Comment:Tel.: +41 44 7392 427 |
M. Abdalla | Austin Humphries |
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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 | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Department of Fisheries, 45 Upper College Rd, Kingston, RI 02881 |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | yee.susan@epa.gov | markus.didion@wsl.ch | abdallm@tcd.ie | humphries@uri.edu |
EM Description
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EM ID
em.detail.idHelp
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EM-79 | EM-449 |
EM-467
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EM-593
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EM-965
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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, 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." | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef." | ABSTRACT: "...We examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to (i) examine the effect of the application of three different published Yasso07 parameter sets on simulated decay rate; (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests;…" AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root litter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The result of the simulation was an annual estimate of the remaining fraction of the initial mass, which could then be compared with observed data." | 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. | Narragansett Bay (Rhode Island, USA) is an estuary undergoing changes from a combination of rising water temperatures, nutrient fluxes, and human uses. In this study, we created an ecosystem food web model and evaluated its ability to predict functional group biomasses. Specifically, we used Ecopath to construct 2 mass-balanced models covering different time periods in Narragansett Bay: a historical model using data from 1994-1998 and a present-day model that represents 2014-2018. With the historical model as a starting point, we used Ecosim fit to time series data and projected forward to present-day values, forcing the model with both phytoplankton biomass and fishing mortality. The biomass of most mid- and upper trophic level groups increased by 2018, with the exception of carnivorous benthos, which experienced a large decline. There were changes in the composition of fisheries, with a large increase in recreational benthivorous fish landings and a decrease in commercial landings of planktivorous fish and suspension feeding benthos. The inclusion of fishing mortality and phytoplankton biomass as forcing functions, as well as adjusting the vulnerability levels of prey, greatly improved our model fits for all functional groups with the exception of gelatinous zooplankton. Our ecosystem model was able to correctly predict the direction of change for all fish and fished invertebrate groups with a relatively high degree of precision, particularly for the upper trophic levels. Thus, this ecosystem model is broadly applicable and suitable to project trends in the Narragansett Bay food web associated with localized and adaptive ecosystem-based management. |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified | climate change | None identified |
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Biophysical Context
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Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | No additional description provided | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | NA |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented |
No scenarios presented ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
air temperature, precipitation, Atmospheric CO2 concentrations | 1) Time period from 1994 to 1998 and 2) time period from 2014 to 2018 |
EM Relationship to Other EMs or Applications
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EM ID
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EM-79 | EM-449 |
EM-467
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EM-593
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EM-965
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
Method + Application (multiple runs exist) View EM Runs |
Method + Application (multiple runs exist) View EM Runs ?Comment:Run 1: 1994-1998 |
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New or Pre-existing EM?
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New or revised model | Application of existing model | Application of existing model | 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-79 | EM-449 |
EM-467
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EM-593
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EM-965
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Document ID for related EM
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Doc-260 | Doc-269 | Doc-335 | Doc-342 | Doc-344 | None |
?Comment:Document 450 is an additional source for this EM. |
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EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83 | EM-447 | EM-448 | EM-466 | EM-469 | EM-480 | EM-485 | EM-598 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-79 | EM-449 |
EM-467
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EM-593
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EM-965
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EM Temporal Extent
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2007-2008 | 2006-2007, 2010 | 1993-2013 | 1961-1990 | 1994-1998 |
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EM Time Dependence
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time-stationary | time-stationary | time-dependent | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | future time | both | past time |
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EM Time Continuity
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Not applicable | Not applicable | discrete | discrete | discrete |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | 1 | 1 | 4 |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Year | Day | Year |
EM Spatial Extent
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EM ID
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EM-79 | EM-449 |
EM-467
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EM-593
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EM-965
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Bounding Type
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Physiographic or Ecological | Physiographic or ecological | Geopolitical | Point or points | Physiographic or ecological |
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Spatial Extent Name
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Central French Alps | Coastal zone surrounding St. Croix | Switzerland | Oak Park Research centre | Narragansett Bay |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | 1-10 ha | 100-1000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-79 | EM-449 |
EM-467
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EM-593
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EM-965
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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 | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable |
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Spatial Grain Size
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20 m x 20 m | 10 m x 10 m | 5 sites | Not applicable | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-79 | EM-449 |
EM-467
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EM-593
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EM-965
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EM Computational Approach
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Analytic | Analytic | Numeric | Numeric | Analytic |
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EM Determinism
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deterministic | deterministic | stochastic | 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-79 | EM-449 |
EM-467
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EM-593
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EM-965
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Model Calibration Reported?
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No | Yes | No | No | Unclear |
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Model Goodness of Fit Reported?
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Yes | No | No |
Yes ?Comment:for N2O fluxes |
No |
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Goodness of Fit (metric| value | unit)
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None | None |
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None |
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Model Operational Validation Reported?
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No | Yes | Yes | Yes | Unclear |
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Model Uncertainty Analysis Reported?
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No | No | No | No | Unclear |
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Model Sensitivity Analysis Reported?
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No | No | No | 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-79 | EM-449 |
EM-467
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EM-593
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EM-965
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None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-79 | EM-449 |
EM-467
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EM-593
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EM-965
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| None |
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None | None |
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Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-79 | EM-449 |
EM-467
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EM-593
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EM-965
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Centroid Latitude
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45.05 | 17.73 | 46.82 | 52.86 | 41.62 |
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Centroid Longitude
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6.4 | -64.77 | 8.23 | 6.54 | 71.35 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | None provided | WGS84 |
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Centroid Coordinates Status
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Provided | Estimated | Estimated | Provided | Estimated |
Environments and Scales Modeled
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EM ID
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EM-79 | EM-449 |
EM-467
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EM-593
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EM-965
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Near Coastal Marine and Estuarine |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Coral reefs | forests | farm pasture | Coastal bay |
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EM Ecological Scale
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Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of 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-79 | EM-449 |
EM-467
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EM-593
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EM-965
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EM Organismal Scale
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Community | Not applicable | Community | Not applicable | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-79 | EM-449 |
EM-467
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EM-593
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EM-965
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| None Available | None Available | None Available | None Available |
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EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
em.detail.cicesHelp
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| EM-79 | EM-449 |
EM-467
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EM-593
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EM-965
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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>
fegs1Help
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
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| EM-79 | EM-449 |
EM-467
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EM-593
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EM-965
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| None |
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None |
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