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-374 | EM-438 |
EM-467 
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EM-593
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EM-660
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EM Short Name
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Divergence in flowering date, Central French Alps | InVEST carbon storage and sequestration (v3.2.0) | InVESTv3.0 Nutrient retention, Guánica Bay | Yasso07 v1.0.1, Switzerland | DayCent N2O flux simulation, Ireland | RUM: Valuing fishing quality, Michigan, USA |
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EM Full Name
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Functional divergence in flowering date, Central French Alps | InVEST v3.2.0 Carbon storage and sequestration | InVEST (Integrated Valuation of Environmental Services and Tradeoffs)v3.0 Nutrient retention, Guánica Bay, Puerto Rico, USA | Yasso07 v1.0.1 forest litter decomposition, Switzerland | DayCent simulation N2O flux and climate change, Ireland | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA |
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EM Source or Collection
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EU Biodiversity Action 5 | InVEST | US EPA | InVEST | None | None | None |
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EM Source Document ID
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260 | 315 | 338 | 343 | 358 |
382 ?Comment:Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010. |
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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. | The Natural Capital Project | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Didion, M., B. Frey, N. Rogiers, and E. Thurig | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson |
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Document Year
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2011 | 2015 | 2017 | 2014 | 2010 | 2014 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Carbon storage and sequestration - InVEST (v3.2.0) | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | 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 | Valuing recreational fishing quality at rivers and streams |
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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 | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Website | 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-374 | EM-438 |
EM-467
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EM-593
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EM-660
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| Not applicable | https://www.naturalcapitalproject.org/invest/ | http://www.naturalcapitalproject.org/invest/ | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | Not applicable | Not applicable | |
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Contact Name
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Sandra Lavorel | The Natural Capital Project | Susan H. Yee |
Markus Didion ?Comment:Tel.: +41 44 7392 427 |
M. Abdalla | Richard Melstrom |
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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 | 371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | U.S. Environmental Protection Agency, 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 Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | invest@naturalcapitalproject.org | yee.susan@epa.gov | markus.didion@wsl.ch | abdallm@tcd.ie | melstrom@okstate.edu |
EM Description
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EM ID
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EM-79 | EM-374 | EM-438 |
EM-467
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EM-593
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EM-660
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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." | 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." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "Nutrient retention was estimated by first calculating water yield and establishing the quantity of nitrogen or phosphorus retained by different land cover classes using a water purification model (InVEST 3.0.0; Tallis et al., 2013). Different land cover classes were assumed to have different capacities for retaining nutrients, depending on the efficiency of vegetation in removing either nitrogen or phosphorus and the rates of nitrogen or phosphorus loading." “Use of other models in conjunction with this model:Average runoff per pixel modeled here were derived from the InVEST Water Yield model" | 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. | ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. " |
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Specific Policy or Decision Context Cited
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None identified | None identified | Improving water quality | 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 | Not applicable | 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 | stream and river reaches of Michigan |
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EM Scenario Drivers
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No scenarios presented | Optional future scenarios for changed LULC and wood harvest | 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 | targeted sport fish biomass |
EM Relationship to Other EMs or Applications
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EM ID
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EM-79 | EM-374 | EM-438 |
EM-467
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EM-593
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EM-660
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Method Only, Application of Method or Model Run
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Method + Application | Method Only | 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 |
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New or Pre-existing EM?
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New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | New or revised 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-374 | EM-438 |
EM-467
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EM-593
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EM-660
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Document ID for related EM
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Doc-260 | Doc-269 | Doc-309 | Doc-309 | Doc-205 | Doc-342 | Doc-344 | None | None |
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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-349 | EM-363 | EM-112 | 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-374 | EM-438 |
EM-467
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EM-593
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EM-660
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EM Temporal Extent
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2007-2008 | Not applicable | 1980 - 2013 | 1993-2013 | 1961-1990 | 2008-2010 |
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EM Time Dependence
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time-stationary | time-dependent | time-dependent | time-dependent | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | future time | other or unclear (comment) | future time | both | Not applicable |
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EM Time Continuity
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Not applicable | discrete | discrete | discrete | discrete | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | 1 | 1 | 1 | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Year | Year | Year | Day | Not applicable |
EM Spatial Extent
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EM ID
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EM-79 | EM-374 | EM-438 |
EM-467
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EM-593
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EM-660
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Bounding Type
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Physiographic or Ecological | Not applicable | Watershed/Catchment/HUC | Geopolitical | Point or points | Watershed/Catchment/HUC |
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Spatial Extent Name
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Central French Alps | Not applicable | Guanica Bay Study Area | Switzerland | Oak Park Research centre | HUCS in Michigan |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | Not applicable | 1000-10,000 km^2. | 10,000-100,000 km^2 | 1-10 ha | 100,000-1,000,000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-79 | EM-374 | EM-438 |
EM-467
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EM-593
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EM-660
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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) | spatially distributed (in at least some 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 | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) |
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Spatial Grain Size
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20 m x 20 m | application specific | 30 m x 30 m | 5 sites | Not applicable | reach in HUC |
EM Structure and Computation Approach
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EM ID
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EM-79 | EM-374 | EM-438 |
EM-467
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EM-593
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EM-660
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EM Computational Approach
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Analytic | Analytic | Numeric | Numeric | Numeric | Numeric |
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EM Determinism
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deterministic | 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-374 | EM-438 |
EM-467
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EM-593
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EM-660
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Model Calibration Reported?
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No | Not applicable | No | No | No | No |
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Model Goodness of Fit Reported?
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Yes | Not applicable | No | No |
Yes ?Comment:for N2O fluxes |
Yes |
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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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No | Not applicable | No | Yes | Yes | No |
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Model Uncertainty Analysis Reported?
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No | Not applicable | No | No | No | No |
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Model Sensitivity Analysis Reported?
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No | Not applicable | No | No | 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 | 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-374 | EM-438 |
EM-467
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EM-593
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EM-660
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-79 | EM-374 | EM-438 |
EM-467
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EM-593
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EM-660
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| None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-79 | EM-374 | EM-438 |
EM-467
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EM-593
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EM-660
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Centroid Latitude
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45.05 | -9999 | 17.97 | 46.82 | 52.86 | 45.12 |
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Centroid Longitude
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6.4 | -9999 | -66.93 | 8.23 | 6.54 | 85.18 |
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Centroid Datum
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WGS84 | Not applicable | WGS84 | WGS84 | None provided | WGS84 |
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Centroid Coordinates Status
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Provided | Not applicable | Estimated | Estimated | Provided | Estimated |
Environments and Scales Modeled
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EM ID
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EM-79 | EM-374 | EM-438 |
EM-467
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EM-593
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EM-660
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Not applicable | Aquatic Environment (sub-classes not fully specified) | Inland Wetlands | Near Coastal Marine and Estuarine | Open Ocean and Seas | Forests | Agroecosystems | Created Greenspace | Scrubland/Shrubland | Barren | Forests | Agroecosystems | Rivers and Streams |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Terrestrial environments, but not specified for methods | 13 LULC were used | forests | farm pasture | stream reaches |
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EM Ecological Scale
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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 | 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-374 | EM-438 |
EM-467
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EM-593
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EM-660
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EM Organismal Scale
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Community | Not applicable | Not applicable | Community | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-79 | EM-374 | EM-438 |
EM-467
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EM-593
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EM-660
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| None Available | 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-374 | EM-438 |
EM-467
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EM-593
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EM-660
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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-79 | EM-374 | EM-438 |
EM-467
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EM-593
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EM-660
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| None | None | None | None |
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