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-80 | EM-91 | EM-94 | EM-97 | EM-196 | EM-428 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-630 | EM-654 | EM-683 | EM-703 |
EM-713 ![]() |
EM-779 ![]() |
EM-821 ![]() |
EM-905 ![]() |
EM Short Name
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Agronomic ES and plant traits, Central French Alps | RHyME2, Upper Mississippi River basin, USA | Reduction in pesticide runoff risk, Europe | AnnAGNPS, Kaskaskia River watershed, IL, USA | N removal by wetlands, Contiguous USA | Retained rainwater, Guánica Bay, Puerto Rico | Value of finfish, St. Croix, USVI | InVEST fisheries, lobster, South Africa | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | RBI Spatial Analysis Method | WaterWorld v2, Santa Basin, Peru | Forest recreation, Wisconsin, USA | Estuary visitation, Cape Cod, MA | Gadwall duck recruits, CREP wetlands, Iowa, USA | ESII Tool, Michigan, USA | Arthropod flower preference, CA, USA | Aquatic vertebrate IBI for Western streams, USA | Oyster filtration of estuary waters, U. S. |
EM Full Name
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Agronomic ecosystem service estimated from plant functional traits, Central French Alps | RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | Reduction in pesticide runoff risk, Europe | AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | Nitrogen removal by wetlands as a function of loading, Contiguous USA | Retained rainwater, Guánica Bay, Puerto Rico, USA | Relative value of finfish (on reef), St. Croix, USVI | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa | DeNitrification-DeComposition simulation of N2O flux Ireland | Rapid Benefit Indicator (RBI) Spatial Analysis Toolset Method | WaterWorld v2, Santa Basin, Peru | Forest recreation, Wisconsin, USA | Value of recreational use of an estuary, Cape Cod, Massachusetts | Gadwall duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | ESII (Ecosystem Services Identification and Inventory) Tool, Michigan, USA | Arthropod flower type preference, California, USA | Development of an aquatic vertebrate index of biotic integrity (IBI) for Western streams, USA | Oyster filtration of estuary waters, U. S. |
EM Source or Collection
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EU Biodiversity Action 5 | US EPA | None | US EPA | US EPA | US EPA | US EPA | InVEST | None | None | None | None | US EPA | None | None | None | None | None |
EM Source Document ID
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260 | 123 | 255 | 137 | 63 | 338 | 335 |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
358 | 367 | 368 | 376 | 387 |
372 ?Comment:Document 373 is a secondary source for this EM. |
392 ?Comment:Document 391 is an additional source for this EM. |
399 | 404 | 425 |
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. | Lautenbach, S., Maes, J., Kattwinkel, M., Seppelt, R., Strauch, M., Scholz, M., Schulz-Zunkel, C., Volk, M., Weinert, J. and Dormann, C. | Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Jordan, S., Stoffer, J. and Nestlerode, J. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Bousquin, J., Mazzotta M., and W. Berry | Van Soesbergen, A. and M. Mulligan | Qiu, J. and M. G. Turner | Mulvaney, K K., Atkinson, S.F., Merrill, N.H., Twichell, J.H., and M.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 | Guertin, F., K. Halsey, T. Polzin, M. Rogers, and B. Witt | Lundin, O., Ward, K.L., and N.M. Williams | Pont, D., Hughes, R.M., Whittier, T.R., and S. Schmutz. | zu Ermgassen, S. E., M, D. Spalding, R. E. Grizzle, and R. D. Brumbaugh |
Document Year
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2011 | 2013 | 2012 | 2011 | 2011 | 2017 | 2014 | 2018 | 2010 | 2017 | 2018 | 2013 | 2019 | 2010 | 2019 | 2018 | 2009 | 2013 |
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 | Mapping water quality-related ecosystem services: concepts and applications for nitrogen retention and pesticide risk reduction | AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | Wetlands as sinks for reactive nitrogen at continental and global scales: A meta-analysis | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals | Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture | Rapid Benefit Indicators (RBI) Spatial Analysis Toolset - Manual. | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | Spatial interactions among ecosystem services in an urbanizing agricultural watershed | Quantifying Recreational Use of an Estuary: A case study of three bays, Cape Cod, USA | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | From ash pond to riverside wetlands: Making the business case for engineered natural technologies | Indentifying native plants for coordinated hanbitat manegement of arthroppod pollinators, herbivores and natural enemies | A Predictive Index of Biotic Integrity Model for A predictive index of biotic integrity model foraquatic-vertebrate assemblages of Western U.S. Streams | Quantifying the loss of a marine ecosystem service: Filtration by the Eastern Oyster in US estuaries |
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 but unpublished (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report | Published journal manuscript | Published journal manuscript | Draft manuscript-work progressing | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-80 | EM-91 | EM-94 | EM-97 | EM-196 | EM-428 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-630 | EM-654 | EM-683 | EM-703 |
EM-713 ![]() |
EM-779 ![]() |
EM-821 ![]() |
EM-905 ![]() |
Not applicable | Not applicable | Not applicable | https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | Not applicable | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | http://www.dndc.sr.unh.edu | Not applicable | www.policysupport.org/waterworld | Not applicable | Not applicable | Not applicable | https://www.esiitool.com/ | Not applicable | Not applicable | Not applicable | |
Contact Name
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Sandra Lavorel | Liem Tran | Sven Lautenbach | Yongping Yuan | Steve Jordan | Susan H. Yee | Susan H. Yee | Michelle Ward | M. Abdalla | Justin Bousquin | Arnout van Soesbergen | Monica G. Turner | Mulvaney, Kate | David Otis | Not reported | Ola Lundin | Didier Pont | P. S. E. zu Ermgassen |
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 | Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany | U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | Gulf Ecology Division U.S. Environmental Protection Agency, 1 Sabine Island Drive, Gulf Breeze, Florida 32561 | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | US EPA, Office of Research and Development, National health and environmental Effects Lab, Gulf Ecology Division, Gulf Breeze, FL 32561 | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | Not reported | US EPA, ORD, NHEERL, Atlantic Ecology Division, Narragansett, RI | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Not reported | Department of Ecology, Swedish Univ. of Agricultural Sciences, Uppsala, Sweden | 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 Zoology, University of Cambridge, Cambridge CB2 3EJ, UK |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | ltran1@utk.edu | sven.lautenbach@ufz.de | yuan.yongping@epa.gov | steve.jordan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | m.ward@uq.edu.au | abdallm@tcd.ie | bousquin.justin@epa.gov | arnout.van_soesbergen@kcl.ac.uk | turnermg@wisc.edu | None reported | dotis@iastate.edu | Not reported | ola.lundin@slu.se | didier.pont@cemagref.fr | philine.zuermgassen@cantab.net |
EM ID
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EM-80 | EM-91 | EM-94 | EM-97 | EM-196 | EM-428 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-630 | EM-654 | EM-683 | EM-703 |
EM-713 ![]() |
EM-779 ![]() |
EM-821 ![]() |
EM-905 ![]() |
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 Agronomic ecosystem service map is a simple sum 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 agronomic ecosystem services are based on stakeholders’ perceptions, given positive or negative 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) | AUTHOR'S DESCRIPTION: "We used a spatially explicit model to predict the potential exposure of small streams to insecticides (run-off potential – RP) as well as the resulting ecological risk (ER) for freshwater fauna on the European scale (Schriever and Liess 2007; Kattwinkel et al. 2011)...The recovery of community structure after exposure to insecticides is facilitated by the presence of undisturbed upstream stretches that can act as sources for recolonization (Niemi et al. 1990; Hatakeyama and Yokoyama 1997). In the absence of such sources for recolonization, the structure of the aquatic community at sites that are exposed to insecticides differs significantly from that of reference sites (Liess and von der Ohe 2005)...Hence, we calculated the ER depending on RP for insecticides and the amount of recolonization zones. ER gives the percentage of stream sites in each grid cell (10 × 10 km) in which the composition of the aquatic community deviated from that of good ecological status according to the WFD. In a second step, we estimated the service provided by the environment comparing the ER of a landscape lacking completely recolonization sources with that of the actual landscape configuration. Hence, the ES provided by non-arable areas (forests, pastures, natural grasslands, moors and heathlands) was calculated as the reduction of ER for sensitive species. The service can be thought of as a habitat provisioning/nursery service that leads to an improvement of ecological water quality." | AUTHORS' DESCRIPTION: "AnnAGNPS is an advanced simulation model developed by the USDA-ARS and Natural Resource Conservation Services (NRCS) to help evaluate watershed response to agricultural management practices. It is a continuous simulation, daily time step, pollutant loading model designed to simulate water, sediment and chemical movement from agricultural watersheds.p. 198" | ABSTRACT: "We compiled published data from wetland studies worldwide to estimate total Nr removal and to evaluate factors that influence removal rates. Over several orders of magnitude in wetland area and Nr loading rates, there is a positive, near-linear relationship between Nr removal and Nr loading. The linear model (null hypothesis) explains the data better than either a model of declining Nr removal efficiency with increasing Nr loading, or a Michaelis–Menten (saturation) model." | AUTHOR'S DESCRIPTION: "In total, 19 ecosystem services metrics were identified as relevant to stakeholder objectives in the Guánica Bay watershed identified during the 2013 Public Values Forum (Table 2)...Ecological production functions were applied to translate LULC measures of ecosystem condition to supply of ecosystem services…The volume of retained rainwater per unit area (in^3/in^2) includes both the maximum soil moisture retention and the initial abstraction of water before runoff due to infiltration, evaporation, or interception by vegetation…" | 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:...(5) value of finfish," | AUTHOR'S DESCRIPTION: "Here we develop a method for assessing future scenarios of environmental management change that improve coastal ecosystem services and thereby, support the success of the SDGs. We illustrate application of the method using a case study of South Africa’s West Coast Rock Lobster fishery within the Table Mountain National Park (TMNP) Marine Protected Area...We calculated the retrospective and current value of the West Coast Rock Lobster fishery using published and unpublished data from various sources and combined the market worth of landed lobster from recreational fishers, small-scale fisheries (SSF), large-scale fisheries (LSF) and poachers. Then using the InVEST tool, we combined data to build scenarios that describe possible futures for the West Coast Rock Lobster fishery (see Table 1). The first scenario, entitled ‘Business as Usual’ (BAU), takes the current situation and most up-to-date data to model the future if harvest continues at the existing rate. The second scenario is entitled ‘Redirect the Poachers’ (RP), which attempts to model implementation of strict management, whereby poaching is minimised from the Marine Protected Area and other economic and nutritional sources are made available through government initiatives. The third scenario, entitled ‘Large Scale Cutbacks’ (LSC), excludes large-scale fisheries from harvesting West Coast Rock Lobster within the TMNP Marine Protected Area." | Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. | AUTHOR DESCRIPTION: "The Rapid Benefits Indicators (RBI) approach consists of five steps and is outlined in Assessing the Benefits of Wetland Restoration – A Rapid Benefits Indicators Approach for Decision Makers, hereafter referred to as the “guide.” The guide presents the assessment approach, detailing each step of the indicator development process and providing an example application in the “Step in Action” pages. The spatial analysis toolset is intended to be used to analyze existing spatial information to produce metrics for many of the indicators developed in that guide. This spatial analysis toolset manual gives directions on the mechanics of the tool and its data requirements, but does not detail the reasoning behind the indicators and how to use results of the assessment; this information is found in the guide. " | ABSTRACT: "Water resources in the Santa basin in the Peruvian Andes are increasingly under pressure from climate change and population increases. Impacts of temperature-driven glacier retreat on stream flow are better studied than those from precipitation changes, yet present and future water resources are mostly dependent on precipitation which is more difficult to predict with climate models. This study combines a broad range of projections from climate models with a hydrological model (WaterWorld), showing a general trend towards an increase in water availability due to precipitation increases over the basin. However, high uncertainties in these projections necessitate the need for basin-wide policies aimed at increased adaptability." AUTHOR'S DESCRIPTION: "WaterWorld is a fully distributed, process-based hydrological model that utilises remotely sensed and globally available datasets to support hydrological analysis and decision-making at national and local scales globally, with a particular focus on un-gauged and/or data-poor environments, which makes it highly suited to this study. The model (version 2) currently runs on either 10 degree tiles, large river basins or countries at 1-km2 resolution or 1 degree tiles at 1-ha resolution utilising different datasets. It simulates a hydrological baseline as a mean for the period 1950-2000 and can be used to calculate the hydrological impact of scenarios of climate change, land use change, land management options, impacts of extractives (oil & gas and mining) and impacts of changes in population and demography as well as combinations of these. The model is ‘self parameterising’ (Mulligan, 2013a) in the sense that all data required for model application anywhere in the world is provided with the model, removing a key barrier to model application. However, if users have better data than those provided, it is possible to upload these to WaterWorld as GIS files and use them instead. Results can be viewed visually within the web browser or downloaded as GIS maps. The model’s equations and processes are described in more detail in Mulligan and Burke (2005) and Mulligan (2013b). The model parameters are not routinely calibrated to observed flows as it is designed for hydrological scenario analysis in which the physical basis of its parameters must be retained and the model is also often used in un-gauged basins. Calibration is inappropriate under these circumstances (Sivapalan et al., 2003). The freely available nature of the model means that anyone can apply it and replicate the results shown here. WaterWorld’s (V2) snow and ice module is capable of simulating the processes of melt water production, snow fall and snow pack, making this version highly suited to the current application. The model component is based on a full energy-balance for snow accumulation and melting based on Walter et al., (2005) with input data provided globally by the SimTerra database (Mulligan, 2011) upon which the model r | AUTHOR'S DESCRIPTION (from Supporting Information): "Forest recreation service as a function of the amount of forest habitat, recreational opportunities provided, proximity to population center, and accessibility of the area. Several assumptions were made for this assessment approach: larger areas and places with more recreational opportunities would provide more recreational service; areas near large population centers would be visited and used more than remote areas; and proximity to major roads would increase access and thus recreational use of an area… we quantified forest recreation service for each 30-m grid cells using the equation below: FRSi = Ai Σ(Oppti + Popi +Roadi), where FRS is forest recreation score, A is the area of forest habitat, Oppt represents the recreation opportunities, Pop is the proximity to population centers, and Road stands for the distance to major roads. To simplify interpretation, we rescaled the original forest recreation score (ranging from 0 to 5,200) to a range of 0–100, with 0 representing no forest recreation service and 100 representing highest service. | [ABSTRACT: "Estimates of the types and number of recreational users visiting an estuary are critical data for quantifying the value of recreation and how that value might change with variations in water quality or other management decisions. However, estimates of recreational use are minimal and conventional intercept surveys methods are often infeasible for widespread application to estuaries. Therefore, a practical observational sampling approach was developed to quantify the recreational use of an estuary without the use of surveys. Designed to be simple and fast to allow for replication, the methods involved the use of periodic instantaneous car counts multiplied by extrapolation factors derived from all-day counts. This simple sampling approach can be used to estimate visitation to diverse types of access points on an estuary in a single day as well as across multiple days. Evaluation of this method showed that when periodic counts were taken within a preferred time window (from 11am-4:30pm), the estimates were within 44 percent of actual daily visitation. These methods were applied to the Three Bays estuary system on Cape Cod, USA. The estimated combined use across all its public access sites is similar to the use at a mid-sized coastal beach, demonstrating the value of estuarine systems. Further, this study is the first to quantify the variety and magnitude of recreational uses at several different types of access points throughout the estuary using observational methods. This work can be transferred to the many small coastal access points used for recreation across New England and beyond." ] | 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: "The 2015 announcement of The Dow Chemical Company's (Dow) Valuing Nature Goal, which aims to identify $1 billion in business value from projects that are better for nature, gives nature a spot at the project design table. To support this goal, Dow and The Nature Conservancy have extended their long-standing collaboration and are now working to develop a defensible methodology to support the implementation of the goal. This paper reviews the nature valuation methodology framework developed by the Collaboration in support of the goal. The nature valuation methodology is a three-step process that engages Dow project managers at multiple stages in the project design and capital allocation processes. The three-step process identifies projects that may have a large impact on nature and then promotes the use of ecosystem service tools, such as the Ecosystem Services Identification and Inventory Tool, to enhance the project design so that it better supports ecosystem health. After reviewing the nature valuation methodology, we describe the results from a case study of redevelopment plans for a 23-acre site adjacent to Dow's Michigan Operations plant along the Tittabawassee River." AUTHOR'S DESCRIPTION: "The ESII Tool measures the environmental impact or proposed land changes through eight specific ecosystem services: (i) water provisioning, (ii) air quality control (nitrogen and particulate removal), (iii) climate regulation (carbon uptake and localized air temperature regulation), (iv) erosion regulation, (v) water quality control (nitrogen and filtration), (vi) water temperature regulation, (vii) water quantity control, and (viii) aesthetics (noise and visual). The ESII Tool allows for direct comparison of the performance of these eight ecosystem services both across project sites and across project design proposals within a site." "The team was also asked to use an iterative design process using the ESII Tool to create alternative restoration scenarios…The project team developed three alternative restoration designs: i) standard brownfield restoration (i.e., cap and plant grass) on the ash pond and 4-D property (referred to as SBR); ii) ecological restoration (i.e., excavate ash and associated soil for secured disposal in approved landfill and restore historic forest, prairie, wetland) of the ash pond only, with SBR on the 4-D property (referred to as ER); and iii) ecological restoration on the ash pond and 4- D property (referred to as ER+)." | ABSTRACT: " Plant species differed in attractiveness for each arthropod functional group. Floral area of the focal plant species positively affected honeybee, predator, and parasitic wasp attractiveness. Later bloom period was associated with lower numbers of parasitic wasps. Flower type (actinomorphic, composite, or zygomorphic) predicted attractiveness for honeybees, which preferred actinomorphic over composite flowers and for parasitic wasps, which preferred composite flowers over actinomorphic flowers. 4. Across plant species, herbivore, predator, and parasitic wasp abundances were positively correlated, and honeybee abundance correlated negatively to herbivore abundance. 5. Synthesis and applications. We use data from our common garden experiment to inform evidence-based selection of plants that support pollinators and natural enemies without enhancing potential pests. We recommend selecting plant species with a high floral area per ground area unit, as this metric predicts the abundances of several groups of beneficial arthropods. Multiple correlations between functionally important arthropod groups across plant species stress the importance of a multifunctional approach to arthropod habitat management. " Changes in arthropod abundance were estimated for flower type (entered as separate runs); Actinomorphic, Composite, Zygomorphic. 43 plant species evaluated included Amsinckia intermedia, Calandrinia menziesii, Nemophila maculata, Nemophila menziesii, Phacelia ciliata, Achillea millefolium, Collinsia heterophylla, Fagopyrum esculentum, Lasthenia fremontii, Lasthenia glabrata, Limnanthes alba, Lupinus microcarpus densiflorus, Lupinus succelentus, Phacelia californica, Phacelia campanularia, Phacelia tanacetifolia, Salvia columbariae, Sphaeralcea ambigua, Trifolium fucatum, Trifolium gracilentum, Antirrhinum conutum, Clarkia purpurea, Clarkia unguiculata, Clarkia williamsonii, Eriophyllum lanatum, Eschscholzia californica, Monardella villosa, Scrophularia californica, Asclepia eriocarpa, Asclepia fascicularis, Camissoniopsis Cheiranthifolia, Eriogonum fasciculatum, Gilia capitata, Grindelia camporum, Helianthus annuus, Lupinus formosus, Malacothrix saxatilis, Oenothera elata, Helianthus bolanderi, Helianthus californicus, Madia elegans, Trichostema lanceolatum, Heterotheca grandiflora." | 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 oyster habitat in the USA is a valuable resource that has suffered significant declines over the past century. While this loss of habitat is well documented, the loss of associated ecosystem services remains poorly quantified. Meanwhile, ecosystem service recovery has become a major impetus for restoration. Here we propose a model for estimating the volume of water filtered by oyster populations under field conditions and make estimates of the contribution of past (c. 1880–1910) and present (c. 2000– 2010) oyster populations to improving water quality in 13 US estuaries…" |
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
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None identified | Not reported | European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | Not reported | None identified | Meeting water demands for agriculture and domestic purposes. | None identified | Future rock lobster fisheries management | climate change | None identified | None identified | None identified | None identified | None identified | Use ESII to answer the following business decision question: how can Dow close the ash pond while enhancing ecosystem services to Dow and the community and creating local habitat, for a lesser overall cost to Dow than the option currently defined? | None reported | None reported | None identified |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | No additional description provided | Not applicable | Upper Mississipi River basin, elevation 142-194m, | Estuarine Emergent; Agricultural; Salt Marsh; Palustrine Emergent; Palustrine Forested | No additional descriptions provided | No additional description provided | No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | wetlands | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | No additional description provided | None identified | Prairie Pothole Region of Iowa | No additional description provided | Mediteranean climate | Wadeable and boatable streams in 12 western USA states | No additional description provided |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | No scenarios presented | No scenarios presented | No scenarios presented | Fisheries exploitation; fishing vulnerability (of age classes) | fertilization | N/A | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | No scenarios presented | N/A | No scenarios presented | Alternative restoration designs | Arthropod groups | not applicable | No scenarios presented |
EM ID
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EM-80 | EM-91 | EM-94 | EM-97 | EM-196 | EM-428 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-630 | EM-654 | EM-683 | EM-703 |
EM-713 ![]() |
EM-779 ![]() |
EM-821 ![]() |
EM-905 ![]() |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method + Application (multiple runs exist) | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application View EM Runs | Method + Application (multiple runs exist) View EM Runs |
New or Pre-existing EM?
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New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | New or revised model | Application of existing 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 |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-80 | EM-91 | EM-94 | EM-97 | EM-196 | EM-428 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-630 | EM-654 | EM-683 | EM-703 |
EM-713 ![]() |
EM-779 ![]() |
EM-821 ![]() |
EM-905 ![]() |
Document ID for related EM
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Doc-260 | Doc-270 | Doc-123 |
Doc-254 | Doc-256 ?Comment:Document 254 was also used as a source document for this EM |
Doc-142 | None | None | None | None | None | None | None | None | None | Doc-372 | Doc-373 | Doc-391 | None | Doc-403 | 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-81 | EM-82 | EM-83 | None | None | None | None | None | None | None | EM-593 | None | None | None | EM-682 | EM-684 | EM-685 | EM-705 | EM-704 | EM-702 | EM-701 | EM-700 | EM-632 | EM-712 | None | EM-820 | EM-826 | None |
EM Modeling Approach
EM ID
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EM-80 | EM-91 | EM-94 | EM-97 | EM-196 | EM-428 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-630 | EM-654 | EM-683 | EM-703 |
EM-713 ![]() |
EM-779 ![]() |
EM-821 ![]() |
EM-905 ![]() |
EM Temporal Extent
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Not reported | 1987-1997 | 2000 | 1980-2006 | 2004 | 2006 - 2012 | 2006-2007, 2010 | 1986-2115 | 1961-1990 | Not applicable | 1950-2071 | 2000-2006 | Summer 2017 | 1987-2007 | Not reported | 2015-2016 | 2004-2005 | 1880-1910; 2000-2010 |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | both | Not applicable | both | Not applicable | past time | Not applicable | Not applicable | Not applicable | past time | past time |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | 1 | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Day | Not applicable | Month | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Month |
EM ID
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EM-80 | EM-91 | EM-94 | EM-97 | EM-196 | EM-428 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-630 | EM-654 | EM-683 | EM-703 |
EM-713 ![]() |
EM-779 ![]() |
EM-821 ![]() |
EM-905 ![]() |
Bounding Type
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Physiographic or Ecological | Watershed/Catchment/HUC | Geopolitical | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical | Point or points | Not applicable | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Point or points | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) |
Spatial Extent Name
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Central French Alps | Upper Mississippi River basin; St. Croix River Watershed | EU-27 | East Fork Kaskaskia River watershed basin | Contiguous U.S. | Guanica Bay watershed | Coastal zone surrounding St. Croix | Table Mountain National Park Marine Protected Area | Oak Park Research centre | Not applicable | Santa Basin | Yahara Watershed, Wisconsin | Three Bays, Cape Cod | CREP (Conservation Reserve Enhancement Program | Dow Midland Operations facility ash pond and Posey Riverside (4-D property) | Harry Laidlaw Jr. Honey Bee Research facility | Western 12 states | East Coast and Gulf of Mexico U. S. estuaries |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 100,000-1,000,000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 100-1000 km^2 | 1-10 ha | Not applicable | 10,000-100,000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | 10,000-100,000 km^2 | 10-100 ha | <1 ha | >1,000,000 km^2 | 10,000-100,000 km^2 |
EM ID
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EM-80 | EM-91 | EM-94 | EM-97 | EM-196 | EM-428 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-630 | EM-654 | EM-683 | EM-703 |
EM-713 ![]() |
EM-779 ![]() |
EM-821 ![]() |
EM-905 ![]() |
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 lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all 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 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) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | NHDplus v1 | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | 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) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable |
Spatial Grain Size
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20 m x 20 m | NHDplus v1 | 10 km x 10 km | 1 km^2 | Not applicable | 30 m x 30 m | 10 m x 10 m | Not applicable | Not applicable | Not reported | 1 km2 | 30m x 30m | beach length | multiple, individual, irregular sites | map unit | Not applicable | stream reach | Not applicable |
EM ID
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EM-80 | EM-91 | EM-94 | EM-97 | EM-196 | EM-428 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-630 | EM-654 | EM-683 | EM-703 |
EM-713 ![]() |
EM-779 ![]() |
EM-821 ![]() |
EM-905 ![]() |
EM Computational Approach
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Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | * | Analytic | Numeric | Analytic | Analytic | Numeric | Analytic | Analytic |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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None |
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EM ID
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EM-80 | EM-91 | EM-94 | EM-97 | EM-196 | EM-428 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-630 | EM-654 | EM-683 | EM-703 |
EM-713 ![]() |
EM-779 ![]() |
EM-821 ![]() |
EM-905 ![]() |
Model Calibration Reported?
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No | Yes | No | No | Yes | No | Yes | No | Yes | Not applicable | No | No | Yes | Unclear | Unclear | Not applicable | No | No |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | Yes | No | No | Yes | No | No | No |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
Not applicable | No | No | No | No | No | Not applicable | No | No |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None |
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None | None |
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None | None | None |
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None | None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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No | No | Yes | Yes | No | No | Yes |
Yes ?Comment:A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data. |
Yes | Not applicable | Yes | No | No | No | Unclear | Not applicable |
Yes ?Comment:Compared to another journal manuscript IBI scores (Whittier et al) |
No |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | Yes | Yes | No | No | No | No | Not applicable | No | No | No | No | No | No | No | No |
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. |
No | Unclear | Yes | No | No | No | No | Not applicable | No | No | No | No | No | No | Yes | No |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Yes | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Yes | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-80 | EM-91 | EM-94 | EM-97 | EM-196 | EM-428 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-630 | EM-654 | EM-683 | EM-703 |
EM-713 ![]() |
EM-779 ![]() |
EM-821 ![]() |
EM-905 ![]() |
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None |
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None | None |
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None | None |
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None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-80 | EM-91 | EM-94 | EM-97 | EM-196 | EM-428 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-630 | EM-654 | EM-683 | EM-703 |
EM-713 ![]() |
EM-779 ![]() |
EM-821 ![]() |
EM-905 ![]() |
None | None | None | None | None | None |
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None | None | None | None |
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None | None | None | None |
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Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-80 | EM-91 | EM-94 | EM-97 | EM-196 | EM-428 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-630 | EM-654 | EM-683 | EM-703 |
EM-713 ![]() |
EM-779 ![]() |
EM-821 ![]() |
EM-905 ![]() |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | 42.5 | 50.53 | 38.69 | -9999 | 17.96 | 17.73 | -34.18 | 52.86 | Not applicable | -9.05 | 43.1 | 41.62 | 42.62 | 43.6 | 38.54 | 44.2 | 30.33 |
Centroid Longitude
em.detail.ddLongHelp
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6.4 | -90.63 | 7.6 | -89.1 | -9999 | -67.02 | -64.77 | 18.35 | 6.54 | Not applicable | -77.81 | -89.4 | -70.42 | -93.84 | -84.24 | -121.79 | -113.07 | -81.6 |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | None provided | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Estimated | Estimated | Provided | Not applicable | Estimated | Estimated | Provided | Provided | Not applicable | Estimated | Provided | Estimated | Estimated | Estimated | Provided | Estimated | Estimated |
EM ID
em.detail.idHelp
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EM-80 | EM-91 | EM-94 | EM-97 | EM-196 | EM-428 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-630 | EM-654 | EM-683 | EM-703 |
EM-713 ![]() |
EM-779 ![]() |
EM-821 ![]() |
EM-905 ![]() |
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 | Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Agroecosystems | Inland Wetlands | Near Coastal Marine and Estuarine | Inland Wetlands | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Agroecosystems | Inland Wetlands | None | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Near Coastal Marine and Estuarine | Inland Wetlands | Agroecosystems | Grasslands | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Rivers and Streams | Near Coastal Marine and Estuarine |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows. | None | Streams and near upstream environments | Row crop agriculture in Kaskaskia river basin | Wetlands (multiple types) | 13 LULC were used | Coral reefs | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | farm pasture | Restored wetlands | tropical, coastal to montane | Mixed environment watershed of prairie converted to predominantly agriculture and urban landscape | Beaches | Wetlands buffered by grassland within agroecosystems | Ash pond and surrounding environment | Agricultural fields | wadeable and boatable streams | Estuarine |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is coarser than that of the Environmental Sub-class | Ecosystem | Ecological scale is coarser 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 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 is finer than that of the Environmental Sub-class | Other or unclear (comment) | 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 is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-80 | EM-91 | EM-94 | EM-97 | EM-196 | EM-428 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-630 | EM-654 | EM-683 | EM-703 |
EM-713 ![]() |
EM-779 ![]() |
EM-821 ![]() |
EM-905 ![]() |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Individual or population, within a species | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Guild or Assemblage | Guild or Assemblage | Individual or population, within a species |
Taxonomic level and name of organisms or groups identified
EM-80 | EM-91 | EM-94 | EM-97 | EM-196 | EM-428 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-630 | EM-654 | EM-683 | EM-703 |
EM-713 ![]() |
EM-779 ![]() |
EM-821 ![]() |
EM-905 ![]() |
None Available | None Available | None Available | None Available | None Available | None Available |
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None Available | None Available | None Available | None Available | None Available |
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None Available |
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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-80 | EM-91 | EM-94 | EM-97 | EM-196 | EM-428 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-630 | EM-654 | EM-683 | EM-703 |
EM-713 ![]() |
EM-779 ![]() |
EM-821 ![]() |
EM-905 ![]() |
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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-80 | EM-91 | EM-94 | EM-97 | EM-196 | EM-428 | EM-462 |
EM-541 ![]() |
EM-598 | EM-617 | EM-630 | EM-654 | EM-683 | EM-703 |
EM-713 ![]() |
EM-779 ![]() |
EM-821 ![]() |
EM-905 ![]() |
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None | None |
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None |
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None | None |
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None |