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-449 |
EM-593 
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EM-629 | EM-939 | EM-1001 |
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EM Short Name
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Decrease in erosion (shoreline), St. Croix, USVI | DayCent N2O flux simulation, Ireland | SolVES, Pike & San Isabel NF, WY | ESTIMAP- Recreation, Europe | NBS benefits explorer |
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EM Full Name
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Decrease in erosion (shoreline) by reef, St. Croix, USVI | DayCent simulation N2O flux and climate change, Ireland | SolVES, Social Values for Ecosystem Services, Pike and San Isabel National Forest, CO | ESTIMAP- Recreation, Europe | Benefit Accounting of Nature-Based Solutions for Watersheds: Guide |
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EM Source or Collection
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US EPA | None | None | None | None |
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EM Source Document ID
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335 | 358 | 369 | 432 | 471 |
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Document Author
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Yee, S. H., Dittmar, J. A., and L. M. Oliver | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | Zulian, G., Parrachini, M.L., Maes, J., | Brill, G., T. Shiao, C. Kammeyer, S. Diringer, K. Vigerstol, N. Ofosu-Amaah, M. Matosich, C. Müller-Zantop, W. Larson and T. Dekker |
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Document Year
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2014 | 2010 | 2014 | 2013 | 2022 |
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Document Title
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Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | 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 | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | ESTIMAP: Ecosystem services mapping at the European scale | Benefit Accounting of Nature-Based Solutions for Watersheds: Guide |
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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 report | Published report |
Software and Access
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EM ID
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EM-449 |
EM-593
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EM-629 | EM-939 | EM-1001 |
| Not applicable | Not applicable | Not applicable | N.A. | https://nbsbenefitsexplorer.net/tool | |
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Contact Name
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Susan H. Yee | M. Abdalla | Benson Sherrouse | Grazia Zulian | Gregg Brill |
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Contact Address
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US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | Joint Research Centre, Via Enrico Fermi 2749, TP 272, 21027 Ispra (VA), Italy | Not reported |
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Contact Email
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yee.susan@epa.gov | abdallm@tcd.ie | bcsherrouse@usgs.gov | grazia.zulian@jrc.ec.europa.e | Not reported |
EM Description
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EM ID
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EM-449 |
EM-593
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EM-629 | EM-939 | EM-1001 |
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Summary Description
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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." | 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: " "Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders.With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems. | AUTHOR Descriptions: "ESTIMAP consists of a set of separate components, each of which can be run separately. The models have been all framed in the ecosystem services cascade model [4] which connects ecosystem structure and functioning to human well-being through the flow of ecosystem services. At present, three modules are operational and described in further detail in this report: pollination, recreation and coastal protectionPeople can benefit from the opportunities provided by nature for recreational activities if they are able to reach them. The Recreation Opportunity spectrum was chosen as a method to map different degrees of service available according to their proximity to the people. Remoteness and proximity have been addressed in the second step of the analysis, in order to assess how the benefit (recreation) can be delivered to people. The proxy that has been identified couples information on both variables and has been mapped by classifying the EU into zones of proximity versus remoteness. From the ROS perspective this part takes into account remoteness and to some extent expected social experience. Distance from roads and residential areas have been used as inputs. The information on the road network is provided by the TeleAtlas database, and covers all paved roads in Europe. Gravel roads have been discarded to ease the processing. Residential areas are extracted from CORINE land cover classes “continuous urban fabric” and “discontinuous urban fabric”, therefore, all urban patches larger than 25 ha are considered in the mapping. In the current exercise there was the necessity to adapt overseas experiences to the peculiarities of the European continent, especially considering that the EU does not contain large wilderness areas like other continents " | Watersheds around the world are in peril and risk further decline from climate change and human impacts, like pollution, degrading landscapes, and unsustainable water use. These impacts can inhibit the ability of ecosystems to regulate water flows, sequester carbon to reduce atmospheric greenhouse gas levels, maintain biodiversity and healthy waterways, promote social well-being, offer economic opportunities, and sustain agricultural productivity. Climate change is exacerbating these impacts by shifting weather and precipitation patterns, degrading habitats, and increasing the recurrence and severity of natural disasters. Urgent action is needed to address these impacts by implementing nature-based solutions (NBS). NBS protect, sustainably manage, and restore natural or modified watersheds, to address societal challenges effectively and adaptively, simultaneously providing human well-being and biodiversity benefits (IUCN, 2016). Investment in NBS offers a mechanism to restore degraded watersheds and protect intact ones, leading to improved water quality and quantity, improved carbon sequestration and increased biodiversity, among many other social and economic benefits. NBS also support climate mitigation and adaptation efforts and reduce the impacts from other shocks, such as floods, droughts, and extreme weather events. Implementing NBS can also help advance progress toward achieving the United Nations Sustainable Development Goals (SDGs), particularly SDG 2 (zero hunger), SDG 6 (water), SDG 11 (sustainable cities and communities), SDG 13 (climate action), and SDG 15 (life on land). NBS therefore support social, economic and environmental objectives, and may be particularly important in supporting vulnerable communities. |
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Specific Policy or Decision Context Cited
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None identified | climate change | None | None | None identified |
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Biophysical Context
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No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Rocky mountain conifer forests | Continential Scale | NA |
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EM Scenario Drivers
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No scenarios presented | air temperature, precipitation, Atmospheric CO2 concentrations | N/A | N.A. | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-449 |
EM-593
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EM-629 | EM-939 | EM-1001 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method Only |
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New or Pre-existing EM?
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Application of existing model | Application of existing model | New or revised model | New or revised 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-449 |
EM-593
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EM-629 | EM-939 | EM-1001 |
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Document ID for related EM
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Doc-335 | None | Doc-369 | None | None |
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EM ID for related EM
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EM-447 | EM-448 | EM-598 | EM-626 | EM-628 | EM-941 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-449 |
EM-593
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EM-629 | EM-939 | EM-1001 |
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EM Temporal Extent
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2006-2007, 2010 | 1961-1990 | 2004-2008 | Not applicable | Not applicable |
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EM Time Dependence
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time-stationary | time-dependent | time-stationary | Not applicable | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | both | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | discrete | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Day | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-449 |
EM-593
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EM-629 | EM-939 | EM-1001 |
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Bounding Type
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Physiographic or ecological | Point or points | Geopolitical | No location (no locational reference given) | Not applicable |
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Spatial Extent Name
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Coastal zone surrounding St. Croix | Oak Park Research centre | National Park | Not applicable | Not applicable |
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Spatial Extent Area (Magnitude)
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100-1000 km^2 | 1-10 ha | 1000-10,000 km^2. | >1,000,000 km^2 | Not applicable |
Spatial Distribution of Computations
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EM ID
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EM-449 |
EM-593
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EM-629 | EM-939 | EM-1001 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all 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 | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable |
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Spatial Grain Size
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10 m x 10 m | Not applicable | 30m2 | Pixel size | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-449 |
EM-593
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EM-629 | EM-939 | EM-1001 |
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EM Computational Approach
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Analytic | Numeric | Numeric | Numeric | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-449 |
EM-593
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EM-629 | EM-939 | EM-1001 |
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Model Calibration Reported?
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Yes | No | No | No | Not applicable |
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Model Goodness of Fit Reported?
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No |
Yes ?Comment:for N2O fluxes |
Yes | Not applicable | Not applicable |
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Goodness of Fit (metric| value | unit)
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None |
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None | None |
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Model Operational Validation Reported?
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Yes | Yes | No | Unclear | Unclear |
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Model Uncertainty Analysis Reported?
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No | No | No | No | Not applicable |
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Model Sensitivity Analysis Reported?
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No | No | No | Yes | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Unclear | 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-449 |
EM-593
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EM-629 | EM-939 | EM-1001 |
| None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-449 |
EM-593
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EM-629 | EM-939 | EM-1001 |
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None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-449 |
EM-593
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EM-629 | EM-939 | EM-1001 |
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Centroid Latitude
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17.73 | 52.86 | 38.7 | Not applicable | Not applicable |
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Centroid Longitude
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-64.77 | 6.54 | 105.89 | Not applicable | Not applicable |
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Centroid Datum
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WGS84 | None provided | WGS84 | Not applicable | Not applicable |
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Centroid Coordinates Status
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Estimated | Provided | Estimated | Not applicable | Not applicable |
Environments and Scales Modeled
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EM ID
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EM-449 |
EM-593
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EM-629 | EM-939 | EM-1001 |
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EM Environmental Sub-Class
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Near Coastal Marine and Estuarine | Agroecosystems | Forests | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland |
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Specific Environment Type
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Coral reefs | farm pasture | Montain forest | Not applicable | None |
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EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer 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 |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-449 |
EM-593
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EM-629 | EM-939 | EM-1001 |
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EM Organismal Scale
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-449 |
EM-593
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EM-629 | EM-939 | EM-1001 |
| None Available | None Available | None Available | None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
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| EM-449 |
EM-593
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EM-629 | EM-939 | EM-1001 |
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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-449 |
EM-593
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EM-629 | EM-939 | EM-1001 |
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None | None |