EcoService Models Library (ESML)
Home
Search EMs
My
EMs 
Learn about
ESML
View ESML Data Maploading
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.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
|
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 |
EM-122 
|
EM-193 | EM-260 | EM-368 | EM-414 | EM-463 | EM-684 | EM-700 |
EM-760
|
EM-821
|
EM-847 | EM-938 |
EM-948
|
EM-963 | EM-964 | EM-979 |
|
EM Short Name
em.detail.shortNameHelp
?
|
Green biomass production, Central French Alps | Community flowering date, Central French Alps | Land-use change and crop-based production, Europe | Cultural ecosystem services, Bilbao, Spain | Coral taxa and land development, St.Croix, VI, USA | InVEST - Water Yield (v3.0) | SAV occurrence, St. Louis River, MN/WI, USA | Curios/jewelry production, St. Croix, USVI | Beach visitation, Barnstable, MA, USA | Mallard recruits, CREP wetlands, Iowa, USA | WESP: Marsh & wet meadow, ID, USA | Aquatic vertebrate IBI for Western streams, USA | Eastern kingbird abundance, Piedmont region, USA | OpenNSPECT v. 1.2 | Global forest stock, biomass and carbon downscaled | Eastern Meadowlark Abundance | EcoSim II - method | Predicting ecosystem service values, Bangladesh |
|
EM Full Name
em.detail.fullNameHelp
?
|
Green biomass production, Central French Alps | Community weighted mean flowering date, Central French Alps | Land-use change effects on crop-based production, Europe | Cultural ecosystem services, Bilbao, Spain | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | InVEST v3.0 Reservoir Hydropower Projection, aka Water Yield | Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA | Relative curios/jewelry production (on reef), St. Croix, USVI | Beach visitation, Barnstable, Massachusetts, USA | Mallard duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | WESP: Seasonally flooded marsh & wet meadow, Idaho, USA | Development of an aquatic vertebrate index of biotic integrity (IBI) for Western streams, USA | Eastern kingbird abundance, Piedmont ecoregion, USA | OpenNSPECT v. 1.2 | Global forest growing stock, biomass and carbon downscaled map | TEST: CRP Impacts on Eastern Meadowlark Abundance | EcoSim II - method | Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh |
|
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 |
None ?Comment:EU Mapping Studies |
US EPA | InVEST | US EPA | US EPA | US EPA | None | None | None | None | None | None |
None ?Comment:Could not find any information pertaining to a model collection. |
None | None |
|
EM Source Document ID
|
260 | 260 | 228 | 191 | 96 | 311 | 330 | 335 | 386 |
372 ?Comment:Document 373 is a secondary source for this EM. |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
404 | 405 | 431 | 442 | 405 | 448 | 457 |
|
Document Author
em.detail.documentAuthorHelp
?
|
Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Haines-Young, R., Potschin, M. and Kienast, F. | Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Natural Capital Project | Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | 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 | Murphy, C. and T. Weekley | Pont, D., Hughes, R.M., Whittier, T.R., and S. Schmutz. | Riffel, S., Scognamillo, D., and L. W. Burger | Eslinger, David L., H. Jamieson Carter, Matt Pendleton, Shan Burkhalter, Margaret Allen | Kindermann, G.E., I. McCallum, S. Fritz, and M. Obersteiner | Riffel, S., Scognamillo, D., and L. W. Burger | Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell | Morshed, S. R., Fattah, M. A., Haque, M. N., & Morshed, S. Y. |
|
Document Year
em.detail.documentYearHelp
?
|
2011 | 2011 | 2012 | 2013 | 2011 | 2015 | 2013 | 2014 | 2018 | 2010 | 2012 | 2009 | 2008 | 2012 | 2008 | 2008 | 2000 | 2022 |
|
Document Title
em.detail.sourceIdHelp
?
|
Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Water Yield: Reservoir Hydropower Production- InVEST (v3.0) | Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | A Predictive Index of Biotic Integrity Model for A predictive index of biotic integrity model foraquatic-vertebrate assemblages of Western U.S. Streams | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | “OpenNSPECT: The Open-source Nonpoint Source Pollution and Erosion Comparison Tool.” NOAA Office for Coastal Management, Charleston, South Carolina. Accessed (11/2022) at https://coast.noaa.gov/digitalcoast/tools/opennspect.html | A global forest growing stock, biomass and carbon map based on FAO statistics | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II | Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh |
|
Document Status
em.detail.statusCategoryHelp
?
|
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 | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
|
Comments on Status
em.detail.commentsOnStatusHelp
?
|
Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Web published | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published report | Published journal manuscript | Published journal manuscript | Webpage | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
Software and Access
|
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 |
EM-122
|
EM-193 | EM-260 | EM-368 | EM-414 | EM-463 | EM-684 | EM-700 |
EM-760
|
EM-821
|
EM-847 | EM-938 |
EM-948
|
EM-963 | EM-964 | EM-979 |
| Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://coast.noaa.gov/digitalcoast/tools/opennspect.html | Not applicable | Not applicable | https://ecopath.org/downloads/ | Not applicable | |
|
Contact Name
em.detail.contactNameHelp
?
|
Sandra Lavorel | Sandra Lavorel | Marion Potschin | Izaskun Casado-Arzuaga | Leah Oliver | Natural Capital Project | Ted R. Angradi | Susan H. Yee | Kate K, Mulvaney | David Otis | Chris Murphy | Didier Pont | Sam Riffell | Not reported | Georg Kindermann |
L. Wes Burger ?Comment:Lead author, Sam Riffell, pass away. Using last author. |
Carl Walters | Syed Riad Morshed |
|
Contact Address
|
Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain | National Health and Environmental Research Effects Laboratory | 371 Serra Mall, Stanford University, Stanford, Ca 94305 | U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, 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 | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Centre d’E´ tude du Machinisme Agricole et du Genie Rural, des Eaux et Foreˆts (Cemagref), Unit HYAX Hydrobiologie, 3275 Route de Ce´zanne, Le Tholonet, 13612 Aix en Provence, France | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | NOAA Coastal Services Center, 2234 South Hobson Avenue Charleston, South Carolina 29405-2413 | International Institute for Applied Systems Analysis, Laxenburg, Austria | Mississippi State University, Mississippi State, MS | Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4 | Department of Urban and Regional Planning, Khulna University of Engineering and Technology, Khulna, Bangladesh |
|
Contact Email
|
sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | marion.potschin@nottingham.ac.uk | izaskun.casado@ehu.es | leah.oliver@epa.gov | invest@naturalcapitalproject.org | angradi.theodore@epa.gov | yee.susan@epa.gov | Mulvaney.Kate@EPA.gov | dotis@iastate.edu | chris.murphy@idfg.idaho.gov | didier.pont@cemagref.fr | sriffell@cfr.msstate.edu | Not reported | kinder(at)iiasa.ac.at | w.burger@msstate.edu | c.walters@oceans.ubc.ca | riad.kuet.urp16@gmail.com |
EM Description
|
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 |
EM-122
|
EM-193 | EM-260 | EM-368 | EM-414 | EM-463 | EM-684 | EM-700 |
EM-760
|
EM-821
|
EM-847 | EM-938 |
EM-948
|
EM-963 | EM-964 | EM-979 |
|
Summary Description
em.detail.summaryDescriptionHelp
?
|
ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)." | 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: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The novel aspect of this work is an analysis of whether the historical and the projected land use changes for the periods 1990–2000, 2000–2006, and 2000–2030 are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Crop-based production); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000 and 2000–2006 (LEAC, EEA, 2006) and EURURALIS 2.0 land use scenarios for 2000–2030. The results are reported at three spatial reporting units, i.e. (1) the NUTS-X regions, (2) the bioclimatic regions, and (3) the dominant landscape types." AUTHOR'S DESCRIPTION: "The analysis for “Crop-based production” maps all the areas that are important for food crops produced through commercial agriculture….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers…The futures component of the work was based on EURURALIS 2.0 land use scenarios for 2000–2030, which are based on the four IPCC SRES land use scenarios." | ABSTRACT "This paper presents a method to quantify cultural ecosystem services (ES) and their spatial distribution in the landscape based on ecological structure and social evaluation approaches. The method aims to provide quantified assessments of ES to support land use planning decisions. A GIS-based approach was used to estimate and map the provision of recreation and aesthetic services supplied by ecosystems in a peri-urban area located in the Basque Country, northern Spain. Data of two different public participation processes (frequency of visits to 25 different sites within the study area and aesthetic value of different landscape units) were used to validate the maps. Three maps were obtained as results: a map showing the provision of recreation services, an aesthetic value map and a map of the correspondences and differences between both services. The data obtained in the participation processes were found useful for the validation of the maps. A weak spatial correlation was found between aesthetic quality and recreation provision services, with an overlap of the highest values for both services only in 7.2 % of the area. A consultation with decision-makers indicated that the results were considered useful to identify areas that can be targeted for improvement of landscape and recreation management." | AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. AUTHOR'S DESCRIPTION: "The InVEST Reservoir Hydropower model estimates the relative contributions of water from different parts of a landscape, offering insight into how changes in land use patterns affect annual surface water yield and hydropower production. Modeling the connections between landscape changes and hydrologic processes is not simple. Sophisticated models of these connections and associated processes (such as the WEAP model) are resource and data intensive and require substantial expertise. To accommodate more contexts, for which data are readily available, InVEST maps and models the annual average water yield from a landscape used for hydropower production, rather than directly addressing the affect of LULC changes on hydropower failure as this process is closely linked to variation in water inflow on a daily to monthly timescale. Instead, InVEST calculates the relative contribution of each land parcel to annual average hydropower production and the value of this contribution in terms of energy production. The net present value of hydropower production over the life of the reservoir also can be calculated by summing discounted annual revenues. The model runs on a gridded map. It estimates the quantity and value of water used for hydropower production from each subwatershed in the area of interest. It has three components, which run sequentially. First, it determines the amount of water running off each pixel as the precipitation less the fraction of the water that undergoes evapotranspiration. The model does not differentiate between surface, subsurface and baseflow, but assumes that all water yield from a pixel reaches the point of interest via one of these pathways. This model then sums and averages water yield to the subwatershed level. The pixel-scale calculations allow us to represent the heterogeneity of key driving factors in water yield such as soil type, precipitation, vegetation type, etc. However, the theory we are using as the foundation of this set of models was developed at the subwatershed to watershed scale. We are only confident in the interpretation of these models at the subwatershed scale, so all outputs are summed and/or averaged to the subwatershed scale. We do continue to provide pixel-scale representations of some outputs for calibration and model-checking purposes only. These pixel-scale maps are not to be interpreted for understanding of hydrological processes or to inform decision making of any kind. | ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake." | 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…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(6) production of curios and jewelry associated with each habitat." | ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "...We needed beach visitation estimates to assess the number of people who would be impacted by beach closures. We modeled visits by combining daily parking counts with other factors that help explain variations in attendance, including weather, day of the week or point within a season, and physical differences in sites (Kreitler et al. 2013). We designed the resulting model to estimate visitation for uncounted days as well as for beaches without counts on a given day. When combined with estimates of value per day, the visitation model can be used to value a lost beach day while accounting for beach size, time of season, and other factors...Since our count data of visitation for all four beaches are relatively large numbers (mean = 490, SD = 440), we used a log-linear regression model as opposed to a count data model. We selected a random effects model to account for time invariant variables such as parking spaces, modeling differences across beaches based on this variable…" Equation 2, page 15, provides the econometric regression. | 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). | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | ABSTRACT: "Because of natural environmental and faunal differences and scientific perspectives, numerous indices of biological integrity (IBIs) have been developed at local, state, and regional scales in the USA. These multiple IBIs, plus different criteria for judging impairment, hinder rigorous national and multistate assessments. Many IBI metrics are calibrated for water body size, but none are calibrated explicitly for other equally important natural variables such as air temperature, channel gradient, or geology. We developed a predictive aquatic-vertebrate IBI model using a total of 871 stream sites (including 162 least-disturbed and 163 most-disturbed sites) sampled as part of the U.S. Environmental Protection Agency’s Environmental Monitoring and Assessment Program survey of 12 conterminous western U.S. states. The selected IBI metrics (calculated from both fish and aquatic amphibians) were vertebrate species richness, benthic native species richness, assemblage tolerance index, proportion of invertivore–piscivore species, and proportion of lithophilic-reproducing species. Mean model IBI scores differed significantly between least-disturbed and most-disturbed sites as well as among ecoregions. Based on a model IBI impairment criterion of 0.44 (risks of type I and II errors balanced), an estimated 34.7% of stream kilometers in the western USA were deemed impaired, compared with 18% for a set of traditional IBIs. Also, the model IBI usually displayed less variability than the traditional IBIs, presumably because it was better calibrated for natural variability. " | 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. " | "This open-source version of the Nonpoint Source Pollution and Erosion Comparison Tool is used to investigate potential water quality impacts from climate change and development to other land uses. The downloadable tool is designed to be broadly applicable for coastal and noncoastal areas alike. Tool functions simulate erosion, pollution, and the accumulation from overland flow. OpenNSPECT uses spatial elevation data to calculate flow direction and flow accumulation throughout a watershed. To do this, land cover, precipitation, and soils data are processed to estimate runoff volume at both the local and watershed levels. Coefficients representing the contribution of each land cover class to the expected pollutant load are also applied to land cover data to approximate total pollutant loads. These coefficients are taken from published sources or can be derived from local water quality studies. The output layers display estimates of runoff volume, pollutant loads, pollutant concentration, and total sediment yield. Requires MapWindow GIS v.4.8.8 (open source software)" | ABSTRACT: "Currently, information on forest biomass is available from a mixture of sources, including in-situ measurements, national forest inventories, administrative-level statistics, model outputs and regional satellite products. These data tend to be regional or national, based on different methodologies and not easily accessible. One of the few maps available is the Global Forest Resources Assessment (FRA) produced by the Food and Agriculture Organization of the United Nations (FAO 2005) which contains aggregated country-level information about the growing stock, biomass and carbon stock in forests for 229 countries and territories. This paper presents a technique to downscale the aggregated results of the FRA2005 from the country level to a half degree global spatial dataset containing forest growing stock; above/belowground biomass, dead wood and total forest biomass; and above-ground, below-ground, dead wood, litter and soil carbon. In all cases, the number of countries providing data is incomplete. For those countries with missing data, values were estimated using regression equations based on a downscaling model. The downscaling method is derived using a relationship between net primary productivity (NPP) and biomass and the relationship between human impact and biomass assuming a decrease in biomass with an increased level of human activity. The results, presented here, represent one of the first attempts to produce a consistent global spatial database at half degree resolution containing forest growing stock, biomass and carbon stock values. All results from the methodology described in this paper are available online at www. iiasa.ac.at/Research/FOR/. " | 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, comprehen- sive 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. AUTHOR'S DESCRIPTION: For each species, we developed multiple regression models for the entire study area and for each of the seven ecological regions separately. We included only those routes that met quality standards for both bird abundance and land use data, and this left a total of 636 useable routes. The number of routes within individual ecological regions ranged from a low of 55 (central hardwoods) to a high of 154 (Appalachian Mountains). Using our estimates of bird abundance as response variables and landscape variables as explanatory variables, we used a stepwise selection process (retaining only explanatory variables that satisfied α < 0.05) to build models for each of the seven ecological regions and for the study region as a whole. | ABSTRACT: " EcoSim II uses results from the Ecopath procedure for trophic mass-balance analysis to define biomass dynamics models for predicting temporal change in exploited ecosystems. Key populations can be repre- sented in further detail by using delay-difference models to account for both biomass and numbers dynamics. A major problem revealed by linking the population and biomass dynamics models is in representation of population responses to changes in food supply; simple proportional growth and reproductive responses lead to unrealistic predic- tions of changes in mean body size with changes in fishing mortality. EcoSim II allows users to specify life history mechanisms to avoid such unrealistic predictions: animals may translate changes in feed- ing rate into changes in reproductive rather than growth rates, or they may translate changes in food availability into changes in foraging time that in turn affects predation risk. These options, along with model relationships for limits on prey availabil- ity caused by predation avoidance tactics, tend to cause strong compensatory responses in modeled populations. It is likely that such compensatory responses are responsible for our inability to find obvious correlations between interacting trophic components in fisheries time-series data. But Eco- sim II does not just predict strong compensatory responses: it also suggests that large piscivores may be vulnerable to delayed recruitment collapses caused by increases in prey species that are in turn competitors/predators of juvenile piscivores " | Land Use/Land Cover (LULC) provides provisional, supporting, cultural, and regulating ecosystem services that contribute to ecological environments, enhance human health and living, have economic advantages for sustaining living organisms. LULC transformation due to enormous urban expansion diminishing Ecosystem Services Values (ESVs) and discouraging sustainability. Though unplanned LULC transformation practice became more prevalent in developing countries, comprehensive assessment of LULC changes and their influences in ESVs are rarely attempted. This study aimed to illustrate and forecast the LULC changes and their influences on ESVs change in Jashore using remote sensing technologies. ESVs estimation and change analysis were conducted by utilizing -derived LULC data of the year 2000, 2010, and 2020 with the corresponding global value coefficients of each LULC type which are previously published. For simulating future LULC and ESVs, Land Change Modeler of TerrSet Geospatial Monitoring and Modeling Software was used in Multi-Layer Perceptron-Markov Chain and Artificial Neural Network method. The decline of agricultural land by 13.13% and waterbody by 5.79% has resulted in the reduction of total ESVs US$0.23 million (24.47%) during 2000–2020. The forecasted result shows that the built-up area will be dominant LULC in the future, and ESVs of provisioning and cultural services will be diminished by $0.107 million, $63400.3 by 2050 with the declination of agricultural, waterbody, vegetation, and vacant land covers. The study signifies the importance of a strategic rational land-use plan to strictly monitor and control the encroachment of built-up areas into vegetation, waterbodies, and agricultural land in addition to scientific mitigative policies for ensuring ecological sustainability. |
|
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
None identified | None identified | None identified | Land management, ecosystem management, response to EU 2020 Biodiversity Strategy | Not applicable | None identified | None identified | None identified | To assess the number of people who would be impacted by beach closures. | None identified | None identified | None reported | None reported | None identified | None identified | Food Security Act of 1985 | None | N/A |
|
Biophysical Context
|
Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | No additional description provided | Northern Spain; Bizkaia region | nearshore; <1.5 km offshore; <12 m depth | None applicable | submerged aquatic vegetation | No additional description provided | Four separate beaches within the community of Barnstable | Prairie Pothole Region of Iowa | restored, enhanced and created wetlands | Wadeable and boatable streams in 12 western USA states | Conservation Reserve Program lands left to go fallow | No additional description provided | No additional description provided | Bird Conservation Regions ranging from Central to eastern United States and from the Gulf of Mexico to the Great Lakes. | None, Ocean ecosystems | Jashore city, Bangladesh |
|
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
No scenarios presented | No scenarios presented | Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use changes (2000-2030) | No scenarios presented | Not applicable | N/A | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Sites, function or habitat focus | not applicable | N/A | No scenarios presented | No scenarios presented | Separate models created for each Bird Conservation Region, including different land use, agriculture, and CRP variable values. | N/A | No scenarios presented |
EM Relationship to Other EMs or Applications
|
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 |
EM-122
|
EM-193 | EM-260 | EM-368 | EM-414 | EM-463 | EM-684 | EM-700 |
EM-760
|
EM-821
|
EM-847 | EM-938 |
EM-948
|
EM-963 | EM-964 | EM-979 |
|
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method Only | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application View EM Runs | Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) | Method Only | Method + Application |
|
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
New or revised model | New or revised model | 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 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
em.detail.relatedEmHelp
?
EM Modeling Approach
EM Relationship to Time
|
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 |
EM-122
|
EM-193 | EM-260 | EM-368 | EM-414 | EM-463 | EM-684 | EM-700 |
EM-760
|
EM-821
|
EM-847 | EM-938 |
EM-948
|
EM-963 | EM-964 | EM-979 |
|
EM Temporal Extent
em.detail.tempExtentHelp
?
|
2007-2009 | 2007-2008 | 1990-2030 | 2000 - 2007 | 2006-2007 | Not applicable | 2010 - 2012 | 2006-2007, 2010 | 2011 - 2016 | 1987-2007 | 2010-2012 | 2004-2005 | 2008 | Not applicable | 1999-2005 | 1995-1999 | Not applicable | 2000-2050 |
|
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent |
|
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
Not applicable | Not applicable | future time | Not applicable | Not applicable | future time | Not applicable | Not applicable | past time | Not applicable | past time | past time | Not applicable | Not applicable | Not applicable | Not applicable | both | both |
|
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
discrete ?Comment:Modeller dependent |
discrete |
|
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
Not applicable | Not applicable | 6, 10, and 30 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 10 |
|
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable | Not applicable | Year | Not applicable | Not applicable | Year | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Day | Year |
EM Spatial Extent
|
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 |
EM-122
|
EM-193 | EM-260 | EM-368 | EM-414 | EM-463 | EM-684 | EM-700 |
EM-760
|
EM-821
|
EM-847 | EM-938 |
EM-948
|
EM-963 | EM-964 | EM-979 |
|
Bounding Type
em.detail.boundingTypeHelp
?
|
Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Geopolitical | Physiographic or Ecological | Not applicable | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Geopolitical | Physiographic or ecological | Not applicable | No location (no locational reference given) | Physiographic or ecological | Other | Geopolitical |
|
Spatial Extent Name
em.detail.extentNameHelp
?
|
Central French Alps | Central French Alps | The EU-25 plus Switzerland and Norway | Bilbao Metropolitan Greenbelt | St.Croix, U.S. Virgin Islands | Not applicable | St. Louis River Estuary | Coastal zone surrounding St. Croix | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | CREP (Conservation Reserve Enhancement Program | Wetlands in idaho | Western 12 states | Piedmont Ecoregion | Not applicable | Global | Bird Conservation Regions comprising the northern bobwhite breeding range. | Not applicable | Jashore city, Bangladesh |
|
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 10-100 km^2 | Not applicable | 10-100 km^2 | 100-1000 km^2 | 10-100 ha | 10,000-100,000 km^2 | 100,000-1,000,000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | Not applicable | >1,000,000 km^2 | >1,000,000 km^2 | Not applicable | 1000-10,000 km^2. |
Spatial Distribution of Computations
|
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 |
EM-122
|
EM-193 | EM-260 | EM-368 | EM-414 | EM-463 | EM-684 | EM-700 |
EM-760
|
EM-821
|
EM-847 | EM-938 |
EM-948
|
EM-963 | EM-964 | EM-979 |
|
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
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) ?Comment:pixel is likely 30m x 30m |
spatially distributed (in at least some cases) ?Comment:BH: Each individual transect?s data was parceled into location reports, and that each report?s ?quadrat? area was dependent upon the angle of the hydroacoustic sampling beam. The spatial grain is 0.07 m^2, 0.20 m^2 and 0.70 m^2 for depths of 1 meter, 2 meters and 3 meters, respectively. |
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) ?Comment:871 total sites surveyed for this work |
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) |
|
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | map scale, for cartographic feature |
|
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
20 m x 20 m | 20 m x 20 m | 1 km x 1 km | 2 m x 2 m | Not applicable | Not specified | 0.07 m^2 to 0.70 m^2 | 10 m x 10 m | by beach site | multiple, individual, irregular sites | Not applicable | stream reach | Not applicable | 30 m | 0.5 x 0.5 degrees | 1962 km^2 | Not applicable | 30m |
EM Structure and Computation Approach
|
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 |
EM-122
|
EM-193 | EM-260 | EM-368 | EM-414 | EM-463 | EM-684 | EM-700 |
EM-760
|
EM-821
|
EM-847 | EM-938 |
EM-948
|
EM-963 | EM-964 | EM-979 |
|
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Analytic | Analytic | Logic- or rule-based | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Logic- or rule-based | Analytic | Analytic | Analytic | Analytic | Analytic |
|
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
|
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Model Checking Procedures Used
|
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 |
EM-122
|
EM-193 | EM-260 | EM-368 | EM-414 | EM-463 | EM-684 | EM-700 |
EM-760
|
EM-821
|
EM-847 | EM-938 |
EM-948
|
EM-963 | EM-964 | EM-979 |
|
Model Calibration Reported?
em.detail.calibrationHelp
?
|
No | No | No | No | Yes |
Yes ?Comment:Annual Yield can be calibrated with actual yield based up 10 year average input data though this was an "optional" part of the model. Calibrate with total precipitation and potential evapotranspiration. Before the calibration process is commenced, the modelers suggest performing a sensitivity analysis with the observed runoff data to define the parameters that influence model outputs the most. The calibration can then focus on highly sensitive parameters followed by less sensitive ones. |
Yes | Yes | Yes | Unclear | No | No | No | Not applicable | No |
Unclear ?Comment:Does accounting for autocorrelation count as validation? |
No | Yes |
|
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
Yes | Yes | No | No | Yes | Not applicable | Yes | No | No | No | No | No | No | Not applicable |
Yes ?Comment:For the 0.5 grid level equation where the country forest level is missing. |
Not applicable | No | Yes |
|
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
|
|
None | None |
|
None |
|
None | None | None | None | None | None | None |
|
None | None |
|
|
Model Operational Validation Reported?
em.detail.validationHelp
?
|
Yes | No | No | Yes | No | No | Yes | Yes | No | No | No |
Yes ?Comment:Compared to another journal manuscript IBI scores (Whittier et al) |
No | Not applicable | Yes | No | Not applicable | Yes |
|
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
No | No | No | No | Yes | No | No | No | No | No | No | No | No | Not applicable | No | No | Not applicable | Unclear |
|
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
No | No | No | No | No | Not applicable | No | No | Yes | No | No | Yes | Yes | Not applicable | No | No | Not applicable | Unclear |
|
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Yes | Unclear | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
em.detail.relationToSpaceTerrestrialHelp
?
| EM-65 | EM-71 |
EM-122
|
EM-193 | EM-260 | EM-368 | EM-414 | EM-463 | EM-684 | EM-700 |
EM-760
|
EM-821
|
EM-847 | EM-938 |
EM-948
|
EM-963 | EM-964 | EM-979 |
|
|
|
|
None | None |
|
None |
|
|
|
|
|
None | None |
|
None |
|
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
?
| EM-65 | EM-71 |
EM-122
|
EM-193 | EM-260 | EM-368 | EM-414 | EM-463 | EM-684 | EM-700 |
EM-760
|
EM-821
|
EM-847 | EM-938 |
EM-948
|
EM-963 | EM-964 | EM-979 |
| None | None | None | None |
|
None | None |
|
|
None | None | None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
|
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 |
EM-122
|
EM-193 | EM-260 | EM-368 | EM-414 | EM-463 | EM-684 | EM-700 |
EM-760
|
EM-821
|
EM-847 | EM-938 |
EM-948
|
EM-963 | EM-964 | EM-979 |
|
Centroid Latitude
em.detail.ddLatHelp
?
|
45.05 | 45.05 | 50.53 | 43.25 | 17.75 | -9999 | 46.72 | 17.73 | 41.64 | 42.62 | 44.06 | 44.2 | 36.23 | Not applicable | 44.51 | 36.53 | Not applicable | 23.95 |
|
Centroid Longitude
em.detail.ddLongHelp
?
|
6.4 | 6.4 | 7.6 | -2.92 | -64.75 | -9999 | -96.13 | -64.77 | -70.29 | -93.84 | -114.69 | -113.07 | -81.9 | Not applicable | -123.51 | -88.45 | Not applicable | 89.12 |
|
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | WGS84 | WGS84 | NAD83 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | NAD83 | Not applicable | other |
|
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Provided | Provided | Estimated | Provided | Estimated | Not applicable | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated | Not applicable | Provided |
Environments and Scales Modeled
|
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 |
EM-122
|
EM-193 | EM-260 | EM-368 | EM-414 | EM-463 | EM-684 | EM-700 |
EM-760
|
EM-821
|
EM-847 | EM-938 |
EM-948
|
EM-963 | EM-964 | EM-979 |
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Agroecosystems | Grasslands | Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Near Coastal Marine and Estuarine | Rivers and Streams | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Rivers and Streams | Grasslands | Aquatic Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Forests |
Terrestrial Environment (sub-classes not fully specified) ?Comment:Is there a way to choose more than one? |
Open Ocean and Seas | Terrestrial Environment (sub-classes not fully specified) |
|
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Not applicable | none | stony coral reef | Watershed | Freshwater estuarine system | Coral reefs | Saltwater beach | Wetlands buffered by grassland within agroecosystems | created, restored and enhanced wetlands | wadeable and boatable streams | grasslands | Coastal and non-coastal | Forests | A mixture of developed and natural environments including cultivated and non-cultivated cropland, pastures, roads / railways, and urban areas as well as grasslands, forest, and freshwater habitats spanning the central to eastern United States. | Pelagic | Urban city |
|
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Not applicable | Not applicable | 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 | Not applicable | 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 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 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 |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
em.detail.nameOfOrgsOrGroupsHelp
?
|
EM ID
em.detail.idHelp
?
|
EM-65 | EM-71 |
EM-122
|
EM-193 | EM-260 | EM-368 | EM-414 | EM-463 | EM-684 | EM-700 |
EM-760
|
EM-821
|
EM-847 | EM-938 |
EM-948
|
EM-963 | EM-964 | EM-979 |
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Community | Community | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Guild or Assemblage | Species | Not applicable | Not applicable | Species |
Other (Comment) ?Comment:Varied levels of taxonomic order |
Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
?
| EM-65 | EM-71 |
EM-122
|
EM-193 | EM-260 | EM-368 | EM-414 | EM-463 | EM-684 | EM-700 |
EM-760
|
EM-821
|
EM-847 | EM-938 |
EM-948
|
EM-963 | EM-964 | EM-979 |
| None Available | None Available | None Available | None Available |
|
None Available | None Available | None Available | 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.detail.cicesHelp
?
| EM-65 | EM-71 |
EM-122
|
EM-193 | EM-260 | EM-368 | EM-414 | EM-463 | EM-684 | EM-700 |
EM-760
|
EM-821
|
EM-847 | EM-938 |
EM-948
|
EM-963 | EM-964 | EM-979 |
| None | None |
|
|
|
|
|
|
|
|
|
|
|
|
None |
|
|
|
<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>
fegs1Help
?
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
?
| EM-65 | EM-71 |
EM-122
|
EM-193 | EM-260 | EM-368 | EM-414 | EM-463 | EM-684 | EM-700 |
EM-760
|
EM-821
|
EM-847 | EM-938 |
EM-948
|
EM-963 | EM-964 | EM-979 |
|
None |
|
|
|
|
None |
|
|
|
None |
|
|
None | None |
|
|
|