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-86 |
EM-98 
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EM-127
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EM Short Name
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Fodder crude protein content, Central French Alps | Area and hotspots of soil retention, South Africa | PATCH, western USA | Annual profit - carbon plantings, South Australia |
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EM Full Name
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Fodder crude protein content, Central French Alps | Area and hotspots of soil retention, South Africa | PATCH (Program to Assist in Tracking Critical Habitat), western USA | Annual profit from carbon plantings, South Australia |
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EM Source or Collection
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EU Biodiversity Action 5 | None | US EPA | None |
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EM Source Document ID
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260 | 271 | 2 | 243 |
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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. | 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. | Crossman, N. D., Bryan, B. A., and Summers, D. M. |
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Document Year
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2011 | 2008 | 2006 | 2011 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Mapping ecosystem services for planning and management | Defining recovery goals and strategies for endangered species: The wolf as a case study | Carbon payments and low-cost conservation |
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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 |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-68 | EM-86 |
EM-98
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EM-127
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| Not applicable | Not applicable | Not applicable | Not applicable | |
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Contact Name
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Sandra Lavorel | Benis Egoh | Carlos Carroll | Neville D. Crossman |
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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 | Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | Klamath Center for Conservation Research, Orleans, CA 95556 | CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | Not reported | carlos@cklamathconservation.org | neville.crossman@csiro.au |
EM Description
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EM ID
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EM-68 | EM-86 |
EM-98
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EM-127
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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." | 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: "A price on carbon is expected to generate demand for carbon offset schemes. This demand could drive investment in tree-based monocultures that provide higher carbon yields than diverse plantings of native tree and shrub species, which sequester less carbon but provide greater variation in vegetation structure and composition. Economic instruments such as species conservation banking, the creation and trading of credits that represent biological-diversity values on private land, could close the financial gap between monocultures and more diverse plantings by providing payments to individuals who plant diverse species in locations that contribute to conservation and restoration goals. We studied a highly modified agricultural system in southern Australia that is typical of many temperate agriculture zones globally (i.e., has a high proportion of endangered species, high levels of habitat fragmentation, and presence of non-native species). We quantified the economic returns...from carbon plantings (monoculture and mixed tree and shrubs) under six carbon-price scenarios." AUTHOR'S DESCRIPTION: "The economic returns of carbon plantings are highly variable and depend primarily on carbon yield and price and opportunity costs (Newell & Stavins 2000; Richards & Stokes 2004; Torres et al. 2010)...The spatial variation in carbon yield and costs, including establishment, maintenance, transaction, and opportunity costs, means that the net economic returns of carbon plantings are also likely to vary spatially." |
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Specific Policy or Decision Context Cited
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None identified | 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 | None identified |
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Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominantely south-facing slopes | 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 | Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm. |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | Population growth, road development (density) on public vs private land | Carbon prices at $10/t CO2^-e, $15/t CO2^-e, $20/t CO2^-e, $25/t CO2^-e, $30/t CO2^-e, and $40/t CO2^-e |
EM Relationship to Other EMs or Applications
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EM ID
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EM-68 | EM-86 |
EM-98
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EM-127
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs |
Method + Application (multiple runs exist) View EM Runs ?Comment:Runs are differentiated based on the the expected annual profit from two types of carbon plantings: 1) Tree-based monocultures (i.e., monoculture carbon planting) and 2) Diverse plantings of native tree and shrub species (i.e., ecological carbon planting) |
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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 | 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-68 | EM-86 |
EM-98
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EM-127
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Document ID for related EM
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Doc-260 | Doc-269 |
Doc-271 ?Comment:Document 273 used for source information on soil erosion potential variable |
Doc-328 | Doc-337 | Doc-245 | Doc-246 | Doc-247 | Doc-243 |
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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-85 | EM-87 | EM-88 | EM-403 | EM-422 | EM-128 | EM-141 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-68 | EM-86 |
EM-98
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EM-127
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EM Temporal Extent
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2007-2009 | Not reported | 2000-2025 | 2009-2050 |
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EM Time Dependence
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time-stationary | time-stationary | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | future time | future time |
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EM Time Continuity
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Not applicable | Not applicable | discrete | discrete |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | 1 | 1 |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Year | Year |
EM Spatial Extent
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EM ID
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EM-68 | EM-86 |
EM-98
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EM-127
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Bounding Type
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Physiographic or Ecological | Geopolitical | Physiographic or ecological | Physiographic or Ecological |
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Spatial Extent Name
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Central French Alps | South Africa | Western United States | Agricultural districts of the state of South Australia |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-68 | EM-86 |
EM-98
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EM-127
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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) |
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Spatial Grain Type
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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature |
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Spatial Grain Size
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20 m x 20 m | Distributed across catchments with average size of 65,000 ha | 504 km^2 | 1 ha x 1 ha |
EM Structure and Computation Approach
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EM ID
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EM-68 | EM-86 |
EM-98
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EM-127
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EM Computational Approach
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Analytic | Analytic | Numeric | Analytic |
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EM Determinism
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deterministic | deterministic | stochastic | 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-86 |
EM-98
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EM-127
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Model Calibration Reported?
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No | No | Unclear | No |
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Model Goodness of Fit Reported?
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Yes | No | No | No |
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Goodness of Fit (metric| value | unit)
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None | None | None |
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Model Operational Validation Reported?
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Yes | No | No | No |
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Model Uncertainty Analysis Reported?
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No | No | No | No |
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Model Sensitivity Analysis Reported?
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No | 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. |
No |
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Model Sensitivity Analysis Include Interactions?
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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-68 | EM-86 |
EM-98
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EM-127
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-68 | EM-86 |
EM-98
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EM-127
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| None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-68 | EM-86 |
EM-98
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EM-127
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Centroid Latitude
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45.05 | -30 | 39.88 | -34.9 |
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Centroid Longitude
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6.4 | 25 | -113.81 | 138.7 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Provided | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-68 | EM-86 |
EM-98
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EM-127
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Not reported | Not reported | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows |
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EM Ecological Scale
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Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
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-86 |
EM-98
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EM-127
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EM Organismal Scale
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Community | Not applicable | Species | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
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| EM-68 | EM-86 |
EM-98
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EM-127
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| None Available | None Available |
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EnviroAtlas URL
| EM-68 | EM-86 |
EM-98
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EM-127
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| GAP Ecological Systems, Carbon storage by tree biomass (kg/m2) | None Available | Dasymetric Allocation of Population | Carbon Storage by Tree Biomass |
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-68 | EM-86 |
EM-98
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EM-127
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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-68 | EM-86 |
EM-98
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EM-127
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None |
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None |