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-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-339 | EM-418 | EM-492 | EM-649 |
EM-661 ![]() |
EM-699 | EM-703 |
EM-728 ![]() |
EM-819 | EM-859 | EM-981 | EM-996 |
EM Short Name
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Pollination ES, Central French Alps | RHyME2, Upper Mississippi River basin, USA | PATCH, western USA | Birds in estuary habitats, Yaquina Estuary, WA, USA | InVEST crop pollination, NJ and PA, USA | SIRHI, St. Croix, USVI | EnviroAtlas - Restorable wetlands | Grasshopper Sparrow density, CREP, Iowa, USA | Alwife phosphorus flux in lakes, Connecticut, USA | Fish species richness, St. John, USVI, USA | Gadwall duck recruits, CREP wetlands, Iowa, USA | Seed mix and mowing in prairie reconstruction, USA | QHEI | ARIES Outdoor recreation, Santa Fe, NM | Atlantis ecosystem biology submodel | Co$ting Nature model method |
EM Full Name
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Pollination ecosystem service estimated from plant functional traits, Central French Alps | RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | PATCH (Program to Assist in Tracking Critical Habitat), western USA | Bird use of estuarine habitats, Yaquina Estuary, WA, USA | InVEST crop pollination, New Jersey and Pennsylvania, USA | SIRHI (SImplified Reef Health Index), St. Croix, USVI | US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | Grasshopper Sparrow population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Net phosphorus flux in freshwater lakes from alewives, Connecticut, USA | Fish species richness, St. John, USVI, USA | Gadwall duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Seed mix design and first year management in prairie reconstruction, IA, USA | QHEI (Qualitative Habitat Evaluation Index) | Artificial intelligence for Ecosystem Services (ARIES): Outdoor recreation, Santa Fe, New Mexico | Calibrating process-based marine ecosystem models: An example case using Atlantis | Co$ting Nature model method |
EM Source or Collection
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EU Biodiversity Action 5 | US EPA | US EPA | US EPA | InVEST | US EPA | US EPA | EnviroAtlas | None | None | None | None | None | None | None | None | None |
EM Source Document ID
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260 | 123 | 2 | 275 | 279 | 335 | 262 | 372 | 383 | 355 |
372 ?Comment:Document 373 is a secondary source for this EM. |
395 | 402 | 411 | 459 | 466 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C. | Carroll, C, Phillips, M. K. , Lopez-Gonzales, C. A and Schumaker, N. H. | Frazier, M. R., Lamberson, J. O. and Nelson, W. G. | Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | Yee, S. H., Dittmar, J. A., and L. M. Oliver | US EPA Office of Research and Development - National Exposure Research Laboratory | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | West, D. C., A. W. Walters, S. Gephard, and D. M. Post | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Meissen, J. C., A. J. Glidden, M. E. Sherrard, K. J. Elgersma, and L. L. Jackson | Taft, B., J. P. Koncelik | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. | Pethybridge, H. R., Weijerman, M., Perrymann, H., Audzijonyte, A., Porobic, J., McGregor, V., … & Fulton, E. | Mulligan, M. |
Document Year
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2011 | 2013 | 2006 | 2014 | 2009 | 2014 | 2013 | 2010 | 2010 | 2007 | 2010 | 2019 | 2006 | 2018 | 2019 | None |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads | Defining recovery goals and strategies for endangered species: The wolf as a case study | Intertidal habitat utilization patterns of birds in a Northeast Pacific estuary | Modelling pollination services across agricultural landscapes | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | EnviroAtlas - National | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Nutrient loading by anadromous alewife (Alosa pseudoharengus): contemporary patterns and predictions for restoration efforts | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Seed mix design and first year management influence multifunctionality and cost-effectiveness in prairie reconstruction | Methods for assessing habitat in flowing waters: Using the Qualitative Habitat Evaluation Index (QHEI) | Towards globally customizable ecosystem service models | Calibrating process-based marine ecosystem models: An example case using Atlantis | Conservation prioritisation and Ecostystem services mapping with Co$ting Nature |
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 | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Other or unclear (explain in Comment) |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | Published report | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Web page so cannot tell if documentation is reviewed |
EM ID
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EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-339 | EM-418 | EM-492 | EM-649 |
EM-661 ![]() |
EM-699 | EM-703 |
EM-728 ![]() |
EM-819 | EM-859 | EM-981 | EM-996 |
Not applicable | Not applicable | Not applicable | Not applicable | http://www.naturalcapitalproject.org/models/crop_pollination.html | Not applicable | https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
https://integratedmodelling.org/hub/#/register ?Comment:Need to set up an account first and then can access the main integrated modelling hub page: |
https://noaa-fisheries-integrated-toolbox.github.io/Atlantis | http://www1.policysupport.org/cgi-bin/ecoengine/start.cgi?project=costingnature&version=3 | |
Contact Name
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Sandra Lavorel | Liem Tran | Carlos Carroll |
M. R. Frazier ?Comment:Present address: M. R. Frazier National Center for Ecological Analysis and Synthesis, 735 State St. Suite 300, Santa Barbara, CA 93101, USA |
Eric Lonsdorf | Susan H. Yee | EnviroAtlas Team | David Otis | Derek C. West | Simon Pittman | David Otis | Justin Meissen | Edward T. Rankin | Javier Martinez-Lopez | Heidi R. Pethybridge | Mark Mulligan |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA | Klamath Center for Conservation Research, Orleans, CA 95556 | Western Ecology Division, Office of Research and Development, U.S. Environmental Protection Agency, Pacific coastal Ecology Branch, 2111 SE marine Science Drive, Newport, OR 97365 | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Not reported | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Dept. of Ecology and Evolutionary Biology, Yale University, 165 Prospect Street, New Haven, CT 06511, USA | 1305 East-West Highway, Silver Spring, MD 20910, USA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Tallgrass Prairie Center, 2412 West 27th Street, Cedar Falls, IA 50614-0294, USA | Midwest Biodiversity Institute, P.O. Box 21561, Columbus, OH 43221-0561 | BC3-Basque Centre for Climate Change, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain | CSIRO Oceans and Atmosphere, GPO Box 1538, Hobart, Tasmania, 7000, Australia | King's College London, Dept. of Geography |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | ltran1@utk.edu | carlos@cklamathconservation.org | frazier@nceas.ucsb.edu | ericlonsdorf@lpzoo.org | yee.susan@epa.gov | enviroatlas@epa.gov | dotis@iastate.edu | derek.west@yale.edu | simon.pittman@noaa.gov | dotis@iastate.edu | justin.meissen@uni.edu | Not reported | javier.martinez@bc3research.org | Heidi.Pethybridge@csiro.au | mark.mulligan@kcl.uk |
EM ID
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EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-339 | EM-418 | EM-492 | EM-649 |
EM-661 ![]() |
EM-699 | EM-703 |
EM-728 ![]() |
EM-819 | EM-859 | EM-981 | EM-996 |
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." AUTHOR'S DESCRIPTION: "The pollination ecosystem service map was a simple sums of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to pollination ecosystem services are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions." | ABSTRACT: "We describe a framework called Regional Hydrologic Modeling for Environmental Evaluation (RHyME2) for hydrologic modeling across scales. Rooted from hierarchy theory, RHyME2 acknowledges the rate-based hierarchical structure of hydrological systems. Operationally, hierarchical constraints are accounted for and explicitly described in models put together into RHyME2. We illustrate RHyME2with a two-module model to quantify annual nutrient loads in stream networks and watersheds at regional and subregional levels. High values of R2 (>0.95) and the Nash–Sutcliffe model efficiency coefficient (>0.85) and a systematic connection between the two modules show that the hierarchy theory-based RHyME2 framework can be used effectively for developing and connecting hydrologic models to analyze the dynamics of hydrologic systems." Two EMs will be entered in EPF-Library: 1. Regional scale module (Upper Mississippi River Basin) - this entry 2. Subregional scale module (St. Croix River Basin) | **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)." | AUTHOR'S DESCRIPTION: "To describe bird utilization patterns of intertidal habitats within Yaquina estuary, Oregon, we conducted censuses to obtain bird species and abundance data for the five dominant estuarine intertidal habitats: Zostera marina (eelgrass), Upogebia (mud shrimp)/ mudflat, Neotrypaea (ghost shrimp)/sandflat, Zostera japonica (Japanese eelgrass), and low marsh. EPFs were developed for the following metrics of bird use: standardized species richness; Shannon diversity; and density for the following four groups: all birds, all birds excluding gulls, waterfowl (ducks and geese), and shorebirds." | 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." | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of indicators have been proposed for measuring reef integrity, defined as the capacity to maintain healthy function and retention of diversity (Turner et al., 2000). The Simplified Integrated Reef Health Index (SIRHI) combines four attributes of reef condition into a single index: SIRHI = ΣiGi where Gi are the grades on a scale of 1 to 5 for four key reef attributes: percent coral cover, percent macroalgal cover, herbivorous fish biomass, and commercial fish biomass (Table2; Healthy Reefs Initiative, 2010). For a number of coral reef condition attributes, including fish richness, coral richness, and reef structural complexity, available data were point surveys from field monitoring by the US Environmental Protection Agency (see Oliver et al. (2011)) or the NOAA Caribbean Coral Reef Ecosystem Monitoring Program (see Pittman et al. (2008)). To generate continuous maps of coral condition for St. Croix, we fitted regression tree models to point survey data for St. Croix and then used models to predict reef condition in non-sampled locations (Fig. 1). In general, we followed the methods of Pittman et al. (2007) which generated predictive models for fish richness using readily available benthic habitat maps and bathymetry data. Because these models rely on readily available data (benthic habitat maps and bathymetry data), the models have the potential for high transferability to other locati | DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map." | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Grasshopper Sparrow (Ammodramus savannarum)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: GRSP density = e (-2.554612 + 0.0246975 * grass400 – 0.1032461 * trees400) | ABSTRACT: "Anadromous alewives (Alosa pseudoharengus) have the potential to alter the nutrient budgets of coastal lakes as they migrate into freshwater as adults and to sea as juveniles. Alewife runs are generally a source of nutrients to the freshwater lakes in which they spawn, but juveniles may export more nutrients than adults import in newly restored populations. A healthy run of alewives in Connecticut imports substantial quantities of phosphorus; mortality of alewives contributes 0.68 g P_fish–1, while surviving fish add 0.18 g P, 67% of which is excretion. Currently, alewives contribute 23% of the annual phosphorus load to Bride Lake, but this input was much greater historically, with larger runs of bigger fish contributing 2.5 times more phosphorus in the 1960s..." AUTHOR'S DESCRIPTION: "Here, we evaluate the patterns of net nutrient loading by alewives over a range of population sizes. We concentrate on phosphorus, as it is generally the nutrient that limits production in the lake ecosystems in which alewives spawn (Schindler 1978). First, we estimate net alewife nutrient loading and parameterize an alewife nutrient loading model using data from an existing run of anadromous alewives in Bride Lake. We then compare the current alewife nutrient load to that in the 1960s when alewives were more numerous and larger. Next, since little is known about the actual patterns of nutrient loading during restoration, we predict the net nutrient loading for a newly restored population across a range of adult escapement… Anadromous fish move nutrients both into and out of freshwater ecosystems, although inputs are typically more obvious and much better studied (Moore and Schindler 2004). Net loading into freshwater ecosystems is fully described as inputs due to adult mortality, gametes, and direct excretion of nutrients minus the removal of nutrients from freshwater ecosystems by juvenile fish when they emigrate… Our research was conducted at Bride Lake and Linsley Pond in Connecticut. Bride Lake contains an anadromous alewife population that we used to both evaluate contemporary and historic net nutrient loading by an alewife population and parameterize our general alewife nutrient loading model." | ABSTRACT: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems." | ABSTRACT: "Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2*R*n where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = H*Z*B/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632). | ABSTRACT: "Agricultural intensification continues to diminish many ecosystem services in the North American Corn Belt. Conservation programs may be able to combat these losses more efficiently by developing initiatives that attempt to balance multiple ecological benefits. In this study, we examine how seed mix design and first year management influence three ecosystem services commonly provided by tallgrass prairie reconstructions (erosion control, weed resistance, and pollinator resources). We established research plots with three seed mixes, with and without first year mowing. The grass-dominated “Economy” mix had 21 species and a 3:1 grass-to-forb seeding ratio. The forb-dominated “Pollinator”mix had 38 species and a 1:3 grass-to-forb seeding ratio. The grass:forb balanced “Diversity” mix, which was designed to resemble regional prairie remnants, had 71 species and a 1:1 grass-to-forb ratio. To assess ecosystem services, we measured native stem density, cover, inflorescence production, and floral richness from 2015 to 2018. The Economy mix had high native cover and stem density, but produced few inflorescences and had low floral richness. The Pollinator mix had high inflorescence production and floral richness, but also had high bare ground and weed cover. The Diversity mix had high inflorescence production and floral richness (comparable to the Pollinator mix) and high native cover and stem density (comparable to the Economy mix). First year mowing accelerated native plant establishment and inflorescence production, enhancing the provisioning of ecosystem services during the early stages of a reconstruction. Our results indicate that prairie reconstructions with thoughtfully designed seed mixes can effectively address multiple conservation challenges." | ABSTRACT: "This document summarizes the methodology for completing a general evaluation of macrohabitat, generally done by the fish field crew leader while sampling each location using the Ohio EPA Site Description Sheet - Fish (Appendix 1). This form is used to tabulate data and information for calculating the Qualitative Habitat Evaluation Index (QHEI). The following guidance should be used when completing the site evaluation form." AUTHORS' DESCRIPTION: "The Qualitative Habitat Evaluation Index (QHEI) is a physical habitat index designed to provide an empirical, quantified evaluation of the general lotic macrohabitat characteristics that are important to fish communities. A detailed analysis of the development and use of the QHEI is available in Rankin (1989) and Rankin (1995). The QHEI is composed of six principal metrics each of which are described below. The maximum possible QHEI site score is 100. Each of the metrics are scored individually and then summed to provide the total QHEI site score. This is completed at least once for each sampling site during each year of sampling. An exception to this convention would be when substantial changes to the macrohabitat have occurred between sampling passes. Standardized definitions for pool, run, and riffle habitats, for which a variety of existing definitions and perceptions exist, are essential for accurately using the QHEI." ENTERERS' DESCRIPTION: "Additional information is entered on the back of the data sheet, including; method, distance, stage, canopy, clarity, aesthetics, maintenance, recreation, issues, measurments and stream drawing." | ABSTRACT: "Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The Artificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multicriteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed. " | Calibration of complex, process-based ecosystem models is a timely task with modellers challenged by many parameters, multiple outputs of interest and often a scarcity of empirical data. Incorrect calibration can lead to unrealistic ecological and socio-economic predictions with the modeller’s experience and available knowledge of the modelled system largely determining the success of model calibration. Here we provide an overview of best practices when calibrating an Atlantis marine ecosystem model, a widely adopted framework that includes the parameters and processes comprised in many different ecosystem models. We highlight the importance of understanding the model structure and data sources of the modelled system. We then focus on several model outputs (biomass trajectories, age distributions, condition at age, realised diet proportions, and spatial maps) and describe diagnostic routines that can assist modellers to identify likely erroneous parameter values. We detail strategies to fine tune values of four groups of core parameters: growth, predator-prey interactions, recruitment and mortality. Additionally, we provide a pedigree routine to evaluate the uncertainty of an Atlantis ecosystem model based on data sources used. Describing best and current practices will better equip future modellers of complex, processed-based ecosystem models to provide a more reliable means of explaining and predicting the dynamics of marine ecosystems. Moreover, it promotes greater transparency between modellers and end-users, including resource managers. | ABSTRACT: " Co$tingNature is a sophisticated web-based spatial policy support system for natural capital accounting and analysing the ecosystem services provided by natural environments (i.e. nature's benefits), identifying the beneficiaries of these services and assessing the impacts of human interventions. This PSS is a testbed for the development and implementation of conservation strategies focused on sustaining and improving ecosystem services. It also focused on enabling the intended and unintended consequences of development actions on ecosystem service provision to be tested in silico before they are tested in vivo . The PSS incorporates detailed spatial datasets at 1-square km and 1 hectare resolution for the entire World, spatial models for biophysical and socioeconomic processes along with scenarios for climate and land use. The PSS calculates a baseline for current ecosystem service provision and allows a series of interventions (policy options) or scenarios of change to be used to understand their impact on ecosystem service delivery. We do not focus on valuing nature (how much someone is willing to pay for it) but rather costing it (understanding the resource e.g. land area and opportunity cost of nature being protected to produce the ecosystem services that we need and value). " |
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
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None identified | Not reported | 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 | None identified | None identified | None Identified | None identified | Restoration and management of diadromous fish runs in coastal New England | None provided | None identified | None identified | Flowing water habitat assessment for Ohio EPA | None identified | N/A | Conservation priorities |
Biophysical Context
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Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | No additional description provided | Great Plains to Pacific Coast, northern Rocky Mountains, Pacific Northwest | Estuarine intertidal, eelgrass, mudflat, sandflat and low marsh | No additional description provided | No additional description provided | No additional description provided | Prairie pothole region of north-central Iowa | Bride Lake is 28.7 ha and linked to Long Island Sound by the 3.3 km Bride Brook. | Hard and soft benthic habitat types approximately to the 33m isobath | Prairie Pothole Region of Iowa | The site, located at the Iowa State University Northeast Research and Demonstration Farm near Nashua, Iowa, is relatively level with slopes not exceeding a 5% grade. Soil composition is primarily poorly drained Clyde clay loams with a minor component of somewhat poorly drained Floyd loams. Sub-surface tile drains exist on site and are spaced approximately 18–24m apart. The land was used for corn and soybean production prior to site establishment in 2015. | No additional description provided | Watersheds surrounding Santa Fe and Albuquerque, New Mexico | Marine ecosystem | Woldwide coverage |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | Population growth, road development (density) on public vs private land | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | current and historical run size | No scenarios presented | No scenarios presented | Seed mix design | No scenarios presented | N/A | No scenarios presented | Policy decisions affecting future land use |
EM ID
em.detail.idHelp
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EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-339 | EM-418 | EM-492 | EM-649 |
EM-661 ![]() |
EM-699 | EM-703 |
EM-728 ![]() |
EM-819 | EM-859 | EM-981 | EM-996 |
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 | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application | Method Only | Method Only |
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 | New or revised model | Application of existing model | New or revised model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
em.detail.idHelp
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EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-339 | EM-418 | EM-492 | EM-649 |
EM-661 ![]() |
EM-699 | EM-703 |
EM-728 ![]() |
EM-819 | EM-859 | EM-981 | EM-996 |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-260 | Doc-123 | Doc-328 | Doc-337 | None | Doc-279 | None | None | Doc-372 | None | Doc-355 | Doc-372 | Doc-373 | Doc-394 | None | Doc-411 | Doc-456 | None |
EM ID for related EM
em.detail.relatedEmEmIdHelp
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EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-83 | None | EM-403 | EM-422 | None | EM-340 | EM-338 | None | None | EM-652 | EM-651 | EM-650 | EM-648 | EM-667 | EM-672 | EM-674 | EM-673 | EM-590 | EM-698 | EM-705 | EM-704 | EM-702 | EM-701 | EM-700 | EM-632 | EM-719 | None | EM-855 | EM-856 | EM-858 | EM-978 | EM-983 | EM-985 | EM-990 | EM-991 | None |
EM Modeling Approach
EM ID
em.detail.idHelp
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EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-339 | EM-418 | EM-492 | EM-649 |
EM-661 ![]() |
EM-699 | EM-703 |
EM-728 ![]() |
EM-819 | EM-859 | EM-981 | EM-996 |
EM Temporal Extent
em.detail.tempExtentHelp
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Not reported | 1987-1997 | 2000-2025 | December 2007 - November 2008 | 2000-2002 | 2006-2007, 2010 | 2006-2013 | 2002-2007 | 1960"s and early 2000's | 2000-2005 | 1987-2007 | 2015-2018 | Not applicable | 1981-2015 | Not applicable | Not applicable |
EM Time Dependence
em.detail.timeDependencyHelp
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time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
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Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | continuous | Not applicable |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-339 | EM-418 | EM-492 | EM-649 |
EM-661 ![]() |
EM-699 | EM-703 |
EM-728 ![]() |
EM-819 | EM-859 | EM-981 | EM-996 |
Bounding Type
em.detail.boundingTypeHelp
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Physiographic or Ecological | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Other | Physiographic or ecological | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Watershed/Catchment/HUC | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Other | Not applicable | Watershed/Catchment/HUC | Not applicable | Not applicable |
Spatial Extent Name
em.detail.extentNameHelp
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Central French Alps | Upper Mississippi River basin; St. Croix River Watershed | Western United States | Yaquina Estuary (intertidal), Oregon, USA | Central New Jersey and east-central Pennsylvania | Coastal zone surrounding St. Croix | conterminous United States | CREP (Conservation Reserve Enhancement Program) wetland sites | Bride Lake and Linsley Pond | SW Puerto Rico, | CREP (Conservation Reserve Enhancement Program | Iowa State University Northeast Research and Demonstration Farm near Nashua, Iowa | Not applicable | Santa Fe Fireshed | Not applicable | Not applicable |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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10-100 km^2 | 100,000-1,000,000 km^2 | >1,000,000 km^2 | 1-10 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | >1,000,000 km^2 | 1-10 km^2 | 10-100 ha | 100-1000 km^2 | 10,000-100,000 km^2 | <1 ha | Not applicable | 100-1000 km^2 | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-339 | EM-418 | EM-492 | EM-649 |
EM-661 ![]() |
EM-699 | EM-703 |
EM-728 ![]() |
EM-819 | EM-859 | EM-981 | EM-996 |
EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | 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 distributed (in at least some cases) | Not applicable | spatially lumped (in all cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | NHDplus v1 | area, for pixel or radial feature | other (habitat type) | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | Not applicable | Not applicable |
Spatial Grain Size
em.detail.spGrainSizeHelp
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20 m x 20 m | NHDplus v1 | 504 km^2 | 0.87-104.29 ha | 30 m x 30 m | 10 m x 10 m | irregular | multiple, individual, irregular shaped sites | Not applicable | not reported | multiple, individual, irregular sites | 6.1 m x 8.53 m | Not applicable | 30 m | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-339 | EM-418 | EM-492 | EM-649 |
EM-661 ![]() |
EM-699 | EM-703 |
EM-728 ![]() |
EM-819 | EM-859 | EM-981 | EM-996 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Numeric | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Not applicable | Analytic | Analytic | Analytic |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | Not applicable | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-339 | EM-418 | EM-492 | EM-649 |
EM-661 ![]() |
EM-699 | EM-703 |
EM-728 ![]() |
EM-819 | EM-859 | EM-981 | EM-996 |
Model Calibration Reported?
em.detail.calibrationHelp
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No | Yes | Unclear | Unclear | Unclear | Yes | No | Unclear | Yes | No | Unclear | Not applicable | Not applicable | Unclear | Yes | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | Yes | No | No | No | No | No | No | No | Yes | No | No | Not applicable | No | Not applicable | Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None |
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None | None | None | None | None | None | None |
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None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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No | No | No | No |
Yes ?Comment:Aggregate native bee abundance on watermelon flowers was measured at 23 sites in 2005. Species richness was measured using the specimens collected from watermelon flowers at the end of the sampling period. |
Yes | No | Unclear | No | Yes | No | No | Not applicable | No | Not applicable | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | No | No | No | No | No | No | No | No | Not applicable | No | Not applicable | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No |
No ?Comment:Some model coefficients serve, by their magnitude, to indicate the proportional impact on the final result of variation in the parameters they modify. |
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 | No | No | No | No | Yes | Yes | No | No | Not applicable | No | Not applicable | Not applicable |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Unclear | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-339 | EM-418 | EM-492 | EM-649 |
EM-661 ![]() |
EM-699 | EM-703 |
EM-728 ![]() |
EM-819 | EM-859 | EM-981 | EM-996 |
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None |
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None |
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None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-339 | EM-418 | EM-492 | EM-649 |
EM-661 ![]() |
EM-699 | EM-703 |
EM-728 ![]() |
EM-819 | EM-859 | EM-981 | EM-996 |
None | None | None |
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None |
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None | None | None |
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None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-339 | EM-418 | EM-492 | EM-649 |
EM-661 ![]() |
EM-699 | EM-703 |
EM-728 ![]() |
EM-819 | EM-859 | EM-981 | EM-996 |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | 42.5 | 39.88 | 44.62 | 40.2 | 17.73 | 39.5 | 42.62 | 41.33 | 17.79 | 42.62 | 42.93 | Not applicable | 35.86 | Not applicable | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
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6.4 | -90.63 | -113.81 | -124.06 | -74.8 | -64.77 | -98.35 | -93.84 | -72.24 | -64.62 | -93.84 | -92.57 | Not applicable | -105.76 | Not applicable | Not applicable |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | Not applicable | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Not applicable | Estimated | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-339 | EM-418 | EM-492 | EM-649 |
EM-661 ![]() |
EM-699 | EM-703 |
EM-728 ![]() |
EM-819 | EM-859 | EM-981 | EM-996 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Near Coastal Marine and Estuarine | Agroecosystems | Inland Wetlands | Agroecosystems | Grasslands | Rivers and Streams | Lakes and Ponds | Near Coastal Marine and Estuarine | Inland Wetlands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows. | None | Not reported | Estuarine intertidal | Cropland and surrounding landscape | Coral reefs | Terrestrial | Grassland buffering inland wetlands set in agricultural land | Coastal lakes and ponds and associated streams | shallow coral reefs | Wetlands buffered by grassland within agroecosystems | prairie/grassland reconstruction at demonstration farm site | Flowing fresh waters | watersheds | Multiple | Non urban |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is coarser than that of the Environmental Sub-class | Ecosystem | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | 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 | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-339 | EM-418 | EM-492 | EM-649 |
EM-661 ![]() |
EM-699 | EM-703 |
EM-728 ![]() |
EM-819 | EM-859 | EM-981 | EM-996 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Not applicable | Species | Guild or Assemblage | Species | Guild or Assemblage | Not applicable | Species | Individual or population, within a species | Guild or Assemblage | Individual or population, within a species | Community | Not applicable | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-339 | EM-418 | EM-492 | EM-649 |
EM-661 ![]() |
EM-699 | EM-703 |
EM-728 ![]() |
EM-819 | EM-859 | EM-981 | EM-996 |
None Available | None Available |
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None Available |
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None Available | None Available | None Available | None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-339 | EM-418 | EM-492 | EM-649 |
EM-661 ![]() |
EM-699 | EM-703 |
EM-728 ![]() |
EM-819 | EM-859 | EM-981 | EM-996 |
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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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-82 | EM-91 |
EM-98 ![]() |
EM-103 | EM-339 | EM-418 | EM-492 | EM-649 |
EM-661 ![]() |
EM-699 | EM-703 |
EM-728 ![]() |
EM-819 | EM-859 | EM-981 | EM-996 |
None | None |
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None | None |
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None |
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None | None | None |
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