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-86 |
EM-98 
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EM-154 | EM-185 | EM-838 | EM-985 |
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EM Short Name
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Area and hotspots of soil retention, South Africa | PATCH, western USA | Mangrove development, Tampa Bay, FL, USA | Blue crabs and SAV, Chesapeake Bay, USA | Eastern meadowlark abundance, Piedmont region, USA | Atlantis ecosystem assessment submodel |
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EM Full Name
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Area and hotspots of soil retention, South Africa | PATCH (Program to Assist in Tracking Critical Habitat), western USA | Mangrove wetland development, Tampa Bay, FL, USA | Blue crabs and submerged aquatic vegetation interaction, Chesapeake Bay, 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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None | US EPA | US EPA | None | None | None |
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EM Source Document ID
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271 | 2 | 97 |
292 ?Comment:Conference paper |
405 | 463 |
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Document Author
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Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | 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. | Mykoniatis, N. and Ready, R. | 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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2008 | 2006 | 2012 | 2013 | 2008 | 2011 |
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Document Title
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Mapping ecosystem services for planning and management | 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 | Evaluating habitat-fishery interactions: The case of submerged aquatic vegetation and blue crab fishery in the Chesapeake Bay | 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 | Not formally documented | 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 | Conference proceedings | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-86 |
EM-98
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EM-154 | EM-185 | EM-838 | EM-985 |
| Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://noaa-fisheries-integrated-toolbox.github.io/Atlantis | |
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Contact Name
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Benis Egoh | Carlos Carroll | Michael Osland | Nikolaos Mykoniatis | Sam Riffell | Elizabeth Fulton |
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Contact Address
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Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | Klamath Center for Conservation Research, Orleans, CA 95556 | U.S. Environmental Protection Agency, Gulf Ecology Division, gulf Breeze, FL 32561 | Department of Agricultural Economics, Sociology and Education The Pennsylvania State University | 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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Not reported | carlos@cklamathconservation.org | mosland@usgs.gov | Not reported | sriffell@cfr.msstate.edu | beth.fulton@csiro.au |
EM Description
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EM ID
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EM-86 |
EM-98
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EM-154 | EM-185 | EM-838 | EM-985 |
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Summary Description
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AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil retention was modelled as a function of vegetation or litter cover and soil erosion potential. Schoeman et al. (2002) modelled soil erosion potential and derived eight erosion classes, ranging from low to severe erosion potential for South Africa. The vegetation cover was mapped by ranking vegetation types using expert knowledge of their ability to curb erosion. We used Schulze (2004) index of litter cover which estimates the soil surface covered by litter based on observations in a range of grasslands, woodlands and natural forests. According to Quinton et al. (1997) and Fowler and Rockstrom (2001) soil erosion is slightly reduced with about 30%, significantly reduced with about 70% vegetation cover. The range of soil retention was mapped by selecting all areas that had vegetation or litter cover of more than 30% for both the expert classified vegetation types and litter accumulation index within areas with moderate to severe erosion potential. The hotspot was mapped as areas with severe erosion potential and vegetation/litter cover of at least 70% where maintaining the cover is essential to prevent erosion. An assumption was made that the potential for this service is relatively low in areas with little natural vegetation or litter cover." | **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." | ABSTRACT: "This paper investigates habitat-fisheries interaction between two important resources in the Chesapeake Bay: blue crabs and Submerged Aquatic Vegetation (SAV). A habitat can be essential to a species (the species is driven to extinction without it), facultative (more habitat means more of the species, but species can exist at some level without any of the habitat) or irrelevant (more habitat is not associated with more of the species). An empirical bioeconomic model that nests the essential-habitat model into its facultative-habitat counterpart is estimated. Two alternative approaches are used to test whether SAV matters for the crab stock. Our results indicate that, if we do not have perfect information on habitat-fisheries linkages, the right approach would be to run the more general facultative-habitat model instead of the essential- habitat one." | 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 | Not applicable | None reported | None identified |
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Biophysical Context
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Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | Great Plains to Pacific Coast, northern Rocky Mountains, Pacific Northwest | mangrove forest,Salt marsh, estuary, sea level, | Submerged Aquatic Vegetation (SAV), eelgrass | 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 | Essential or Facultative habitat | N/A | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-86 |
EM-98
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EM-154 | EM-185 | 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 | 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-86 |
EM-98
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EM-154 | EM-185 | EM-838 | EM-985 |
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Document ID for related EM
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Doc-271 ?Comment:Document 273 used for source information on soil erosion potential variable |
Doc-328 | Doc-337 | None | Doc-227 | Doc-405 | Doc-456 | Doc-459 | Doc-461 |
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EM ID for related EM
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EM-85 | EM-87 | EM-88 | EM-403 | EM-422 | None | EM-106 | 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-86 |
EM-98
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EM-154 | EM-185 | EM-838 | EM-985 |
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EM Temporal Extent
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Not reported | 2000-2025 | 1990-2010 | 1993-2011 | 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 | past 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-86 |
EM-98
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EM-154 | EM-185 | EM-838 | EM-985 |
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Bounding Type
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Geopolitical | Physiographic or ecological | Physiographic or Ecological | Physiographic or ecological | Physiographic or ecological | Not applicable |
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Spatial Extent Name
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South Africa | Western United States | Tampa Bay | Chesapeake Bay | Piedmont Ecoregion | Not applicable |
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Spatial Extent Area (Magnitude)
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>1,000,000 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-86 |
EM-98
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EM-154 | EM-185 | 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 lumped (in all cases) | spatially lumped (in all cases) | Not applicable |
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Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable | Not applicable |
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Spatial Grain Size
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Distributed across catchments with average size of 65,000 ha | 504 km^2 | m^2 | Not applicable | Not applicable | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-86 |
EM-98
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EM-154 | EM-185 | EM-838 | EM-985 |
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EM Computational Approach
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Analytic | Numeric | Analytic | Analytic | 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-86 |
EM-98
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EM-154 | EM-185 | EM-838 | EM-985 |
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Model Calibration Reported?
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No | Unclear | No | Yes | Yes | Not applicable |
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Model Goodness of Fit Reported?
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No | No | No | Yes | No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None | None | None | None | None |
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Model Operational Validation Reported?
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No | No | No | Yes | No | Not applicable |
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Model Uncertainty Analysis Reported?
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No | No | Yes | Yes | No | Not applicable |
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Model Sensitivity Analysis Reported?
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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 | Yes | Yes | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Unclear | No | Yes | 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-86 |
EM-98
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EM-154 | EM-185 | EM-838 | EM-985 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-86 |
EM-98
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EM-154 | EM-185 | 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-86 |
EM-98
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EM-154 | EM-185 | EM-838 | EM-985 |
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Centroid Latitude
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-30 | 39.88 | 27.8 | 36.99 | 36.23 | Not applicable |
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Centroid Longitude
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25 | -113.81 | -82.4 | -75.95 | -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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Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable |
Environments and Scales Modeled
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EM ID
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EM-86 |
EM-98
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EM-154 | EM-185 | EM-838 | EM-985 |
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EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | None | 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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Not reported | Not reported | Created Mangrove wetlands | Yes | grasslands | Multiple |
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EM Ecological Scale
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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 | Yes | 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-86 |
EM-98
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EM-154 | EM-185 | EM-838 | EM-985 |
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EM Organismal Scale
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Not applicable | Species | Not applicable | Yes | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-86 |
EM-98
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EM-154 | EM-185 | EM-838 | EM-985 |
| None Available |
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None Available |
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None Available |
EnviroAtlas URL
| EM-86 |
EM-98
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EM-154 | EM-185 | EM-838 | EM-985 |
| None Available | Dasymetric Allocation of Population | None Available | None Available | GAP Ecological Systems, U.S. EPA (Omernik) ecoregions | Big game hunting recreation demand, Percent GAP Status 1 & 2, Total Employment |
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-86 |
EM-98
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EM-154 | EM-185 | 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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-86 |
EM-98
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EM-154 | EM-185 | EM-838 | EM-985 |
| None |
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None |
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None |