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-68 |
EM-98 
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EM-154 |
EM-369
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EM-838 | EM-985 |
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EM Short Name
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Fodder crude protein content, Central French Alps | PATCH, western USA | Mangrove development, Tampa Bay, FL, USA | Envision, Puget Sound, WA, USA | Eastern meadowlark abundance, Piedmont region, USA | Atlantis ecosystem assessment submodel |
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EM Full Name
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Fodder crude protein content, Central French Alps | PATCH (Program to Assist in Tracking Critical Habitat), western USA | Mangrove wetland development, Tampa Bay, FL, USA | Envision, Puget Sound, WA, USA | Eastern meadowlark abundance, Piedmont ecoregion, USA | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
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EM Source or Collection
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EU Biodiversity Action 5 | US EPA | US EPA | Envision | None | None |
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EM Source Document ID
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260 | 2 | 97 |
313 ?Comment:Doc 314 is a secondary source. It is a webpage guide intended to provide support for developing an application using ENVISION. |
405 | 463 |
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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. | Carroll, C, Phillips, M. K. , Lopez-Gonzales, C. A and Schumaker, N. H. | Osland, M. J., Spivak, A. C., Nestlerode, J. A., Lessmann, J. M., Almario, A. E., Heitmuller, P. T., Russell, M. J., Krauss, K. W., Alvarez, F., Dantin, D. D., Harvey, J. E., From, A. S., Cormier, N. and Stagg, C.L. | Bolte, J. and Vache, K. | Riffel, S., Scognamillo, D., and L. W. Burger | Fulton, E.A., Link, J.S., Kaplan, I.C., Savina‐Rolland, M., Johnson, P., Ainsworth, C., Horne, P., Gorton, R., Gamble, R.J., Smith, A.D. and Smith, D.C. |
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Document Year
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2011 | 2006 | 2012 | 2010 | 2008 | 2011 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Defining recovery goals and strategies for endangered species: The wolf as a case study | Ecosystem development after mangrove wetland creation: plant–soil change across a 20-year chronosequence | Envisioning Puget Sound Alternative Futures: PSNERP Final Report | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Documentation is 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 journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-68 |
EM-98
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EM-154 |
EM-369
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EM-838 | EM-985 |
| Not applicable | Not applicable | Not applicable | http://envision.bioe.orst.edu | Not applicable | https://noaa-fisheries-integrated-toolbox.github.io/Atlantis | |
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Contact Name
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Sandra Lavorel | Carlos Carroll | Michael Osland |
John Bolte ?Comment:Phone# 541-737-2041 |
Sam Riffell | Elizabeth Fulton |
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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 | Klamath Center for Conservation Research, Orleans, CA 95556 | U.S. Environmental Protection Agency, Gulf Ecology Division, gulf Breeze, FL 32561 | Oregon State University, Dept. of Biological & Ecological Engineering, 116C Gilmore Hall, Corvallis, OR 97333 | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | CSIRO Wealth from Oceans Flagship, Division of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas. 7001, Australia |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | carlos@cklamathconservation.org | mosland@usgs.gov | boltej@engr.orst.edu | sriffell@cfr.msstate.edu | beth.fulton@csiro.au |
EM Description
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EM ID
em.detail.idHelp
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EM-68 |
EM-98
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EM-154 |
EM-369
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EM-838 | EM-985 |
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Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., fodder crude protein content), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in fodder crude protein content was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy…Fodder crude protein for each pixel was calculated and mapped using model estimates...This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on fodder protein content. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use." | **Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** AUTHORS' DESCRIPTION: "PATCH (program to assist in tracking critical habitat), the SEPM used here, is designed for studying territorial vertebrates. It links the survival and fecundity of individual animals to geographic information system (GIS) data on mortality risk and habitat productivity at the scale of an individual or pack territory. Territories are allocated by intersecting the GIS data with an array of hexagonal cells. The different habitat types in the GIS maps are assigned weights based on the relative levels of fecundity and survival expected in those habitat classes. Base survival and reproductive rates, derived from published field studies, are then supplied to the model as a population projection matrix. The model scales these base matrix values using the mean of the habitat weights within each hexagon, with lower means translating into lower survival rates or reproductive output. Each individual in the population is tracked through a yearly cycle of survival, fecundity, and dispersal events. Environmental stochasticity is incorporated by drawing each year’s base population matrix from a randomized set of matrices whose elements were drawn from a beta (survival) or normal (fecundity) distribution. Adult organisms are classified as either territorial or floaters. The movement of territorial individuals is governed by a parameter for site fidelity, but floaters must always search for available breeding sites. As pack size increases, pack members in the model have a greater tendency to disperse and search for new available breeding sites. Movement decisions use a directed random walk that combines varying proportions of randomness, correlation, and attraction to higher-quality habitat (Schumaker 1998)." | ABSTRACT: "Mangrove wetland restoration and creation effortsare increasingly proposed as mechanisms to compensate for mangrove wetland losses. However, ecosystem development and functional equivalence in restored and created mangrove wetlands are poorly understood. We compared a 20-year chronosequence of created tidal wetland sites in Tampa Bay, Florida (USA) to natural reference mangrove wetlands. Across the chronosequence, our sites represent the succession from salt marsh to mangrove forest communities. Our results identify important soil and plant structural differences between the created and natural reference wetland sites; however, they also depict a positive developmental trajectory for the created wetland sites that reflects tightly coupled plant-soil development. Because upland soils and/or dredge spoils were used to create the new mangrove habitats, the soils at younger created sites and at lower depths (10–30 cm) had higher bulk densities, higher sand content, lower soil organic matter (SOM), lower total carbon (TC), and lower total nitrogen (TN) than did natural reference wetland soils. However, in the upper soil layer (0–10 cm), SOM, TC, and TN increased with created wetland site age simultaneously with mangrove forest growth. The rate of created wetland soil C accumulation was comparable to literature values for natural mangrove wetlands. Notably, the time to equivalence for the upper soil layer of created mangrove wetlands appears to be faster than for many other wetland ecosystem types. Collectively, our findings characterize the rate and trajectory of above- and below-ground changes associated with ecosystem development in created mangrove wetlands; this is valuable information for environmental managers planning to sustain existing mangrove wetlands or mitigate for mangrove wetland losses." | SUMMARY: "...the Puget Sound Nearshore Ecosystem Restoration Project, completed an analysis of alternative future regional trajectories of landscape change for the Puget Sound region. This effort developed three scenarios of change: 1) Status Quo, reflecting a continuation of current trends in the region, 2) Managed Growth, reflecting the adoption of an aggressive set of land use management policies focusing on protecting and restoring ecosystem function and concentrating growth within Urban Growth Areas (UGA) and near regional growth centers, and 3) Unmanaged Growth, reflecting a relaxation of land use restrictions with limited protection of ecosystem functions. Analyses assumed a fixed population growth rate across all three scenarios, defined by the Washington Office of Financial Management county level growth estimates. Scenarios were generated using a spatially- and temporally-explicit alternative futures analysis model, Envision, previously developed by Oregon State University researchers. The model accepts as input a vector-based representation of the landscape and associated datasets describing relevant landscape characteristics, descriptors of various processes influencing landscape change, and a set of policies, or decision alternatives, which reflect scenario-specific land management alternatives. The model generates 1) a set of spatial coverages (maps) reflecting scenario outcomes of a variety of landscape variables, most notably land use/land cover, shoreline modifications, and population projections, and 2) a set of summary statistics describing landscape change variables summarized across spatial reporting units. Analyses were run on each of such sub-basins in the Puget Sound, and aggregated to providing Sound-wide results. This information is being used by PSNERP to project future impairment of ecosystem functions, goods, and services. The Puget Sound Nearshore Ecosystem project data also provide inputs to calculate aspects of future nearshore process degradation. Impairment and degradation are primary factors being used to define future conditions for the PSNERP General Investigation Study." AUTHOR'S DESCRIPTION: "In this report, we document the application of an alternative futures analysis framework that incorporates these capabilities to the analysis of alternative future trajectories in the Puget Sound region. This framework, Envision (Bolte et al, 2007; Hulse et al. 2008) is a spatially and temporally explicit, standards-based, open source toolset specifically designed to facilitate alternative futures analyses. It employs a multiagent-based modeling approach that contains a robust capability for defining alternative management strategies and scenarios, incorporating a variety of landscape change processes, and creating maps of alternative landscape trajectories, expressed though a variety of metrics defined in an application-specific way." ABOUT ENVISION (ENVISION WEBSITE): "Central to Envision, and conceived at the s | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. " | Models are key tools for integrating a wide range of system information in a common framework. Attempts to model exploited marine ecosystems can increase understanding of system dynamics; identify major processes, drivers and responses; highlight major gaps in knowledge; and provide a mechanism to ‘road test’ management strategies before implementing them in reality. The Atlantis modelling framework has been used in these roles for a decade and is regularly being modified and applied to new questions (e.g. it is being coupled to climate, biophysical and economic models to help consider climate change impacts, monitoring schemes and multiple use management). This study describes some common lessons learned from its implementation, particularly in regard to when these tools are most effective and the likely form of best practices for ecosystem-based management (EBM). Most importantly, it highlighted that no single management lever is sufficient to address the many trade-offs associated with EBM and that the mix of measures needed to successfully implement EBM will differ between systems and will change through time. Although it is doubtful that any single management action will be based solely on Atlantis, this modelling approach continues to provide important insights for managers when making natural resource management decisions. |
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Specific Policy or Decision Context Cited
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None identified | AUTHOR DESCRIPTION: "Comprehensive habitat and viability assessments. . . [more rigoursly defined] can clarify debate of goals for recovery of large carnivores"; Endangered Species Act and related litigation | Not applicable | None identified | None reported | None identified |
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Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominantely south-facing slopes | Great Plains to Pacific Coast, northern Rocky Mountains, Pacific Northwest | mangrove forest,Salt marsh, estuary, sea level, | No additional description provided | Conservation Reserve Program lands left to go fallow | N/A |
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EM Scenario Drivers
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No scenarios presented | Population growth, road development (density) on public vs private land | Not applicable | Alternative future land management strategies (status quo, managed growth, unmanaged growth) | N/A | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-68 |
EM-98
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EM-154 |
EM-369
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EM-838 | EM-985 |
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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 + Application (multiple runs exist) View EM Runs | Method + Application | Method Only |
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New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-68 |
EM-98
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EM-154 |
EM-369
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EM-838 | EM-985 |
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Document ID for related EM
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Doc-260 | Doc-269 | Doc-328 | Doc-337 | None |
Doc-314 | Doc-47 ?Comment:Doc 314 is a secondary source. It is a webpage guide intended to provide support for developing an application using ENVISION. |
Doc-405 | Doc-456 | Doc-459 | Doc-461 |
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EM ID for related EM
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EM-65 | EM-66 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-403 | EM-422 | None | EM-12 | EM-333 | EM-831 | EM-841 | EM-842 | EM-843 | EM-844 | EM-845 | EM-846 | EM-847 | EM-978 | EM-981 | EM-983 | EM-990 | EM-991 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-68 |
EM-98
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EM-154 |
EM-369
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EM-838 | EM-985 |
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EM Temporal Extent
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2007-2009 | 2000-2025 | 1990-2010 | 2000-2060 | 2008 | Not applicable |
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EM Time Dependence
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time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | future time | future time | future time | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | discrete | continuous | discrete | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable | 1 | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Year | Not applicable | Year | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-68 |
EM-98
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EM-154 |
EM-369
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EM-838 | EM-985 |
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Bounding Type
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Physiographic or Ecological | Physiographic or ecological | Physiographic or Ecological | Watershed/Catchment/HUC | Physiographic or ecological | Not applicable |
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Spatial Extent Name
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Central French Alps | Western United States | Tampa Bay | Puget Sound watershed | Piedmont Ecoregion | Not applicable |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | 100,000-1,000,000 km^2 | Not applicable |
Spatial Distribution of Computations
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EM ID
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EM-68 |
EM-98
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EM-154 |
EM-369
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EM-838 | EM-985 |
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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) | Not applicable |
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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 | Irregular | Not applicable | Not applicable |
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Spatial Grain Size
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20 m x 20 m | 504 km^2 | m^2 | Varies | Not applicable | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-68 |
EM-98
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EM-154 |
EM-369
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EM-838 | EM-985 |
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EM Computational Approach
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Analytic | Numeric | Analytic | Numeric | Analytic | Analytic |
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EM Determinism
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deterministic | stochastic | 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-68 |
EM-98
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EM-154 |
EM-369
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EM-838 | EM-985 |
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Model Calibration Reported?
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No | Unclear | No | Unclear | Yes | Not applicable |
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Model Goodness of Fit Reported?
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Yes | No | No | Not applicable | No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None | None | None | None |
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Model Operational Validation Reported?
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Yes | No | No | Not applicable | No | Not applicable |
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Model Uncertainty Analysis Reported?
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No | No | Yes | Not applicable | No | Not applicable |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No |
Yes ?Comment:No results reported. Just a general statement was made about PATCH sensitivity and that demographic parameters are more sensitive that variation in other parameters such as dispersadistance . Reference made to another publication Carroll et al. 2003. Use of population viability analysis and reserve slelection algorithms in regional conservation plans. Ecol. App. 13:1773-1789. |
Yes | Not applicable | Yes | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Unclear | No | 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-68 |
EM-98
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EM-154 |
EM-369
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EM-838 | EM-985 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-68 |
EM-98
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EM-154 |
EM-369
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EM-838 | EM-985 |
| None | None |
Comment:Realm: Tropical Atlantic Region: West Tropical Atlantic Province: Tropical Northwestern Atlantic Ecoregion: Floridian |
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None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-68 |
EM-98
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EM-154 |
EM-369
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EM-838 | EM-985 |
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Centroid Latitude
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45.05 | 39.88 | 27.8 | 47.58 | 36.23 | Not applicable |
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Centroid Longitude
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6.4 | -113.81 | -82.4 | -122.32 | -81.9 | Not applicable |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable |
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Centroid Coordinates Status
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Provided | Estimated | Estimated | Estimated | Estimated | Not applicable |
Environments and Scales Modeled
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EM ID
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EM-68 |
EM-98
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EM-154 |
EM-369
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EM-838 | EM-985 |
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Grasslands | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Not reported | Created Mangrove wetlands | Pacific NW US region, coastal to montane, urban to rural | grasslands | Multiple |
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EM Ecological Scale
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Not applicable | 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 | Ecological scale corresponds to 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-68 |
EM-98
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EM-154 |
EM-369
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EM-838 | EM-985 |
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EM Organismal Scale
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Community | Species | Not applicable | Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-68 |
EM-98
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EM-154 |
EM-369
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EM-838 | EM-985 |
| None Available |
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None Available |
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None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
em.detail.cicesHelp
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| EM-68 |
EM-98
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EM-154 |
EM-369
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EM-838 | EM-985 |
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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-68 |
EM-98
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EM-154 |
EM-369
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EM-838 | EM-985 |
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None |
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None |