EcoService Models Library (ESML)
loading
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
em.detail.idHelp
?
|
EM-51 ![]() |
EM-66 | EM-69 | EM-70 | EM-71 | EM-94 |
EM-125 ![]() |
EM-193 | EM-437 |
EM-541 ![]() |
EM-549 | EM-617 | EM-629 | EM-648 | EM-683 |
EM-821 ![]() |
EM-837 | EM-844 |
EM Short Name
em.detail.shortNameHelp
?
|
EnviroAtlas-Nat. filtration-water | Litter biomass production, Central French Alps | Soil carbon content, Central French Alps | Plant species diversity, Central French Alps | Community flowering date, Central French Alps | Reduction in pesticide runoff risk, Europe | Land-use change and recreation, Europe | Cultural ecosystem services, Bilbao, Spain | InVESTv3.0 Water yield, Guánica Bay, Puerto Rico | InVEST fisheries, lobster, South Africa | Nutrient Tracking Tool (NTT) | RBI Spatial Analysis Method | SolVES, Pike & San Isabel NF, WY | Bobolink density, CREP, Iowa, USA | Estuary visitation, Cape Cod, MA | Aquatic vertebrate IBI for Western streams, USA | Bird species diversity on restored landfills, UK | Common yellowthroat abun, Piedmont region, USA |
EM Full Name
em.detail.fullNameHelp
?
|
US EPA EnviroAtlas - Natural filtration (of water by tree cover); Example is shown for Durham NC and vicinity, USA | Litter biomass production, Central French Alps | Soil carbon content, Central French Alps | Plant species diversity, Central French Alps | Community weighted mean flowering date, Central French Alps | Reduction in pesticide runoff risk, Europe | Land-use change effects on recreation, Europe | Cultural ecosystem services, Bilbao, Spain | InVEST (Integrated Valuation of Environmental Services and Tradeoffs) v3.0 Water yield, Guánica Bay, Puerto Rico, USA | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa | Nutrient Tracking Tool (NTT) | Rapid Benefit Indicator (RBI) Spatial Analysis Toolset Method | SolVES, Social Values for Ecosystem Services, Pike and San Isabel National Forest, CO | Bobolink population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Value of recreational use of an estuary, Cape Cod, Massachusetts | Development of an aquatic vertebrate index of biotic integrity (IBI) for Western streams, USA | Bird species diversity on restored landfills compared to paired reference sites, East Midlands, UK | Common yellowthroat abundance, Piedmont ecoregion, USA |
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Hydro model. |
EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | EU Biodiversity Action 5 |
None ?Comment:EU Mapping Studies |
US EPA | InVEST | InVEST | None | None | None | None | US EPA | None | None | None |
EM Source Document ID
|
223 | 260 | 260 | 260 | 260 | 255 | 228 | 191 | 338 |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
352 | 367 | 369 | 372 | 387 | 404 | 406 | 405 |
Document Author
em.detail.documentAuthorHelp
?
|
US EPA Office of Research and Development - National Exposure Research Laboratory | 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. | 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. | Lautenbach, S., Maes, J., Kattwinkel, M., Seppelt, R., Strauch, M., Scholz, M., Schulz-Zunkel, C., Volk, M., Weinert, J. and Dormann, C. | Haines-Young, R., Potschin, M. and Kienast, F. | Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby | Saleh, A. and O. Gallego | Bousquin, J., Mazzotta M., and W. Berry | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | 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 | Mulvaney, K K., Atkinson, S.F., Merrill, N.H., Twichell, J.H., and M.J. Mazzotta | Pont, D., Hughes, R.M., Whittier, T.R., and S. Schmutz. | Rahman, M. L., S. Tarrant, D. McCollin, and J. Ollerton | Riffel, S., Scognamillo, D., and L. W. Burger |
Document Year
em.detail.documentYearHelp
?
|
2013 | 2011 | 2011 | 2011 | 2011 | 2012 | 2012 | 2013 | 2017 | 2018 | 2018 | 2017 | 2014 | 2010 | 2019 | 2009 | 2011 | 2008 |
Document Title
em.detail.sourceIdHelp
?
|
EnviroAtlas - Featured Community | 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 | 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 | Mapping water quality-related ecosystem services: concepts and applications for nitrogen retention and pesticide risk reduction | 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 | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals | Nutrient Tracking Tool (NTT) User Manual | Rapid Benefit Indicators (RBI) Spatial Analysis Toolset - Manual. | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Quantifying Recreational Use of an Estuary: A case study of three bays, Cape Cod, USA | A Predictive Index of Biotic Integrity Model for A predictive index of biotic integrity model foraquatic-vertebrate assemblages of Western U.S. Streams | The conservation value of restored landfill sites in the East Midlands, UK for supporting bird communities in the East Midlands, UK for supporting bird communities | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds |
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 but unpublished (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
em.detail.commentsOnStatusHelp
?
|
Published on US EPA EnviroAtlas website | 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 | webpage | Published EPA report | Published journal manuscript | Published report | Draft manuscript-work progressing | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
em.detail.idHelp
?
|
EM-51 ![]() |
EM-66 | EM-69 | EM-70 | EM-71 | EM-94 |
EM-125 ![]() |
EM-193 | EM-437 |
EM-541 ![]() |
EM-549 | EM-617 | EM-629 | EM-648 | EM-683 |
EM-821 ![]() |
EM-837 | EM-844 |
https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://www.naturalcapitalproject.org/invest/ | https://www.naturalcapitalproject.org/invest/ | http://ntt.tiaer.tarleton.edu/welcomes/new?locale=en | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | |
Contact Name
em.detail.contactNameHelp
?
|
EnviroAtlas Team | Sandra Lavorel | Sandra Lavorel | Sandra Lavorel | Sandra Lavorel | Sven Lautenbach | Marion Potschin | Izaskun Casado-Arzuaga | Susan H. Yee | Michelle Ward |
Ali Saleh ?Comment:Phone # 254-968-9079 |
Justin Bousquin | Benson Sherrouse | David Otis | Mulvaney, Kate | Didier Pont | Lutfor Rahman | Sam Riffell |
Contact Address
|
Not reported | 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 | 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 | Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany | 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 | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia | Associate Director, Texas Institute for Applied Environmental Research, P.O. Box T410, Tarleton State University Stephenville, TX 76402 | US EPA, Office of Research and Development, National health and environmental Effects Lab, Gulf Ecology Division, Gulf Breeze, FL 32561 | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | US EPA, ORD, NHEERL, Atlantic Ecology Division, Narragansett, RI | 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 | Landscape and Biodiversity Research Group, School of Science and Technology, The University of Northampton, Avenue Campus, Northampton NN2 6JD, UK | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA |
Contact Email
|
enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sven.lautenbach@ufz.de | marion.potschin@nottingham.ac.uk | izaskun.casado@ehu.es | yee.susan@epa.gov | m.ward@uq.edu.au | saleh@tarleton.edu | bousquin.justin@epa.gov | bcsherrouse@usgs.gov | dotis@iastate.edu | None reported | didier.pont@cemagref.fr | lutfor.rahman@northampton.ac.uk | sriffell@cfr.msstate.edu |
EM ID
em.detail.idHelp
?
|
EM-51 ![]() |
EM-66 | EM-69 | EM-70 | EM-71 | EM-94 |
EM-125 ![]() |
EM-193 | EM-437 |
EM-541 ![]() |
EM-549 | EM-617 | EM-629 | EM-648 | EM-683 |
EM-821 ![]() |
EM-837 | EM-844 |
Summary Description
em.detail.summaryDescriptionHelp
?
|
The Natural Filtration model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. METADATA ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina... runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA DESCRIPTION: "The i-Tree Hydro model estimates the effects of tree and impervious cover on hourly stream flow values for a watershed (Wang et al 2008). i-Tree Hydro also estimates changes in water quality using hourly runoff estimates and mean and median national event mean concentration (EMC) values. The model was calibrated using hourly stream flow data to yield the best fit between model and measured stream flow results… After calibration, the model was run a number of times under various conditions to see how the stream flow would respond given varying tree and impervious cover in the watershed… The term event mean concentration (EMC) is a statistical parameter used to represent the flow-proportional average concentration of a given parameter during a storm event. EMC data is used for estimating pollutant loading into watersheds. The response outputs were calculated as kg of pollutant per square meter of land area for pollutants. These per square meter values were multiplied by the square meters of land area in the block group to estimate the effects at the block group level." METADATA DESCRIPTION PARAPHRASED: Changes in water quality were estimated for the following pollutants (entered as separate runs); total suspended solids (TSS), total phosphorus, soluble phosphorus, nitrites and nitrates, total Kjeldahl nitrogen (TKN), biochemical oxygen demand (BOD5), chemical oxygen demand (COD5), and copper. "Reduction in annual runoff (census block group)" variable data was derived from the EnviroAtlas water recharge coverage which used the i-Tree Hydro model. | 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., litter biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in litter 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…Litter biomass production for each pixel was calculated and mapped using model estimates...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 litter mass. 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." | 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, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in soil carbon 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…Soil carbon for each pixel was calculated and mapped using model estimates...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 soil carbon. 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." | 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: "Simpson species diversity was modelled using the LU + abiotic [land use and all abiotic variables] model given that functional diversity should be a consequence of species diversity rather than the reverse (Lepsˇ et al. 2006)…Species diversity for each pixel was calculated and mapped using model estimates for effects of land use types, and for regression coefficients on abiotic variables. For each pixel these calculations were applied to mapped estimates of abiotic variables." | 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." | 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." | 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 (Recreation); 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: " 'Recreation' is broadly defined as all areas where landscape properties are favourable for active recreation purposes….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." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "Stakeholders identified an objective of meeting water demands for agriculture and domestic purposes, including irrigation, drinking water, or hydropower production…Geomorphology, climate, and vegetation determine the amount of water runoff from the landscape that could be available for consumptive uses. Long-term average water yield was estimated for each HUC12 sub-watershed as the difference between total precipitation and the amount absorbed by the different land cover classes using a reservoir hydropower production model (InVEST 3.0.0; Tallis et al., 2013)." | 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." | AUTHOR'S DESCRIPTION: "The Nutrient Tracking Tool (NTT) was designed and developed by the Texas Institute for Applied Environmental Research (TIAER), Tarleton State University with funding from USDA Office of Environmental Markets, USDA-NRCS Conservation Innovation Grants program, and various state agencies. NTT is a web-based, site-specific application that estimates nutrient and sediment losses at the field scale or at the small watershed scale. Agricultural producers and land managers can define a number of management scenarios and generate a report showing the expected nutrient loss differences between any selected scenarios for a given field or small watershed. NTT compares agricultural management systems to calculate a change in expected flow, nitrogen, phosphorus, sediment losses, and crop yield. Estimates are made using the APEX model (Williams et al. 2000). Results represent average losses from the field based on 35 years of simulated weather. NTT requires regional soils, climate and site-specific crop management information. NTT currently provides selections for all regions of U.S. and Puerto Rico territory, but it has only been validated for a limited number of states and counties. As validation becomes possible in other parts of the country, parameter files may be updated for additional counties in future versions. There are two versions of new NTT program available: The BASIC version is a user-friendly version of NTT that allows users to estimate N, P and sediment from crop and pasture lands. The Research and Education version of NTT (NTT-RE) was developed for researchers and educational institutes for teaching and training purposes. NTT-RE includes additional functions allowing the user to view and edit soil layers, view crop water and nutrient stresses, and modify and the APEX parameters file for calibration and validation purposes. The data sources and APEX simulations in both versions are identical. For more information regarding NTT, please refer to Saleh et al. (2011 and 2015)." | 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: " "Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders.With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems. | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Bobolink (Dolichonyx oryzivorus)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: BOBO density = e^(-0.8696546 + 0.0180943 * grass400) | [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: "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: "There has been a rapid decline of grassland bird species in the UK over the last four decades. In order to stem declines in biodiversity such as this, mitigation in the form of newly created habitat and restoration of degraded habitats is advocated in the UK biodiversity action plan. One potential restored habitat that could support a number of bird species is re-created grassland on restored landfill sites. However, this potential largely remains unexplored. In this study, birds were counted using point sampling on nine restored landfill sites in the East Midlands region of the UK during 2007 and 2008. The effects of restoration were investigated by examining bird species composition, richness, and abundance in relation to habitat and landscape structure on the landfill sites in comparison to paired reference sites of existing wildlife value. Twelve bird species were found in total and species richness and abundance on restored landfill sites was found to be higher than that of reference sites. Restored landfill sites support both common grassland bird species and also UK Red List bird species such as skylark Alauda arvensis, grey partridge Perdix perdix, lapwing Vanellus vanellus, tree sparrow, Passer montanus, and starling Sturnus vulgaris. Size of the site, percentage of bare soil and amount of adjacent hedgerow were found to be the most influential habitat quality factors for the distribution of most bird species. Presence of open habitat and crop land in the surrounding landscape were also found to have an effect on bird species composition. Management of restored landfill sites should be targeted towards UK Red List bird species since such sites could potentially play a significant role in biodiversity action planning." AUTHOR'S DESCRIPTION: "Mean number of birds from multiple visits were used for data analysis. To analyse the data generalized linear models (GLMs) were constructed to compare local habitat and landscape parameters affecting different species, and to establish which habitat and landscape characteristics explained significant changes in the frequency of occurrence for each species. To ensure analyses focused on resident species, habitat associations were modelled for those seven bird species which were recorded at least three times in the surveys. The analysis was carried out with the software R (R Development Core Team 2003). Nonsignificant predictors (independent variables) were removed in a stepwise manner (least significant factor first). For distribution pattern of bird species, data were initially analysed using detrended correspondence analysis. Redundancy analysis (RDA) was performed on the same data using CANOCO for Windows version 4.0 (ter Braak and Smilauer 2002)." | 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. " |
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
None identified | None identified | None identified | None identified | None identified | European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | None identified | Land management, ecosystem management, response to EU 2020 Biodiversity Strategy | Meeting water demands | Future rock lobster fisheries management | None identified | None identified | None | None identified | None identified | None reported | None identified | None reported |
Biophysical Context
|
No additional description provided | Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Not applicable | No additional description provided | Northern Spain; Bizkaia region | No additional description provided | No additional description provided | No additional description provided | wetlands | Rocky mountain conifer forests | Prairie pothole region of north-central Iowa | None identified | Wadeable and boatable streams in 12 western USA states | The study area covered mainly Northamptonshire and parts of Bedfordshire, Buckinghamshire and Warwickshire, ranging from 51o58’44.74” N to 52o26’42.18” N and 0o27’49.94” W to 1o19’57.67” W. This region has countryside of low, undulating hills separated by valleys and lies entirely within the great belt of scarplands formed by rocks of Jurassic age which stretch across England from Yorkshire to Dorset (Beaver 1943; Sutherland 1995; Wilson 1995). | Conservation Reserve Program lands left to go fallow |
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use change (2000-2030) | No scenarios presented | No scenarios presented | Fisheries exploitation; fishing vulnerability (of age classes) | No scenarios presented | N/A | N/A | No scenarios presented | N/A | not applicable | No scenarios presented | N/A |
EM ID
em.detail.idHelp
?
|
EM-51 ![]() |
EM-66 | EM-69 | EM-70 | EM-71 | EM-94 |
EM-125 ![]() |
EM-193 | EM-437 |
EM-541 ![]() |
EM-549 | EM-617 | EM-629 | EM-648 | EM-683 |
EM-821 ![]() |
EM-837 | EM-844 |
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method Only | Method + Application | Method + Application | Method + Application | Method + Application View EM Runs | Method Only | Method + Application |
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
Application of existing 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 | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
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 Modeling Approach
EM ID
em.detail.idHelp
?
|
EM-51 ![]() |
EM-66 | EM-69 | EM-70 | EM-71 | EM-94 |
EM-125 ![]() |
EM-193 | EM-437 |
EM-541 ![]() |
EM-549 | EM-617 | EM-629 | EM-648 | EM-683 |
EM-821 ![]() |
EM-837 | EM-844 |
EM Temporal Extent
em.detail.tempExtentHelp
?
|
1999-2010 | Not reported | 2007-2009 | 2007-2009 | 2007-2008 | 2000 | 1990-2030 | 2000 - 2007 | 2006 - 2012 | 1986-2115 | 35 yr | Not applicable | 2004-2008 | 2002-2007 | Summer 2017 | 2004-2005 | Not applicable | 2008 |
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-stationary ?Comment:The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, operated on an hourly timestep. The final annual flow parameter however is time stationary. |
time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | past time | past time | Not applicable | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Day | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-51 ![]() |
EM-66 | EM-69 | EM-70 | EM-71 | EM-94 |
EM-125 ![]() |
EM-193 | EM-437 |
EM-541 ![]() |
EM-549 | EM-617 | EM-629 | EM-648 | EM-683 |
EM-821 ![]() |
EM-837 | EM-844 |
Bounding Type
em.detail.boundingTypeHelp
?
|
Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Geopolitical | Not applicable | Not applicable | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Geopolitical | Not applicable | Physiographic or ecological |
Spatial Extent Name
em.detail.extentNameHelp
?
|
Durham, NC and vicinity | Central French Alps | Central French Alps | Central French Alps | Central French Alps | EU-27 | The EU-25 plus Switzerland and Norway | Bilbao Metropolitan Greenbelt | Guanica Bay watershed | Table Mountain National Park Marine Protected Area | Not applicable | Not applicable | National Park | CREP (Conservation Reserve Enhancement Program) wetland sites | Three Bays, Cape Cod | Western 12 states | Not applicable | Piedmont Ecoregion |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
100-1000 km^2 | 10-100 km^2 | 10-100 km^2 | 10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | Not applicable | Not applicable | 1000-10,000 km^2. | 1-10 km^2 | 1000-10,000 km^2. | >1,000,000 km^2 | Not applicable | 100,000-1,000,000 km^2 |
EM ID
em.detail.idHelp
?
|
EM-51 ![]() |
EM-66 | EM-69 | EM-70 | EM-71 | EM-94 |
EM-125 ![]() |
EM-193 | EM-437 |
EM-541 ![]() |
EM-549 | EM-617 | EM-629 | EM-648 | EM-683 |
EM-821 ![]() |
EM-837 | EM-844 |
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 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 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) ?Comment:871 total sites surveyed for this work |
spatially distributed (in at least some cases) | spatially lumped (in all cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | 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 | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | 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 |
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
irregular | 20 m x 20 m | 20 m x 20 m | 20 m x 20 m | 20 m x 20 m | 10 km x 10 km | 1 km x 1 km | 2 m x 2 m | 30 m x 30 m | Not applicable | Not applicable | Not reported | 30m2 | multiple, individual, irregular shaped sites | beach length | stream reach | multiple unrelated sites | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-51 ![]() |
EM-66 | EM-69 | EM-70 | EM-71 | EM-94 |
EM-125 ![]() |
EM-193 | EM-437 |
EM-541 ![]() |
EM-549 | EM-617 | EM-629 | EM-648 | EM-683 |
EM-821 ![]() |
EM-837 | EM-844 |
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Analytic ?Comment:The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, was numeric. The final parameter however did not require iteration. |
Analytic | Analytic | Analytic | Analytic | Analytic | Logic- or rule-based | Analytic | Numeric | Numeric | Numeric | Analytic | Numeric | Analytic | Numeric | 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
?
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
EM ID
em.detail.idHelp
?
|
EM-51 ![]() |
EM-66 | EM-69 | EM-70 | EM-71 | EM-94 |
EM-125 ![]() |
EM-193 | EM-437 |
EM-541 ![]() |
EM-549 | EM-617 | EM-629 | EM-648 | EM-683 |
EM-821 ![]() |
EM-837 | EM-844 |
Model Calibration Reported?
em.detail.calibrationHelp
?
|
Unclear | No | No | No | No | No | No | No | No | No | Not applicable | Not applicable | No | Unclear | Yes | No | Not applicable | Yes |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
No | Yes | Yes | Yes | Yes | No | No | No | No | No | Not applicable | Not applicable | Yes | No | No | No | Not applicable | No |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
None |
|
|
|
|
None | None | None | None | None | None | None |
|
None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
?
|
Unclear | Yes | Yes | No | No | Yes | No | Yes | No |
Yes ?Comment:A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data. |
Unclear | Not applicable | No | Unclear | No |
Yes ?Comment:Compared to another journal manuscript IBI scores (Whittier et al) |
Not applicable | No |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
Unclear | No | No | No | No | No | No | No | No | No | Not applicable | Not applicable | No | No | No | No | Not applicable | No |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
Unclear | No | No | No | No | No | No | No | No | No | Not applicable | Not applicable | No | No | No | Yes | Not applicable | Yes |
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 | Not applicable | Not applicable | Not applicable | Not applicable | Yes | Not applicable | Unclear |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-51 ![]() |
EM-66 | EM-69 | EM-70 | EM-71 | EM-94 |
EM-125 ![]() |
EM-193 | EM-437 |
EM-541 ![]() |
EM-549 | EM-617 | EM-629 | EM-648 | EM-683 |
EM-821 ![]() |
EM-837 | EM-844 |
|
|
|
|
|
|
|
|
|
None | None | None |
|
|
None |
|
None |
|
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-51 ![]() |
EM-66 | EM-69 | EM-70 | EM-71 | EM-94 |
EM-125 ![]() |
EM-193 | EM-437 |
EM-541 ![]() |
EM-549 | EM-617 | EM-629 | EM-648 | EM-683 |
EM-821 ![]() |
EM-837 | EM-844 |
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-51 ![]() |
EM-66 | EM-69 | EM-70 | EM-71 | EM-94 |
EM-125 ![]() |
EM-193 | EM-437 |
EM-541 ![]() |
EM-549 | EM-617 | EM-629 | EM-648 | EM-683 |
EM-821 ![]() |
EM-837 | EM-844 |
Centroid Latitude
em.detail.ddLatHelp
?
|
35.99 | 45.05 | 45.05 | 45.05 | 45.05 | 50.53 | 50.53 | 43.25 | 17.96 | -34.18 | Not applicable | Not applicable | 38.7 | 42.62 | 41.62 | 44.2 | Not applicable | 36.23 |
Centroid Longitude
em.detail.ddLongHelp
?
|
-78.96 | 6.4 | 6.4 | 6.4 | 6.4 | 7.6 | 7.6 | -2.92 | -67.02 | 18.35 | Not applicable | Not applicable | 105.89 | -93.84 | -70.42 | -113.07 | Not applicable | -81.9 |
Centroid Datum
em.detail.datumHelp
?
|
None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Estimated | Provided | Provided | Provided | Provided | Estimated | Estimated | Provided | Estimated | Provided | Not applicable | Not applicable | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated |
EM ID
em.detail.idHelp
?
|
EM-51 ![]() |
EM-66 | EM-69 | EM-70 | EM-71 | EM-94 |
EM-125 ![]() |
EM-193 | EM-437 |
EM-541 ![]() |
EM-549 | EM-617 | EM-629 | EM-648 | EM-683 |
EM-821 ![]() |
EM-837 | EM-844 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Rivers and Streams | Created Greenspace | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | 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 | Inland Wetlands | Near Coastal Marine and Estuarine | Open Ocean and Seas | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Agroecosystems | Inland Wetlands | Forests | Inland Wetlands | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Rivers and Streams | Created Greenspace | Grasslands | Grasslands |
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Urban areas including streams | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Streams and near upstream environments | Not applicable | none | 13 LULC were used | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | Agroecosystems | Restored wetlands | Montain forest | Grassland buffering inland wetlands set in agricultural land | Beaches | wadeable and boatable streams | restored landfills and conserved grasslands | grasslands |
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale 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 | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
?
|
EM-51 ![]() |
EM-66 | EM-69 | EM-70 | EM-71 | EM-94 |
EM-125 ![]() |
EM-193 | EM-437 |
EM-541 ![]() |
EM-549 | EM-617 | EM-629 | EM-648 | EM-683 |
EM-821 ![]() |
EM-837 | EM-844 |
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Not applicable | Community | Community | Community | Community | Not applicable | Not applicable | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Not applicable | Not applicable | Species | Not applicable | Guild or Assemblage | Individual or population, within a species | Species |
Taxonomic level and name of organisms or groups identified
EM-51 ![]() |
EM-66 | EM-69 | EM-70 | EM-71 | EM-94 |
EM-125 ![]() |
EM-193 | EM-437 |
EM-541 ![]() |
EM-549 | EM-617 | EM-629 | EM-648 | EM-683 |
EM-821 ![]() |
EM-837 | EM-844 |
None Available | 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-51 ![]() |
EM-66 | EM-69 | EM-70 | EM-71 | EM-94 |
EM-125 ![]() |
EM-193 | EM-437 |
EM-541 ![]() |
EM-549 | EM-617 | EM-629 | EM-648 | EM-683 |
EM-821 ![]() |
EM-837 | EM-844 |
|
None | 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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-51 ![]() |
EM-66 | EM-69 | EM-70 | EM-71 | EM-94 |
EM-125 ![]() |
EM-193 | EM-437 |
EM-541 ![]() |
EM-549 | EM-617 | EM-629 | EM-648 | EM-683 |
EM-821 ![]() |
EM-837 | EM-844 |
|
None | None | None | None | None |
|
|
None |
|
|
|
|
|
|
|
None |
|