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-130 |
EM-541 
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EM-629 | EM-843 | EM-878 |
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EM Short Name
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KINEROS2, River Ravna watershed, Bulgaria | InVEST fisheries, lobster, South Africa | SolVES, Pike & San Isabel NF, WY | Mourning dove abundance, Piedmont region, USA | Health, safety and greening urban space, PA, USA |
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EM Full Name
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KINEROS (Kinematic runoff and erosion model) v2, River Ravna watershed,Bulgaria | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa | SolVES, Social Values for Ecosystem Services, Pike and San Isabel National Forest, CO | Mourning dove abundance, Piedmont ecoregion, USA | Health, safety and greening urban vacant space, Pennsylvania, USA |
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EM Source or Collection
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EU Biodiversity Action 5 | InVEST | None | None | None |
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EM Source Document ID
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248 ?Comment:Document 277 is also a source document for this EM |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
369 | 405 | 419 |
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Document Author
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Nedkov, S., Burkhard, B. | Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | Riffel, S., Scognamillo, D., and L. W. Burger | Branas, C. C., R. A. Cheney, J. M. MacDonald, V. W. Tam, T. D. Jackson, and T. R. Ten Havey |
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Document Year
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2012 | 2018 | 2014 | 2008 | 2011 |
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Document Title
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Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | A difference-in-differences analysis of health, safety, and greening vacant urban space |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-130 |
EM-541
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EM-629 | EM-843 | EM-878 |
| http://www.tucson.ars.ag.gov/agwa/ | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable | |
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Contact Name
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David C. Goodrich | Michelle Ward | Benson Sherrouse | Sam Riffell | Charles C. Branas |
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Contact Address
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USDA - ARS Southwest Watershed Research Center, 2000 E. Allen Rd., Tucson, AZ 85719 | ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Blockley Hall, Room 936, 423 Guardian Drive, Philadelphia, PA 19104-6021 |
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Contact Email
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agwa@tucson.ars.ag.gov | m.ward@uq.edu.au | bcsherrouse@usgs.gov | sriffell@cfr.msstate.edu | cbranas@upenn.edu |
EM Description
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EM ID
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EM-130 |
EM-541
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EM-629 | EM-843 | EM-878 |
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Summary Description
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ABSTRACT: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. In this study, the capacities of different ecosystems to regulate floods were assessed through investigations of water retention functions of the vegetation and soil cover. The use of the catchment based hydrologic model KINEROS and the GIS AGWA tool provided data about peak rivers’ flows and the capability of different land cover types to “capture” and regulate some parts of the water." AUTHOR'S DESCRIPTION: "KINEROS is a distributed, physically based, event model describing the processes of interception, dynamic infiltration, surface runoff and erosion from watersheds characterized by predominantly overland flow. The watershed is conceptualized as a cascade and the channels, over which the flow is routed in a top–down approach, are using a finite difference solution of the one-dimensional kinematic wave equations (Semmens et al., 2005). Rainfall excess, which leads to runoff, is defined as the difference between precipitation amount and interception and infiltration depth. The rate at which infiltration occurs is not constant but depends on the rainfall rate and the accumulated infiltration amount, or the available moisture condition of the soil. The AGWA tool is a multipurpose hydrologic analysis system addressed to: (1) provide a simple, direct and repeatable method for hydrologic modeling; (2) use basic, attainable GIS data; (3) be compatible with other geospatial basin-based environmental analysis software; and (4) be useful for scenario development and alternative future simulation work at multiple scales (Miller et al., 2002). AGWA provides the functionality to conduct the processes of modeling and assessment for…KINEROS." | AUTHOR'S DESCRIPTION: "Here we develop a method for assessing future scenarios of environmental management change that improve coastal ecosystem services and thereby, support the success of the SDGs. We illustrate application of the method using a case study of South Africa’s West Coast Rock Lobster fishery within the Table Mountain National Park (TMNP) Marine Protected Area...We calculated the retrospective and current value of the West Coast Rock Lobster fishery using published and unpublished data from various sources and combined the market worth of landed lobster from recreational fishers, small-scale fisheries (SSF), large-scale fisheries (LSF) and poachers. Then using the InVEST tool, we combined data to build scenarios that describe possible futures for the West Coast Rock Lobster fishery (see Table 1). The first scenario, entitled ‘Business as Usual’ (BAU), takes the current situation and most up-to-date data to model the future if harvest continues at the existing rate. The second scenario is entitled ‘Redirect the Poachers’ (RP), which attempts to model implementation of strict management, whereby poaching is minimised from the Marine Protected Area and other economic and nutritional sources are made available through government initiatives. The third scenario, entitled ‘Large Scale Cutbacks’ (LSC), excludes large-scale fisheries from harvesting West Coast Rock Lobster within the TMNP Marine Protected Area." | [ABSTRACT: " "Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders.With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems. | 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. " | ABSTRACT: "Greening of vacant urban land may affect health and safety. The authors conducted a decade-long difference-indifferences analysis of the impact of a vacant lot greening program in Philadelphia, Pennsylvania, on health and safety outcomes. ‘‘Before’’ and ‘‘after’’ outcome differences among treated vacant lots were compared with matched groups of control vacant lots that were eligible but did not receive treatment. Control lots from 2 eligibility pools were randomly selected and matched to treated lots at a 3:1 ratio by city section. Random-effects regression models were fitted, along with alternative models and robustness checks. Across 4 sections of Philadelphia, 4,436 vacant lots totaling over 7.8 million square feet (about 725,000 m^2) were greened from 1999 to 2008. Regression adjusted estimates showed that vacant lot greening was associated with consistent reductions in gun assaults across all 4 sections of the city (P < 0.001) and consistent reductions in vandalism in 1 section of the city (P < 0.001). Regression-adjusted estimates also showed that vacant lot greening was associated with residents’ reporting less stress and more exercise in select sections of the city (P < 0.01). Once greened, vacant lots may reduce certain crimes and promote some aspects of health. Limitations of the current study are discussed. Community-based trials are warranted to further test these findings." REVIEWER'S COMMENTS: Regression models were fitted separately for point-based, tract-based, and block group-based outcomes, and for the four sections of Philadelphia separately and combined. This entry presents just the point-based outcomes for the whole of Philadelphia. |
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Specific Policy or Decision Context Cited
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None identified | Future rock lobster fisheries management | None | None reported | None identified |
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Biophysical Context
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Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak. | No additional description provided | Rocky mountain conifer forests | Conservation Reserve Program lands left to go fallow | No additional description provided |
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EM Scenario Drivers
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No scenarios presented | Fisheries exploitation; fishing vulnerability (of age classes) | N/A | N/A | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-130 |
EM-541
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EM-629 | EM-843 | EM-878 |
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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 |
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New or Pre-existing EM?
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Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-130 |
EM-541
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EM-629 | EM-843 | EM-878 |
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Document ID for related EM
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Doc-277 | Doc-294 | Doc-249 | Doc-250 ?Comment:Document 277 is also a source document for this EM |
None | Doc-369 | Doc-405 | None |
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EM ID for related EM
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EM-132 | EM-133 | None | EM-626 | EM-628 | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-844 | EM-845 | EM-846 | EM-847 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-130 |
EM-541
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EM-629 | EM-843 | EM-878 |
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EM Temporal Extent
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Not reported | 1986-2115 | 2004-2008 | 2008 | 1998-2008 |
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EM Time Dependence
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time-dependent | time-dependent | time-stationary | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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future time | future time | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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discrete | discrete | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not reported | 1 | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not reported | Year | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-130 |
EM-541
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EM-629 | EM-843 | EM-878 |
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Bounding Type
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Watershed/Catchment/HUC | Geopolitical | Geopolitical | Physiographic or ecological | Geopolitical |
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Spatial Extent Name
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River Ravna watershed | Table Mountain National Park Marine Protected Area | National Park | Piedmont Ecoregion | Philadelphia |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | 100-1000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-130 |
EM-541
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EM-629 | EM-843 | EM-878 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
spatially distributed (in at least some cases) ?Comment:Point-based measures are continuous and boundary-free, assign each lot to its own unique neighborhood, and avoid aggregation effects while directly accounting for spillover and the variability of neighboring areas. |
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Spatial Grain Type
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area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) |
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Spatial Grain Size
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25 m x 25 m | Not applicable | 30m2 | Not applicable | Point based |
EM Structure and Computation Approach
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EM ID
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EM-130 |
EM-541
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EM-629 | EM-843 | EM-878 |
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EM Computational Approach
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Numeric | Numeric | Numeric | Analytic | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-130 |
EM-541
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EM-629 | EM-843 | EM-878 |
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Model Calibration Reported?
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Yes | No | No | Yes | No |
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Model Goodness of Fit Reported?
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No | No | Yes | No |
No ?Comment:Each outcome was fitted separatly, with R2 provided. See Variable Value comment for each Response. |
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Goodness of Fit (metric| value | unit)
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None | None |
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None | None |
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Model Operational Validation Reported?
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No |
Yes ?Comment:A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data. |
No | No | No |
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Model Uncertainty Analysis Reported?
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No | No | No | No | No |
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Model Sensitivity Analysis Reported?
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No | No | No | Yes | No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Unclear | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| EM-130 |
EM-541
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EM-629 | EM-843 | EM-878 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-130 |
EM-541
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EM-629 | EM-843 | EM-878 |
| None |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-130 |
EM-541
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EM-629 | EM-843 | EM-878 |
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Centroid Latitude
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42.8 | -34.18 | 38.7 | 36.23 | 39.95 |
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Centroid Longitude
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24 | 18.35 | 105.89 | -81.9 | -75.17 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Provided | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-130 |
EM-541
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EM-629 | EM-843 | EM-878 |
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EM Environmental Sub-Class
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Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Forests | Near Coastal Marine and Estuarine | Forests | Grasslands | Created Greenspace |
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Specific Environment Type
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Primarily forested watershed | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | Montain forest | grasslands | Urban and urban green space |
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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 | 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-130 |
EM-541
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EM-629 | EM-843 | EM-878 |
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EM Organismal Scale
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Not applicable | Individual or population, within a species | Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-130 |
EM-541
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EM-629 | EM-843 | EM-878 |
| 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)
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| EM-130 |
EM-541
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EM-629 | EM-843 | EM-878 |
| 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-130 |
EM-541
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EM-629 | EM-843 | EM-878 |
| None |
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