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-65 | EM-86 |
EM-224 ![]() |
EM-333 ![]() |
EM-414 | EM-423 | EM-432 |
EM-467 ![]() |
EM-496 ![]() |
EM-685 |
EM-777 ![]() |
EM-821 ![]() |
EM-876 | EM-891 | EM-892 |
EM-948 ![]() |
EM-964 |
EM Short Name
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Green biomass production, Central French Alps | Area and hotspots of soil retention, South Africa | FORCLIM v2.9, West Cascades, OR, USA | Evoland v3.5 (unbounded growth), Eugene, OR, USA | SAV occurrence, St. Louis River, MN/WI, USA | Air pollutant removal, Guánica Bay, Puerto Rico | Nitrogen fixation rates, Guánica Bay, Puerto Rico | Yasso07 v1.0.1, Switzerland | Sed. denitrification, St. Louis R., MN/WI, USA | Visitor value lost to a beach closure, MA, USA | Bees and managed prairie plants and soil, MO, USA | Aquatic vertebrate IBI for Western streams, USA | Neighborhood greenness and health, FL, USA | Home ownership, Great Lakes, USA | VELMA v. 2.1 contaminant modeling | Global forest stock, biomass and carbon downscaled | EcoSim II - method |
EM Full Name
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Green biomass production, Central French Alps | Area and hotspots of soil retention, South Africa | FORCLIM (FORests in a changing CLIMate) v2.9, West Cascades, OR, USA | Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA | Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA | Air pollutant removal, Guánica Bay, Puerto Rico, USA | Nitrogen fixation rates, Guánica Bay, Puerto Rico, USA | Yasso07 v1.0.1 forest litter decomposition, Switzerland | Sediment denitrification, St. Louis River, MN/WI, USA | Visitor value lost to a beach closure, Barnstable, Massachusetts, USA | Tallgrass prairie bee community affected by management effects on plant community and soil properties, Missouri, USA | Development of an aquatic vertebrate index of biotic integrity (IBI) for Western streams, USA | Neighborhood greenness and chronic health conditions in Medicare beneficiaries, Miami-Dade County, Florida, USA | Human well being indicator - home ownership, Great Lakes waterfront, USA | VELMA (Visualizing Ecosystem Land Management Assessments) v. 2.1 contaminant modeling | Global forest growing stock, biomass and carbon downscaled map | EcoSim II - method |
EM Source or Collection
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EU Biodiversity Action 5 | None | US EPA | Envision | US EPA | US EPA | US EPA | None | US EPA | US EPA | None | None | None | US EPA | US EPA | None | None |
EM Source Document ID
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260 | 271 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
330 |
338 ?Comment:Manuscript in revision, should be published by end of 2016. |
338 ?Comment:WE received a draft copy prior to journal publication that was agency reviewed. |
343 | 333 | 386 | 398 | 404 | 417 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
423 ?Comment:Document #430 is an additional source for this EM. Document #423 has been imcorporated into the more recently published document #430. |
442 | 448 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Didion, M., B. Frey, N. Rogiers, and E. Thurig | Brent J. Bellinger, Terri M. Jicha, LaRae P. Lehto, Lindsey R. Seifert-Monson, David W. Bolgrien, Matthew A. Starry, Theodore R. Angradi, Mark S. Pearson, Colleen Elonen, and Brian H. Hill | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | Buckles, B. J., and A. N. Harmon-Threatt | Pont, D., Hughes, R.M., Whittier, T.R., and S. Schmutz. | Brown, S. C., J. Lombard, K. Wang, M. M. Byrne, M. Toro, E. Plater-Zyberk, D. J. Feaster, J. Kardys, M. I. Nardi, G. Perez-Gomez, H. M. Pantin, and J. Szapocznik | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | McKane | Kindermann, G.E., I. McCallum, S. Fritz, and M. Obersteiner | Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell |
Document Year
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2011 | 2008 | 2007 | 2008 | 2013 | 2017 | 2017 | 2014 | 2014 | 2018 | 2019 | 2009 | 2016 | None | None | 2008 | 2000 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Mapping ecosystem services for planning and management | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Validating tree litter decomposition in the Yasso07 carbon model | Sediment nitrification and denitrification in a Lake Superior estuary | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | Bee diversity in tallgrass prairies affected by management and its effects on above‐ and below‐ground resources | A Predictive Index of Biotic Integrity Model for A predictive index of biotic integrity model foraquatic-vertebrate assemblages of Western U.S. Streams | Neighborhood greenness and chronic health conditions in Medicare beneficiaries | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | Tutorial A.1 – Contaminant Fate and Transport Modeling Concepts; VELMA 2.1 “How To” Documentation | A global forest growing stock, biomass and carbon map based on FAO statistics | Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed but unpublished (explain in Comment) | 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 journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Journal manuscript submitted or in review | Published EPA report | Published journal manuscript | Published journal manuscript |
EM ID
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EM-65 | EM-86 |
EM-224 ![]() |
EM-333 ![]() |
EM-414 | EM-423 | EM-432 |
EM-467 ![]() |
EM-496 ![]() |
EM-685 |
EM-777 ![]() |
EM-821 ![]() |
EM-876 | EM-891 | EM-892 |
EM-948 ![]() |
EM-964 |
Not applicable | Not applicable | Not applicable | http://evoland.bioe.orst.edu/ | Not applicable | Not applicable | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=354355 | Not applicable | https://ecopath.org/downloads/ | |
Contact Name
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Sandra Lavorel | Benis Egoh | Richard T. Busing | Michael R. Guzy | Ted R. Angradi | Susan H. Yee | Susan H. Yee |
Markus Didion ?Comment:Tel.: +41 44 7392 427 |
Brent J. Bellinger ?Comment:Ph# +1 218 529 5247. Other current address: Superior Water, Light and Power Company, 2915 Hill Ave., Superior, WI 54880, USA. |
Kate K, Mulvaney | Alexandra N. Harmon‐Threatt | Didier Pont | Scott C. Brown | Ted Angradi | Robert B. McKane | Georg Kindermann | Carl Walters |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | Oregon State University, Dept. of Biological and Ecological Engineering | U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA | Not reported | Department of Entomology, University of Illinois, Urbana, IL, USA | 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 Public Health Sciences, University of Miami Miller School of Medicine, 1120 NW 14th Street, Clinical Research Building (CRB), Room 1065, Miami FL 33136 | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | US EPA, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon 97333 | International Institute for Applied Systems Analysis, Laxenburg, Austria | Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4 |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | Not reported | rtbusing@aol.com | Not reported | angradi.theodore@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | markus.didion@wsl.ch | bellinger.brent@epa.gov | Mulvaney.Kate@EPA.gov | aht@illinois.edu | didier.pont@cemagref.fr | sbrown@med.miami.edu | tedangradi@gmail.com | mckane.bob@epa.gov | kinder(at)iiasa.ac.at | c.walters@oceans.ubc.ca |
EM ID
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EM-65 | EM-86 |
EM-224 ![]() |
EM-333 ![]() |
EM-414 | EM-423 | EM-432 |
EM-467 ![]() |
EM-496 ![]() |
EM-685 |
EM-777 ![]() |
EM-821 ![]() |
EM-876 | EM-891 | EM-892 |
EM-948 ![]() |
EM-964 |
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. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)." | AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil retention was modelled as a function of vegetation or litter cover and soil erosion potential. Schoeman et al. (2002) modelled soil erosion potential and derived eight erosion classes, ranging from low to severe erosion potential for South Africa. The vegetation cover was mapped by ranking vegetation types using expert knowledge of their ability to curb erosion. We used Schulze (2004) index of litter cover which estimates the soil surface covered by litter based on observations in a range of grasslands, woodlands and natural forests. According to Quinton et al. (1997) and Fowler and Rockstrom (2001) soil erosion is slightly reduced with about 30%, significantly reduced with about 70% vegetation cover. The range of soil retention was mapped by selecting all areas that had vegetation or litter cover of more than 30% for both the expert classified vegetation types and litter accumulation index within areas with moderate to severe erosion potential. The hotspot was mapped as areas with severe erosion potential and vegetation/litter cover of at least 70% where maintaining the cover is essential to prevent erosion. An assumption was made that the potential for this service is relatively low in areas with little natural vegetation or litter cover." | ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range…The simulation of both stand-replacing and partial-stand disturbances across western Oregon improved agreement between simulated and actual data." AUTHOR'S DESCRIPTION: "An analysis of forest successional dynamics was performed on ecoregions 4a and 4b, which cover the south Santiam watershed area selected for intensive study. In each of these two ecoregions, a set of 20 simulated sites was compared to survey plot data summaries. Survey data were analysed by stand age class and simulations of corresponding ages. The statistical methods described…were applied in comparison of actual with simulated forest composition and total basal area by age class. Separate simulations were run with and without fire." | **Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake." | AUTHOR'S DESCRIPTION: "Air pollutant removal, particularly of large dust particles relevant to asthma, was identified as an ecosystem service contributing to the stakeholder objective to improve air quality…Rates of air pollutant removal depend on the downward flux of particles intercepted by the tree canopy…Because atmospheric pollutant concentration can vary widely across space and time, we standardized across watersheds by calculating the removal rate per unit concentration of pollutant, assuming a pollutant concentration of 1 g m^-3. Specifically, the removal rate was calculated per unit concentration of particulate matter greater than…PM<sub>10, applying a typical deposition velocity of 1.25 cm s^-1…" | AUTHOR'S DESCRIPTION: " …In Guánica Bay watershed, Puerto Rico, deforestation and drainage of a large lagoon have led to sediment, contaminant, and nutrient transport into the bay, resulting in declining quality of coral reefs. A watershed management plan is currently being implemented to restore reefs through a variety of proposed actions…After the workshops, fifteen indicators of terrestrial ecosystem services in the watershed and four indicators in the coastal zone were identified to reflect the wide range of stakeholder concerns that could be impacted by management decisions. Ecosystem service production functions were applied to quantify and map ecosystem services supply in the Guánica Bay watershed, as well as an additional highly engineered upper multi-watershed area connected to the lower watershed via a series of reservoirs and tunnels,…” AUTHOR''S DESCRIPTION: "The U.S. Coral Reef Task Force (CRTF), a collaboration of federal, state and territorial agencies, initiated a program in 2009 to better incorporate land-based sources of pollution and socio-economic considerations into watershed strategies for coral reef protection (Bradley et al., 2016)...Baseline measures for relevant ecosystem services were calculated by parameterizing existing methods, largely based on land cover (Egoh et al., 2012; Martinez- Harms and Balvanera, 2012), with relevant rates of ecosystem services production for Puerto Rico, and applying them to map ecosystem services supply for the Guánica Bay Watershed...The Guánica Bay watershed is a highly engineered watershed in southwestern Puerto Rico, with a series of five reservoirs and extensive tunnel systems artificially connecting multiple mountainous sub-watersheds to the lower watershed of the Rio Loco, which itself is altered by an irrigation canal and return drainage ditch that diverts water through the Lajas Valley (PRWRA, 1948)...For each objective, a translator of ecosystem services production, i.e., ecological production function, was used to quantify baseline measurements of ecosystem services supply from land use/land cover (LULC) maps for watersheds across Puerto Rico...Two additional metrics, nitrogen fixation and rates of carbon sequestration into soil and sediment, were also calculated as potential measures of soil quality and agricultural productivity. Carbon sequestration and nitrogen fixation rates were assigned to each land cover class" | ABSTRACT: "...We examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to (i) examine the effect of the application of three different published Yasso07 parameter sets on simulated decay rate; (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests;…" AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root litter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The result of the simulation was an annual estimate of the remaining fraction of the initial mass, which could then be compared with observed data." | ABSTRACT: "Inorganic nitrogen (N) transformations and removal in aquatic sediments are microbially mediated, and rates influence N-transport. In this study we related physicochemical properties of a large Great Lakes embayment, the St. Louis River Estuary (SLRE) of western Lake Superior, to sediment N-transformation rates. We tested for associations among rates and N-inputs, vegetation biomass, and temperature. We measured rates of nitrification (NIT), unamended base denitrification (DeNIT), and potential denitrification [denitrifying enzyme activity (DEA)] in 2011 and 2012 across spatial and depth zones…Nitrogen cycling rates were spatially and temporally variable, but we modeled how alterations to water depth and N-inputs may impact DeNIT rates." AUTHOR'S DESCRIPTION: "We used different survey designs in 2011 and 2012. Both designs were based on area-weighted probability sampling methods, similar to those developed for EPA's Environmental Monitoring and Assessment Program (EMAP) (Crane et al., 2005; Stevens and Olsen, 2003, 2004). Sampling sites were assigned to spatial zones: “harbor” (river km 0–13), “bay” (river km 13–24), or “river” (river km 24–35) (Fig. 1). Sites were also grouped by depth zones (“shallow,” <1 m; “intermediate,” 1–2 m; and “deep,” >2 m). In 2011 (“vegetated-habitat survey”), the sample frame consisted of areas of emergent and submergent vegetation in the SLRE… The resulting sample frame included 2370 ha of potentially vegetated area out of a total SLRE area of 4378 ha. Sixty sites were distributed across the total vegetated area in each spatial zone using an uneven spatially balanced probabilistic design. Vegetated areas were more prevalent, and thus had greater sampling effort, in the bay (n = 33) and river (n = 17) than harbor (n=10) zones, and in the shallow (n=44) and intermediate (n =14) than deep (n =2) zones. All sampling was done in July. In 2012 a probabilistic sampling design (“estuary-wide survey”) was implemented to determine N-cycling rates for the entire SLRE (not just vegetated areas as in 2011). Thirty sites unevenly distributed across spatial and depth zones were sampled monthly in May–September (Fig. 1). Area weighting for each sampled site reflects the SLRE area attributable to each sample by month, spatial zone, and depth zone." "…we were able to create significant predictive models for NIT and DeNIT rates using linear combinations of physiochemical parameters…" "…Simulations of changes in DeNIT rates in response to altered water depth and surface NOx-N concentration for spring (Fig. 4A) and summer (Fig. 4B) show that for a given season, altering water depths would have a greater influence on DeNIT than rising NO3- concentration." | ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "While it might be assumed that the economic value of a beach day and the value of a lost beach day would be symmetric, they are not quite the same in our analysis. This is because the town has many fixed costs for beach management, including staff, facility maintenance, and other amenities. These fixed costs are offset by the daily parking fees charged to out-of-town visitors and the various beach stickers available for town residents. Assuming the town does not make a profit and just breaks even on beach parking fees in relation to the costs incurred to provide the services, the net economic value of a day without a closure (benefits less costs) would simply be the consumer surplus for the public. However, this amount is different than the net economic value lost due to a beach closure, which includes the lost consumer surplus as well as the lost revenue to the town. This revenue is money the town would have collected to cover costs and therefore is considered a loss (negative producer surplus). Therefore, a beach day affected by a closure is valued as a loss of consumer surplus plus lost parking revenue…" Equation 3, page 19, provides the resulting formula for the value lost from a beach closure. | ABSTRACT: "1. Habitat management methods are crucial to maintaining habitats in the long term and ensuring vital resources are available for declining species. However, when management focuses on a single resource there is the potential to reduce or degrade other critical resources and negatively affect species of concern. Although both floral and nesting resources are critical to supporting bee populations, little consideration is given to the availability of nesting resources. Given known effects of management methods on soils, where the majority of bees nest, and floral food resources, increasing our understanding of management effects on both soils and floral resources is important to improving bee conservation efforts. 2. In 20 tallgrass prairie plots managed under 1 of 3 common methods: burning, haying and patch‐burn grazing, we assessed effects on bee communities and necessary above‐ and below‐ground resources. We also considered how management and resources affected below‐ground nesting in each prairie. 3. Management type affected both soil conditions and floral resources with patchburn grazing sites providing overall worse resources for bees compared to ungrazed sites. Soil conditions were also important for predicting most aspects of the bee community including abundance and community composition. Soil conditions also decreased floral richness and Floristic Quality Index (FQI). This suggests management affects bee communities both directly and indirectly through soil. 4. Increased nesting was observed in sites with greater floral abundance and soil conditions that correspond to increased bare ground, lower soil moisture and warmer soil temperatures suggesting management that helps increase floral abundance and improve soil conditions could be critical to increasing bee nesting. 5. Synthesis and applications. Measuring and tracking bare ground, Floristic Quality Index (FQI) and floral richness may help managers determine if their management methods are adversely affecting bees. Grazing and haying management negatively affected the bee community, vital nesting and…" floraresources and nesting rate. These managements may need to be avoided to meet bee conservation goals in prairies. Additionally, while soils have been largely overlooked, we found soil conditions to be an important predictor for bee communities and floral resources, and should be considered more explicitly in conserved areas. | 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: "Introduction: Prior studies suggest that exposure to the natural environment may impact health. The present study examines the association between objective measures of block-level greenness (vegetative presence) and chronic medical conditions, including cardiometabolic conditions, in a large population-based sample of Medicare beneficiaries in Miami-Dade County, Florida. Methods: The sample included 249,405 Medicare beneficiaries aged >=65 years whose location (ZIP+4) within Miami-Dade County, Florida, did not change, from 2010 to 2011. Data were obtained in 2013 and multilevel analyses conducted in 2014 to examine relationships between greenness, measured by mean Normalized Difference Vegetation Index from satellite imagery at the Census block level, and chronic health conditions in 2011, adjusting for neighborhood median household income, individual age, gender, race, and ethnicity. Results: Higher greenness was significantly associated with better health, adjusting for covariates: An increase in mean block-level Normalized Difference Vegetation Index from 1 SD less to 1 SD more than the mean was associated with 49 fewer chronic conditions per 1,000 individuals, which is approximately similar to a reduction in age of the overall study population by 3 years. This same level of increase in mean Normalized Difference Vegetation Index was associated with a reduced risk of diabetes by 14%, hypertension by 13%, and hyperlipidemia by 10%. Planned post-hoc analyses revealed stronger and more consistently positive relationships between greenness and health in lower- than higher-income neighborhoods. Conclusions: Greenness or vegetative presence may be effective in promoting health in older populations, particularly in poor neighborhoods, possibly due to increased time outdoors, physical activity, or stress mitigation." | ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context. " | ABSTRACT: "This document describes the conceptual framework underpinning the use of VELMA 2.1 to model fate and transport of organic contaminants within watersheds. We review how VELMA 2.1 simulates contaminant fate and transport within soils and hillslopes as a function of two processes: (1) the partitioning of the total amount of a contaminant between sorbed (immobile) and aqueous (mobile) phases; and (2) the vertical and lateral transport of the contaminant’s aqueous phase within surface and subsurface waters." | ABSTRACT: "Currently, information on forest biomass is available from a mixture of sources, including in-situ measurements, national forest inventories, administrative-level statistics, model outputs and regional satellite products. These data tend to be regional or national, based on different methodologies and not easily accessible. One of the few maps available is the Global Forest Resources Assessment (FRA) produced by the Food and Agriculture Organization of the United Nations (FAO 2005) which contains aggregated country-level information about the growing stock, biomass and carbon stock in forests for 229 countries and territories. This paper presents a technique to downscale the aggregated results of the FRA2005 from the country level to a half degree global spatial dataset containing forest growing stock; above/belowground biomass, dead wood and total forest biomass; and above-ground, below-ground, dead wood, litter and soil carbon. In all cases, the number of countries providing data is incomplete. For those countries with missing data, values were estimated using regression equations based on a downscaling model. The downscaling method is derived using a relationship between net primary productivity (NPP) and biomass and the relationship between human impact and biomass assuming a decrease in biomass with an increased level of human activity. The results, presented here, represent one of the first attempts to produce a consistent global spatial database at half degree resolution containing forest growing stock, biomass and carbon stock values. All results from the methodology described in this paper are available online at www. iiasa.ac.at/Research/FOR/. " | ABSTRACT: " EcoSim II uses results from the Ecopath procedure for trophic mass-balance analysis to define biomass dynamics models for predicting temporal change in exploited ecosystems. Key populations can be repre- sented in further detail by using delay-difference models to account for both biomass and numbers dynamics. A major problem revealed by linking the population and biomass dynamics models is in representation of population responses to changes in food supply; simple proportional growth and reproductive responses lead to unrealistic predic- tions of changes in mean body size with changes in fishing mortality. EcoSim II allows users to specify life history mechanisms to avoid such unrealistic predictions: animals may translate changes in feed- ing rate into changes in reproductive rather than growth rates, or they may translate changes in food availability into changes in foraging time that in turn affects predation risk. These options, along with model relationships for limits on prey availabil- ity caused by predation avoidance tactics, tend to cause strong compensatory responses in modeled populations. It is likely that such compensatory responses are responsible for our inability to find obvious correlations between interacting trophic components in fisheries time-series data. But Eco- sim II does not just predict strong compensatory responses: it also suggests that large piscivores may be vulnerable to delayed recruitment collapses caused by increases in prey species that are in turn competitors/predators of juvenile piscivores " |
Specific Policy or Decision Context Cited
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None identified | None identified | None Identified | Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None identified | None identified | None provided | None identified | None identified | Economic value of protecting coastal beach water quality from contamination caused closures. | Management strategies of prairie remnants for pollinator community | None reported | None identified | None identified | None identified | None identified | None |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | West Cascade lowlands (4a), and west Cascade montane (4b) ecoregions | No additional description provided | submerged aquatic vegetation | No additional description provided | No additional description provided | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | No additional description provided | Four separate beaches within the community of Barnstable | No additional description provided | Wadeable and boatable streams in 12 western USA states | No additional description provided | Waterfront districts on south Lake Michigan and south lake Erie | No additional description provided | No additional description provided | None, Ocean ecosystems |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | Two scenarios modelled, forests with and without fire | Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves. | No scenarios presented | No scenarios presented | No scenarios presented |
No scenarios presented ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
No scenarios presented | No scenarios presented | Alternative management strategies: burning, haying and patch‐burn grazing | not applicable | No scenarios presented | N/A | No scenarios presented | No scenarios presented | N/A |
EM ID
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EM-65 | EM-86 |
EM-224 ![]() |
EM-333 ![]() |
EM-414 | EM-423 | EM-432 |
EM-467 ![]() |
EM-496 ![]() |
EM-685 |
EM-777 ![]() |
EM-821 ![]() |
EM-876 | EM-891 | EM-892 |
EM-948 ![]() |
EM-964 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application View EM Runs | Method + Application | Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs | Method Only |
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 | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-65 | EM-86 |
EM-224 ![]() |
EM-333 ![]() |
EM-414 | EM-423 | EM-432 |
EM-467 ![]() |
EM-496 ![]() |
EM-685 |
EM-777 ![]() |
EM-821 ![]() |
EM-876 | EM-891 | EM-892 |
EM-948 ![]() |
EM-964 |
Document ID for related EM
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Doc-260 |
Doc-271 ?Comment:Document 273 used for source information on soil erosion potential variable |
Doc-22 | Doc-23 |
Doc-183 | Doc-47 | Doc-313 | Doc-314 ?Comment:Doc 183 is a secondary source for the Evoland model. |
None | None | None | Doc-342 | Doc-344 | None | Doc-386 | Doc-387 | None | Doc-403 | None | Doc-422 | Doc-430 | None | None |
EM ID for related EM
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EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-85 | EM-87 | EM-88 | EM-146 | EM-208 | EM-186 | EM-12 | EM-369 | None | None | None | EM-466 | EM-469 | EM-480 | EM-485 | None | EM-682 | EM-684 | EM-683 | EM-686 | None | EM-820 | EM-826 | None | EM-886 | EM-888 | EM-889 | EM-890 | EM-893 | EM-894 | EM-895 | EM-883 | EM-884 | EM-887 | None | EM-1055 |
EM Modeling Approach
EM ID
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EM-65 | EM-86 |
EM-224 ![]() |
EM-333 ![]() |
EM-414 | EM-423 | EM-432 |
EM-467 ![]() |
EM-496 ![]() |
EM-685 |
EM-777 ![]() |
EM-821 ![]() |
EM-876 | EM-891 | EM-892 |
EM-948 ![]() |
EM-964 |
EM Temporal Extent
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2007-2009 | Not reported | >650 yrs | 1990-2050 | 2010 - 2012 | 2013 | 1978 - 2009 | 1993-2013 |
July 2011 to September 2012 ?Comment:All sampling performed July 2011, and May-September 2012. |
July 1, 2011 to June 31, 2016 | 2012-2016 | 2004-2005 | 2010-2011 | 2022 | Not applicable | 1999-2005 | Not applicable |
EM Time Dependence
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time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | past time | future time | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable | Not applicable | both |
EM Time Continuity
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Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable |
discrete ?Comment:Modeller dependent |
EM Temporal Grain Size Value
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Not applicable | Not applicable | 1 | 2 | Not applicable | Not applicable | Not applicable | 1 | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | 1 |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Year | Year | Not applicable | Not applicable | Not applicable | Year | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable | Day |
EM ID
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EM-65 | EM-86 |
EM-224 ![]() |
EM-333 ![]() |
EM-414 | EM-423 | EM-432 |
EM-467 ![]() |
EM-496 ![]() |
EM-685 |
EM-777 ![]() |
EM-821 ![]() |
EM-876 | EM-891 | EM-892 |
EM-948 ![]() |
EM-964 |
Bounding Type
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Physiographic or Ecological | Geopolitical | Physiographic or ecological | Geopolitical | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Geopolitical | Physiographic or ecological | Physiographic or ecological | Geopolitical | Geopolitical | Geopolitical | Geopolitical | Not applicable | No location (no locational reference given) | Other |
Spatial Extent Name
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Central French Alps | South Africa | West Cascades, Oregon | Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | St. Louis River Estuary | Guanica Bay watershed | Guanica Bay watershed | Switzerland | St. Louis River Estuary (of western Lake Superior) | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | Counties: Barton, St. Clair, Cedar, Dade and Polk | Western 12 states | Miami-Dade County | Great Lakes waterfront | Not applicable | Global | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 10-100 km^2 | 10-100 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 10,000-100,000 km^2 | 10-100 km^2 | 10-100 ha | 1000-10,000 km^2. | >1,000,000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | Not applicable | >1,000,000 km^2 | Not applicable |
EM ID
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EM-65 | EM-86 |
EM-224 ![]() |
EM-333 ![]() |
EM-414 | EM-423 | EM-432 |
EM-467 ![]() |
EM-496 ![]() |
EM-685 |
EM-777 ![]() |
EM-821 ![]() |
EM-876 | EM-891 | EM-892 |
EM-948 ![]() |
EM-964 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:BH: Each individual transect?s data was parceled into location reports, and that each report?s ?quadrat? area was dependent upon the angle of the hydroacoustic sampling beam. The spatial grain is 0.07 m^2, 0.20 m^2 and 0.70 m^2 for depths of 1 meter, 2 meters and 3 meters, respectively. |
spatially distributed (in at least some cases) ?Comment:pp. 14 - "Most ecosystem services were mapped at the same resolution as the LULC data (30 x 30 m^2)." I assumed that, unless otherwise specified, calculations were carried out on a 30 x 30 m^2 pixel. |
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) ?Comment:871 total sites surveyed for this work |
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) |
Spatial Grain Type
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area, for pixel or radial feature | 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 | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | volume, for 3-D feature | area, for pixel or radial feature | Not applicable |
Spatial Grain Size
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20 m x 20 m | Distributed across catchments with average size of 65,000 ha | 0.08 ha | varies | 0.07 m^2 to 0.70 m^2 | 30 m x 30 m | HUC | 5 sites | 35 km river estuary reach, 0 to 5 m depth by 1 m increment | by beach site | 1 ha | stream reach | Census block | Not applicable | user defined | 0.5 x 0.5 degrees | Not applicable |
EM ID
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EM-65 | EM-86 |
EM-224 ![]() |
EM-333 ![]() |
EM-414 | EM-423 | EM-432 |
EM-467 ![]() |
EM-496 ![]() |
EM-685 |
EM-777 ![]() |
EM-821 ![]() |
EM-876 | EM-891 | EM-892 |
EM-948 ![]() |
EM-964 |
EM Computational Approach
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Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-65 | EM-86 |
EM-224 ![]() |
EM-333 ![]() |
EM-414 | EM-423 | EM-432 |
EM-467 ![]() |
EM-496 ![]() |
EM-685 |
EM-777 ![]() |
EM-821 ![]() |
EM-876 | EM-891 | EM-892 |
EM-948 ![]() |
EM-964 |
Model Calibration Reported?
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No | No | No | Unclear | Yes | Yes | No | No | Yes | Yes | No | No | Not applicable | No | Not applicable | No | No |
Model Goodness of Fit Reported?
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Yes | No | No | No | Yes | No | No | No | Yes | No | Not applicable | No | No | No | Not applicable |
Yes ?Comment:For the 0.5 grid level equation where the country forest level is missing. |
No |
Goodness of Fit (metric| value | unit)
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None | None | None |
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None | None | None |
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None | None | None | None | None | None |
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None |
Model Operational Validation Reported?
em.detail.validationHelp
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Yes | No | Yes | No | Yes | No | No | Yes | No | No | No |
Yes ?Comment:Compared to another journal manuscript IBI scores (Whittier et al) |
No | No | Not applicable | Yes | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | No | No | No | No | No | No | No | No | No | No | Not applicable | No | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | No | No | No | No | No | No |
No ?Comment:n/a |
Yes | Yes | No | Yes | Not applicable | No | Not applicable |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Yes | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-65 | EM-86 |
EM-224 ![]() |
EM-333 ![]() |
EM-414 | EM-423 | EM-432 |
EM-467 ![]() |
EM-496 ![]() |
EM-685 |
EM-777 ![]() |
EM-821 ![]() |
EM-876 | EM-891 | EM-892 |
EM-948 ![]() |
EM-964 |
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None | None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-65 | EM-86 |
EM-224 ![]() |
EM-333 ![]() |
EM-414 | EM-423 | EM-432 |
EM-467 ![]() |
EM-496 ![]() |
EM-685 |
EM-777 ![]() |
EM-821 ![]() |
EM-876 | EM-891 | EM-892 |
EM-948 ![]() |
EM-964 |
None | None | None | None | None |
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None | None | None |
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None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-65 | EM-86 |
EM-224 ![]() |
EM-333 ![]() |
EM-414 | EM-423 | EM-432 |
EM-467 ![]() |
EM-496 ![]() |
EM-685 |
EM-777 ![]() |
EM-821 ![]() |
EM-876 | EM-891 | EM-892 |
EM-948 ![]() |
EM-964 |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | -30 | 44.24 | 44.11 | 46.72 | 17.96 | 17.96 | 46.82 | 46.74 | 41.64 | 37.68 | 44.2 | 25.64 | 42.26 | Not applicable | 44.51 | Not applicable |
Centroid Longitude
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6.4 | 25 | -122.24 | -123.09 | -96.13 | -67.04 | -67.02 | 8.23 | -96.13 | -70.29 | -93.71 | -113.07 | -80.5 | -87.84 | Not applicable | -123.51 | Not applicable |
Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | Not applicable |
Centroid Coordinates Status
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Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Not applicable |
EM ID
em.detail.idHelp
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EM-65 | EM-86 |
EM-224 ![]() |
EM-333 ![]() |
EM-414 | EM-423 | EM-432 |
EM-467 ![]() |
EM-496 ![]() |
EM-685 |
EM-777 ![]() |
EM-821 ![]() |
EM-876 | EM-891 | EM-892 |
EM-948 ![]() |
EM-964 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Forests | Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Inland Wetlands | Open Ocean and Seas | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Atmosphere | Inland Wetlands | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Forests | Rivers and Streams | Inland Wetlands | Near Coastal Marine and Estuarine | Grasslands | Rivers and Streams | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Forests | Open Ocean and Seas |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows | Not reported | Primarily conifer forest | Agricultural-urban interface at river junction | Freshwater estuarine system | Multiple environmental types present | Tropical terrestrial | forests | River and riverine estuary (lake) | Saltwater beach | Remnant tallgrass prairie | wadeable and boatable streams | urban neighborhood greenspace | Lake Michigan & Lake Erie waterfront | Terrestrial environment | Forests | Pelagic |
EM Ecological Scale
em.detail.ecoScaleHelp
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Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | 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 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
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EM-65 | EM-86 |
EM-224 ![]() |
EM-333 ![]() |
EM-414 | EM-423 | EM-432 |
EM-467 ![]() |
EM-496 ![]() |
EM-685 |
EM-777 ![]() |
EM-821 ![]() |
EM-876 | EM-891 | EM-892 |
EM-948 ![]() |
EM-964 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Not applicable | Species | Not applicable | Not applicable | Not applicable | Not applicable | Community | Not applicable | Not applicable | Species | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Not applicable |
Other (Comment) ?Comment:Varied levels of taxonomic order |
Taxonomic level and name of organisms or groups identified
EM-65 | EM-86 |
EM-224 ![]() |
EM-333 ![]() |
EM-414 | EM-423 | EM-432 |
EM-467 ![]() |
EM-496 ![]() |
EM-685 |
EM-777 ![]() |
EM-821 ![]() |
EM-876 | EM-891 | EM-892 |
EM-948 ![]() |
EM-964 |
None Available | None Available |
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None Available | None Available | None Available | None Available | None Available | None Available |
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None Available | None Available | None Available | 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-65 | EM-86 |
EM-224 ![]() |
EM-333 ![]() |
EM-414 | EM-423 | EM-432 |
EM-467 ![]() |
EM-496 ![]() |
EM-685 |
EM-777 ![]() |
EM-821 ![]() |
EM-876 | EM-891 | EM-892 |
EM-948 ![]() |
EM-964 |
None |
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None |
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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-65 | EM-86 |
EM-224 ![]() |
EM-333 ![]() |
EM-414 | EM-423 | EM-432 |
EM-467 ![]() |
EM-496 ![]() |
EM-685 |
EM-777 ![]() |
EM-821 ![]() |
EM-876 | EM-891 | EM-892 |
EM-948 ![]() |
EM-964 |
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None | None |
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None | None |
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None | None |
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None |
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None | None | None |
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