EcoService Models Library (ESML)
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Compare EMs
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.
EM Identity and Description
EM ID
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EM-98 ![]() |
EM-340 | EM-658 |
EM Short Name
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PATCH, western USA | InVEST crop pollination, Costa Rica | Polyscape, Wales |
EM Full Name
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PATCH (Program to Assist in Tracking Critical Habitat), western USA | InVEST crop pollination, Costa Rica | Polyscape, Wales |
EM Source or Collection
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US EPA | InVEST | None |
EM Source Document ID
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2 | 279 | 379 |
Document Author
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Carroll, C, Phillips, M. K. , Lopez-Gonzales, C. A and Schumaker, N. H. | Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | Jackson, B., T. Pagella, F. Sinclair, B. Orellana, A. Henshaw, B. Reynolds, N. Mcintyre, H. Wheater, and A. Eycott |
Document Year
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2006 | 2009 | 2013 |
Document Title
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Defining recovery goals and strategies for endangered species: The wolf as a case study | Modelling pollination services across agricultural landscapes | Polyscape: A GIS mapping framework providing efficient and spatially explicit landscape-scale valuation of multple ecosystem services |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-98 ![]() |
EM-340 | EM-658 |
Not applicable | http://www.naturalcapitalproject.org/models/crop_pollination.html |
https://www.lucitools.org/ ?Comment:The LUCI (Land Utilisation and Capability Indicator) model, is a second-generation extension and software implementation of the Polyscape framework. |
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Contact Name
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Carlos Carroll | Eric Lonsdorf | Bethanna Jackson |
Contact Address
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Klamath Center for Conservation Research, Orleans, CA 95556 | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | School of Geography, Environment and Earth Sciences, Victoria University of Wellington, PO Box 600, Wellington, New Zealand |
Contact Email
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carlos@cklamathconservation.org | ericlonsdorf@lpzoo.org | bethanna.jackson@vuw.ac.nz |
EM ID
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EM-98 ![]() |
EM-340 | EM-658 |
Summary Description
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**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)." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "Background and Aims: Crop pollination by bees and other animals is an essential ecosystem service. Ensuring the maintenance of the service requires a full understanding of the contributions of landscape elements to pollinator populations and crop pollination. Here, the first quantitative model that predicts pollinator abundance on a landscape is described and tested. Methods: Using information on pollinator nesting resources, floral resources and foraging distances, the model predicts the relative abundance of pollinators within nesting habitats. From these nesting areas, it then predicts relative abundances of pollinators on the farms requiring pollination services. Model outputs are compared with data from coffee in Costa Rica, watermelon and sunflower in California and watermelon in New Jersey–Pennsylvania (NJPA). Key Results: Results from Costa Rica and California, comparing field estimates of pollinator abundance, richness or services with model estimates, are encouraging, explaining up to 80 % of variance among farms. However, the model did not predict observed pollinator abundances on NJPA, so continued model improvement and testing are necessary. The inability of the model to predict pollinator abundances in the NJPA landscape may be due to not accounting for fine-scale floral and nesting resources within the landscapes surrounding farms, rather than the logic of our model. Conclusions: The importance of fine-scale resources for pollinator service delivery was supported by sensitivity analyses indicating that the model's predictions depend largely on estimates of nesting and floral resources within crops. Despite the need for more research at the finer-scale, the approach fills an important gap by providing quantitative and mechanistic model from which to evaluate policy decisions and develop land-use plans that promote pollination conservation and service delivery." AUTHOR'S DESCRIPTION: "…Lacking information on seasonality, a single flight season was assumed for all species..." | ABSTRACT: "This paper introduces a GIS framework (Polyscape) designed to explore spatially explicit synergies and trade-offs amongst ecosystem services to support landscape management (from individual fields through to catchments of ca 10,000 km2 scale). Algorithms are described and results presented from a case study application within an upland Welsh catchment (Pontbren). Polyscape currently includes algorithms to explore the impacts of land cover change on flood risk, habitat connectivity, erosion and associated sediment delivery to receptors, carbon sequestration and agricultural productivity. Algorithms to trade these single-criteria landscape valuations against each other are also provided, identifying where multiple service synergies exist or could be established. Changes in land management can be input to the tool and “traffic light” coded impact maps produced, allowing visualisation of the impact of different decisions. Polyscape hence offers a means for prioritising existing feature preservation and identifying opportunities for landscape change. The basic algorithms can be applied using widely available national scale digital elevation, land use and soil data. Enhanced output is possible where higher resolution data are available..." AUTHOR'S DESCRIPTION: "The framework acts as a screening tool to identify areas where scientific investigation might be valuably directed and/or where a lack of information exists, and allows flexibility and quick visualisation of the impact of different rural land management decisions on a variety of sustainability criteria. Specifically, Polyscape is designed to facilitate: 1. spatially explicit policy implementation; 2. integration of policy implementation across sectors (e.g., water, biodiversity, agriculture and forestry); 3. participation (and learning) by many different stakeholder groups. Importantly, it is designed not as a prescriptive decision making tool, but as a negotiation tool. Algorithms allow identification of ideas of where change might be beneficial – for example where installation of “structures” such as ponds or buffer strips might be considered optimal at a farm scale – but also allows users to trial their own plans and build in their own knowledge/restrictions. The framework aims to highlight areas with maximum potential for improvement, not to place value judgements on which methods (e.g., tillage change, land use change, hard engineering approaches) might be appropriate to realise such potential. Furthermore, the toolbox aims to identify areas of existing high value – e.g., particularly productive cropland, wetlands..." "Our case study site is the 12.5 km2 catchment of the Pontbren in mid-Wales." NOTE: The LUCI (Land Utilisation and Capability Indicator) model, is a second-generation extension and software implementation of the Polyscape framework, as described in EM-659. https://esml.epa.gov/detail/em/659 |
Specific Policy or Decision Context Cited
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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 | Polyscape acts as a screening tool to allow flexibility and visualisation of the impact of different rural land management decisions. |
Biophysical Context
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Great Plains to Pacific Coast, northern Rocky Mountains, Pacific Northwest | No additional description provided | Elevation ranges between 170 m and 425 m |
EM Scenario Drivers
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Population growth, road development (density) on public vs private land | No scenarios presented | Initial habitat coverage (1990), and planting additional broadleaved woodland (2001-2007) |
EM ID
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EM-98 ![]() |
EM-340 | EM-658 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application |
New or Pre-existing EM?
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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
EM ID
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EM-98 ![]() |
EM-340 | EM-658 |
Document ID for related EM
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Doc-328 | Doc-337 | Doc-279 | Doc-380 |
EM ID for related EM
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EM-403 | EM-422 | EM-338 | EM-339 | EM-659 |
EM Modeling Approach
EM ID
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EM-98 ![]() |
EM-340 | EM-658 |
EM Temporal Extent
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2000-2025 | 2001-2002 | 1990-2007 |
EM Time Dependence
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time-dependent | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable |
EM Time Continuity
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discrete | Not applicable | Not applicable |
EM Temporal Grain Size Value
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1 | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable |
EM ID
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EM-98 ![]() |
EM-340 | EM-658 |
Bounding Type
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Physiographic or ecological | Other | Watershed/Catchment/HUC |
Spatial Extent Name
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Western United States | Large coffee farm, Valle del General | Pontbren catchment |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 10-100 km^2 | 10-100 km^2 |
EM ID
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EM-98 ![]() |
EM-340 | EM-658 |
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) |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature |
Spatial Grain Size
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504 km^2 | 30 m x 30 m | Not reported |
EM ID
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EM-98 ![]() |
EM-340 | EM-658 |
EM Computational Approach
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Numeric | Analytic | Analytic |
EM Determinism
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stochastic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-98 ![]() |
EM-340 | EM-658 |
Model Calibration Reported?
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Unclear | Unclear | No |
Model Goodness of Fit Reported?
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No | No | No |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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No | Yes | No |
Model Uncertainty Analysis Reported?
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No | No | No |
Model Sensitivity Analysis Reported?
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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 | No |
Model Sensitivity Analysis Include Interactions?
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Unclear | No | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-98 ![]() |
EM-340 | EM-658 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-98 ![]() |
EM-340 | EM-658 |
None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-98 ![]() |
EM-340 | EM-658 |
Centroid Latitude
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39.88 | 9.13 | 52.61 |
Centroid Longitude
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-113.81 | -83.37 | -3.3 |
Centroid Datum
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WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Estimated | Estimated |
EM ID
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EM-98 ![]() |
EM-340 | EM-658 |
EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Grasslands |
Specific Environment Type
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Not reported | Cropland and surrounding landscape | mainly of ‘improved’ pasture, semi-natural, unmanaged moorland, mature woodland, recent tree plantations, and small paved/roofed areas, root crops and open water |
EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-98 ![]() |
EM-340 | EM-658 |
EM Organismal Scale
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Species | Species | Unsure |
Taxonomic level and name of organisms or groups identified
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EM-340 | EM-658 |
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None Available |
EnviroAtlas URL
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EM-340 | EM-658 |
Dasymetric Allocation of Population | GAP Ecological Systems | The National Hydrography Dataset (NHD) |
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-98 ![]() |
EM-340 | EM-658 |
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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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-98 ![]() |
EM-340 | EM-658 |
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