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-24 | EM-66 | EM-94 | EM-97 |
EM-275 ![]() |
EM-379 | EM-418 | EM-423 | EM-456 | EM-462 | EM-465 |
EM-541 ![]() |
EM-542 ![]() |
EM-598 | EM-617 | EM-657 |
EM-729 ![]() |
EM-836 | EM-939 | EM-945 | EM-979 | EM-998 |
EM Short Name
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i-Tree Eco: Carbon storage & sequestration, USA | Litter biomass production, Central French Alps | Reduction in pesticide runoff risk, Europe | AnnAGNPS, Kaskaskia River watershed, IL, USA | SWAT, Aixola watershed, Spain | VELMA soil temperature, Oregon, USA | SIRHI, St. Croix, USVI | Air pollutant removal, Guánica Bay, Puerto Rico | Reef dive site favorability, St. Croix, USVI | Value of finfish, St. Croix, USVI | Pharmaceutical product potential, St. Croix, USVI | InVEST fisheries, lobster, South Africa | Coastal protection in Belize | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | RBI Spatial Analysis Method | REQI (River Ecosystem Quality Index), Italy | WESP: Urban Stormwater Treatment, ID, USA | Bird abundance on restored landfills, UK | ESTIMAP- Recreation, Europe | Air pollution removal by green roofs, Chicago, USA | Predicting ecosystem service values, Bangladesh | CAESAR landscape evolution model |
EM Full Name
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i-Tree Eco carbon storage and sequestration (trees), USA | Litter biomass production, Central French Alps | Reduction in pesticide runoff risk, Europe | AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | SWAT (Soil and Water Assessment Tool), Aixola watershed, Spain | VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | SIRHI (SImplified Reef Health Index), St. Croix, USVI | Air pollutant removal, Guánica Bay, Puerto Rico, USA | Dive site favorability (reef), St. Croix, USVI | Relative value of finfish (on reef), St. Croix, USVI | Relative pharmaceutical product potential (on reef), St. Croix, USVI | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa | Coastal Protection provided by Coral, Seagrasses and Mangroves in Belize: | DeNitrification-DeComposition simulation of N2O flux Ireland | Rapid Benefit Indicator (RBI) Spatial Analysis Toolset Method | REQI (River Ecosystem Quality Index), Marecchia River, Italy | WESP: Urban Stormwater Treament, ID, USA | Bird abundance on restored landfills compared to paired reference sites, East Midlands, UK | ESTIMAP- Recreation, Europe | Air pollution removal by green roofs, Chigago, USA | Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh | Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model |
EM Source or Collection
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i-Tree | USDA Forest Service | EU Biodiversity Action 5 | None | US EPA | None | US EPA | US EPA | US EPA | US EPA | US EPA | US EPA | InVEST | InVEST | None | None | None | None | None | None | None | None | None |
EM Source Document ID
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195 | 260 | 255 | 137 | 295 | 317 | 335 |
338 ?Comment:Manuscript in revision, should be published by end of 2016. |
335 | 335 | 335 |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
350 | 358 | 367 | 378 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
406 | 432 |
438 ?Comment:Document 439 is an additional source for this EM. |
457 | 468 |
Document Author
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Nowak, D. J., Greenfield, E. J., Hoehn, R. E. and Lapoint, E. | 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. | Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Zabaleta, A., Meaurio, M., Ruiz, E., and Antigüedad, I. | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby | Guannel, G., Arkema, K., Ruggiero, P., and G. Verutes | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Bousquin, J., Mazzotta M., and W. Berry | Santolini, R, E. Morri, G. Pasini, G. Giovagnoli, C. Morolli, and G. Salmoiraghi | Murphy, C. and T. Weekley | Rahman, M. L., S. Tarrant, D. McCollin, and J. Ollerton | Zulian, G., Parrachini, M.L., Maes, J., | Yang, J., Q. Yu and P. Gong | Morshed, S. R., Fattah, M. A., Haque, M. N., & Morshed, S. Y. | Van De Wiel, M. J., Coulthard, T. J., Macklin, M. G., & Lewin, J. |
Document Year
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2013 | 2011 | 2012 | 2011 | 2014 | 2013 | 2014 | 2017 | 2014 | 2014 | 2014 | 2018 | 2016 | 2010 | 2017 | 2014 | 2012 | 2011 | 2013 | 2008 | 2022 | 2007 |
Document Title
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Carbon storage and sequestration by trees in urban and community areas of the United States | 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 | AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | Simulation climate change impact on runoff and sediment yield in a small watershed in the Basque Country, Northern Spain | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals | The Power of Three: Coral Reefs, Seagrasses and Mangroves Protect Coastal Regions and Increase Their Resilience | Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture | Rapid Benefit Indicators (RBI) Spatial Analysis Toolset - Manual. | Assessing the quality of riparian areas: the case of River Ecosystem Quality Index applied to the Marecchia river (Italy) | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | 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 | ESTIMAP: Ecosystem services mapping at the European scale | Quantifying air pollution removal by green roofs in Chicago | Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh | Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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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 | Published journal manuscript | Published EPA report | Published journal manuscript | Published report | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-24 | EM-66 | EM-94 | EM-97 |
EM-275 ![]() |
EM-379 | EM-418 | EM-423 | EM-456 | EM-462 | EM-465 |
EM-541 ![]() |
EM-542 ![]() |
EM-598 | EM-617 | EM-657 |
EM-729 ![]() |
EM-836 | EM-939 | EM-945 | EM-979 | EM-998 |
Not applicable | Not applicable | Not applicable | https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | http://swat.tamu.edu/software/arcswat/ | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not identified in paper | http://www.dndc.sr.unh.edu | Not applicable | Not applicable | Not applicable | Not applicable | N.A. | Not applicable | Not applicable | http://www.coulthard.org.uk/ | |
Contact Name
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David J. Nowak | Sandra Lavorel | Sven Lautenbach | Yongping Yuan | Ane Zabaleta | Alex Abdelnour | Susan H. Yee | Susan H. Yee | Susan H. Yee | Susan H. Yee | Susan H. Yee | Michelle Ward | Greg Guannel | M. Abdalla | Justin Bousquin | Elisa Morri | Chris Murphy | Lutfor Rahman | Grazia Zulian | Jun Yang | Syed Riad Morshed | Marco J. Van De Wiel |
Contact Address
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USDA Forest Service, Northern Research Station, Syracuse, NY 13210, USA | 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 | U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | Hydrogeology and Environment Group, Science and Technology Faculty, University of the Basque Country, 48940 Leioa, Basque Country (Spain) | Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia | The Nature Conservancy, Coral Gables, FL. USA | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | US EPA, Office of Research and Development, National health and environmental Effects Lab, Gulf Ecology Division, Gulf Breeze, FL 32561 | Dept. of Earth, Life, and Environmental Sciences, Urbino university, via ca le suore, campus scientifico Enrico Mattei, Urbino 61029 Italy | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Landscape and Biodiversity Research Group, School of Science and Technology, The University of Northampton, Avenue Campus, Northampton NN2 6JD, UK | Joint Research Centre, Via Enrico Fermi 2749, TP 272, 21027 Ispra (VA), Italy | Department of Landscape Architecture and Horticulture, Temple University, 580 Meetinghouse Road, Ambler, PA 19002, USA. | Department of Urban and Regional Planning, Khulna University of Engineering and Technology, Khulna, Bangladesh | Department of Geography, University of Western Ontario, London, Ontario, Canada |
Contact Email
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dnowak@fs.fed.us | sandra.lavorel@ujf-grenoble.fr | sven.lautenbach@ufz.de | yuan.yongping@epa.gov | ane.zabaleta@ehu.es | abdelnouralex@gmail.com | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | m.ward@uq.edu.au | greg.guannel@gmail.com | abdallm@tcd.ie | bousquin.justin@epa.gov | elisa.morri@uniurb.it | chris.murphy@idfg.idaho.gov | lutfor.rahman@northampton.ac.uk | grazia.zulian@jrc.ec.europa.e | juny@temple.edu | riad.kuet.urp16@gmail.com | mvandew3@uwo.ca |
EM ID
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EM-24 | EM-66 | EM-94 | EM-97 |
EM-275 ![]() |
EM-379 | EM-418 | EM-423 | EM-456 | EM-462 | EM-465 |
EM-541 ![]() |
EM-542 ![]() |
EM-598 | EM-617 | EM-657 |
EM-729 ![]() |
EM-836 | EM-939 | EM-945 | EM-979 | EM-998 |
Summary Description
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ABSTRACT: "Carbon storage and sequestration by urban trees in the United States was quantified to assess the magnitude and role of urban forests in relation to climate change. Urban tree field data from 28 cities and 6 states were used to determine the average carbon density per unit of tree cover. These data were applied to statewide urban tree cover measurements to determine total urban forest carbon storage and annual sequestration by state and nationally. Urban whole tree carbon storage densities average 7.69 kg C m^2 of tree cover and sequestration densities average 0.28 kg C m^2 of tree cover per year. Total tree carbon storage in U.S. urban areas (c. 2005) is estimated at 643 million tonnes ($50.5 billion value; 95% CI = 597 million and 690 million tonnes) and annual sequestration is estimated at 25.6 million tonnes ($2.0 billion value; 95% CI = 23.7 million to 27.4 million tonnes)." | 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." | AUTHOR'S DESCRIPTION: "We used a spatially explicit model to predict the potential exposure of small streams to insecticides (run-off potential – RP) as well as the resulting ecological risk (ER) for freshwater fauna on the European scale (Schriever and Liess 2007; Kattwinkel et al. 2011)...The recovery of community structure after exposure to insecticides is facilitated by the presence of undisturbed upstream stretches that can act as sources for recolonization (Niemi et al. 1990; Hatakeyama and Yokoyama 1997). In the absence of such sources for recolonization, the structure of the aquatic community at sites that are exposed to insecticides differs significantly from that of reference sites (Liess and von der Ohe 2005)...Hence, we calculated the ER depending on RP for insecticides and the amount of recolonization zones. ER gives the percentage of stream sites in each grid cell (10 × 10 km) in which the composition of the aquatic community deviated from that of good ecological status according to the WFD. In a second step, we estimated the service provided by the environment comparing the ER of a landscape lacking completely recolonization sources with that of the actual landscape configuration. Hence, the ES provided by non-arable areas (forests, pastures, natural grasslands, moors and heathlands) was calculated as the reduction of ER for sensitive species. The service can be thought of as a habitat provisioning/nursery service that leads to an improvement of ecological water quality." | AUTHORS' DESCRIPTION: "AnnAGNPS is an advanced simulation model developed by the USDA-ARS and Natural Resource Conservation Services (NRCS) to help evaluate watershed response to agricultural management practices. It is a continuous simulation, daily time step, pollutant loading model designed to simulate water, sediment and chemical movement from agricultural watersheds.p. 198" | ABSTRACT: "We explored the potential impact of climate change on runoff and sediment yield for the Aixola watershed using the Soil and Water Assessment Tool (SWAT). The model calibration (2007–2010) and validation (2005–2006) results were rated as satisfactory. Subsequently, simulations were run for four climate change model–scenario combinations based on two general circulation models (CGCM2 and ECHAM4) under two emissions scenarios (A2 and B2) from 2011 to 2100." AUTHOR'S DESCRIPTION: "The results were grouped into three consecutive 30-yr periods (2011-2040, 2041-2070, and 2071-2100) and compared with the values simulated for the baseline period (1961-1990)." | ABSTRACT: "We used a new ecohydrological model, Visualizing Ecosystems for Land Management Assessments (VELMA), to analyze the effects of forest harvest on catchment carbon and nitrogen dynamics. We applied the model to a 10 ha headwater catchment in the western Oregon Cascade Range where two major disturbance events have occurred during the past 500 years: a stand-replacing fire circa 1525 and a clear-cut in 1975. Hydrological and biogeochemical data from this site and other Pacific Northwest forest ecosystems were used to calibrate the model. Model parameters were first calibrated to simulate the postfire buildup of ecosystem carbon and nitrogen stocks in plants and soil from 1525 to 1969, the year when stream flow and chemistry measurements were begun. Thereafter, the model was used to simulate old-growth (1969–1974) and postharvest (1975–2008) temporal changes in carbon and nitrogen dynamics…" AUTHOR'S DESCRIPTION: "The soil column model consists of three coupled submodels:...a soil temperature model [Cheng et al., 2010] that simulates daily soil layer temperatures from surface air temperature and snow depth by propagating the air temperature first through the snowpack and then through the ground using the analytical solution of the one-dimensional thermal diffusion equation" | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of indicators have been proposed for measuring reef integrity, defined as the capacity to maintain healthy function and retention of diversity (Turner et al., 2000). The Simplified Integrated Reef Health Index (SIRHI) combines four attributes of reef condition into a single index: SIRHI = ΣiGi where Gi are the grades on a scale of 1 to 5 for four key reef attributes: percent coral cover, percent macroalgal cover, herbivorous fish biomass, and commercial fish biomass (Table2; Healthy Reefs Initiative, 2010). For a number of coral reef condition attributes, including fish richness, coral richness, and reef structural complexity, available data were point surveys from field monitoring by the US Environmental Protection Agency (see Oliver et al. (2011)) or the NOAA Caribbean Coral Reef Ecosystem Monitoring Program (see Pittman et al. (2008)). To generate continuous maps of coral condition for St. Croix, we fitted regression tree models to point survey data for St. Croix and then used models to predict reef condition in non-sampled locations (Fig. 1). In general, we followed the methods of Pittman et al. (2007) which generated predictive models for fish richness using readily available benthic habitat maps and bathymetry data. Because these models rely on readily available data (benthic habitat maps and bathymetry data), the models have the potential for high transferability to other locati | 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…" | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…In lieu of surveys of diver opinion, recreational opportunities can also be estimated by actual field data of coral condition at preferred dive sites. A few studies have directly examined links between coral condition and production of recreational opportunities through field monitoring in an attempt to validate perceptions of recreational quality (Pendleton, 1994; Williams and Polunin, 2002; Leeworthy et al., 2004; Leujakand Ormond, 2007; Uyarraetal., 2009). Uyarraetal. (2009) used surveys to determine reef attributes related to diver perceptions of most and least favorite dive sites. Field data was used to narrow down the suite of potential preferred attributes to those that reflected actual site condition. We combined these attributes to form an index of dive site favorability: Dive site favorability = ΣipiRi where pi is the proportion of respondents indicating each attribute i that affected dive enjoyment positively. Ri is the mean relative magnitude of measured variables used to quantify each descriptive attribute i, including ‘fish abundance’ (pi=0.803), quantified by number of fish schools and fish species richness, and | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(5) value of finfish," | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…When data on sponge diversity is unavailable, benthic habitat coverages may be used to estimate relative magnitudes of sponge diversity and abundance as an indicator of potential pharmaceutical production (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to potential pharmaceutical production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Pharmaceutical product potential = ΣiciMi where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians), and Mi is the relative magnitude of sponge diversity associated with each habitat." | 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: "Natural habitats have the ability to protect coastal communities against the impacts of waves and storms, yet it is unclear how different habitats complement each other to reduce those impacts. Here, we investigate the individual and combined coastal protection services supplied by live corals on reefs, seagrass meadows, and mangrove forests during both non-storm and storm conditions, and under present and future sea-level conditions. Using idealized profiles of fringing and barrier reefs, we quantify the services supplied by these habitats using various metrics of inundation and erosion. We find that, together, live corals, seagrasses, and mangroves supply more protection services than any individual habitat or any combination of two habitats. Specifically, we find that, while mangroves are the most effective at protecting the coast under non-storm and storm conditions, live corals and seagrasses also moderate the impact of waves and storms, thereby further reducing the vulnerability of coastal regions. Also, in addition to structural differences, the amount of service supplied by habitats in our analysis is highly dependent on the geomorphic setting, habitat location and forcing conditions: live corals in the fringing reef profile supply more protection services than seagrasses; seagrasses in the barrier reef profile supply more protection services than live corals; and seagrasses, in our simulations, can even compensate for the long-term degradation of the barrier reef. Results of this study demonstrate the importance of taking integrated and place-based approaches when quantifying and managing for the coastal protection services supplied by ecosystems." | Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. | AUTHOR DESCRIPTION: "The Rapid Benefits Indicators (RBI) approach consists of five steps and is outlined in Assessing the Benefits of Wetland Restoration – A Rapid Benefits Indicators Approach for Decision Makers, hereafter referred to as the “guide.” The guide presents the assessment approach, detailing each step of the indicator development process and providing an example application in the “Step in Action” pages. The spatial analysis toolset is intended to be used to analyze existing spatial information to produce metrics for many of the indicators developed in that guide. This spatial analysis toolset manual gives directions on the mechanics of the tool and its data requirements, but does not detail the reasoning behind the indicators and how to use results of the assessment; this information is found in the guide. " | ABSTRACT: "Riparian areas support a set of river functions and of ecosystem services (ESs). Their role is essential in reducing negative human impacts on river functionality. These aspects could be contained in the River Basin Management Plan, which is the tool for managing and planning freshwater ecosystems in a river basin. In this paper, a new index was developed, namely the River Ecosystem Quality Index (REQI). It is composed of five ecological indices, which assess the quality of riparian areas, and it was first applied to the Marecchia river (central Italy). The REQI was also compared with the Italian River Functionality Index (IFF) and the ESs measured as the capacity of land cover in providing human benefits. Data have shown a decrease in the quality of riparian areas, from the upper to lower part of river, with 53% of all subareas showing medium-quality values…" AUTHOR'S DESCRIPTION: "The evaluation of the quality of the riparian areas is based on the analysis of two fundamental elements of riparian areas: vegetation (characteristics and distribution) and wild birds, measured with standardized methodology and used as indicators of environmental quality and changes...To represent the REQI, each of the five indicators was initially scored with its own range (Figure 3(a)—(e)). Then, all results were redistributed in ranges from 1 to 5, where 5 is the best condition of all indices. Redistributed results were finally summed." | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | ABSTRACT: "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)." | AUTHOR Descriptions: "ESTIMAP consists of a set of separate components, each of which can be run separately. The models have been all framed in the ecosystem services cascade model [4] which connects ecosystem structure and functioning to human well-being through the flow of ecosystem services. At present, three modules are operational and described in further detail in this report: pollination, recreation and coastal protectionPeople can benefit from the opportunities provided by nature for recreational activities if they are able to reach them. The Recreation Opportunity spectrum was chosen as a method to map different degrees of service available according to their proximity to the people. Remoteness and proximity have been addressed in the second step of the analysis, in order to assess how the benefit (recreation) can be delivered to people. The proxy that has been identified couples information on both variables and has been mapped by classifying the EU into zones of proximity versus remoteness. From the ROS perspective this part takes into account remoteness and to some extent expected social experience. Distance from roads and residential areas have been used as inputs. The information on the road network is provided by the TeleAtlas database, and covers all paved roads in Europe. Gravel roads have been discarded to ease the processing. Residential areas are extracted from CORINE land cover classes “continuous urban fabric” and “discontinuous urban fabric”, therefore, all urban patches larger than 25 ha are considered in the mapping. In the current exercise there was the necessity to adapt overseas experiences to the peculiarities of the European continent, especially considering that the EU does not contain large wilderness areas like other continents " | ABSTRACT: "The level of air pollution removal by green roofs in Chicago was quantified using a dry deposition model. The result showed that a total of 1675 kg of air pollutants was removed by 19.8 ha of green roofs in one year with O3 accounting for 52% of the total, NO2 (27%), PM10 (14%), and SO2 (7%). The highest level of air pollution removal occurred in May and the lowest in February. The annual removal per hectare of green roof was 85 kg/ha/yr. The amount of pollutants removed would increase to 2046.89 metric tons if all rooftops in Chicago were covered with intensive green roofs. Although costly, the installation of green roofs could be justified in the long run if the environmental benefits were considered. The green roof can be used to supplement the use of urban trees in air pollution control, especially in situations where land and public funds are not readily available." | Land Use/Land Cover (LULC) provides provisional, supporting, cultural, and regulating ecosystem services that contribute to ecological environments, enhance human health and living, have economic advantages for sustaining living organisms. LULC transformation due to enormous urban expansion diminishing Ecosystem Services Values (ESVs) and discouraging sustainability. Though unplanned LULC transformation practice became more prevalent in developing countries, comprehensive assessment of LULC changes and their influences in ESVs are rarely attempted. This study aimed to illustrate and forecast the LULC changes and their influences on ESVs change in Jashore using remote sensing technologies. ESVs estimation and change analysis were conducted by utilizing -derived LULC data of the year 2000, 2010, and 2020 with the corresponding global value coefficients of each LULC type which are previously published. For simulating future LULC and ESVs, Land Change Modeler of TerrSet Geospatial Monitoring and Modeling Software was used in Multi-Layer Perceptron-Markov Chain and Artificial Neural Network method. The decline of agricultural land by 13.13% and waterbody by 5.79% has resulted in the reduction of total ESVs US$0.23 million (24.47%) during 2000–2020. The forecasted result shows that the built-up area will be dominant LULC in the future, and ESVs of provisioning and cultural services will be diminished by $0.107 million, $63400.3 by 2050 with the declination of agricultural, waterbody, vegetation, and vacant land covers. The study signifies the importance of a strategic rational land-use plan to strictly monitor and control the encroachment of built-up areas into vegetation, waterbodies, and agricultural land in addition to scientific mitigative policies for ensuring ecological sustainability. | We introduce a new computational model designed to simulate and investigate reach-scale alluvial dynamics within a landscape evolution model. The model is based on the cellular automaton concept, whereby the continued iteration of a series of local process ‘rules’ governs the behaviour of the entire system. The model is a modified version of the CAESAR landscape evolution model, which applies a suite of physically based rules to simulate the entrainment, transport and deposition of sediments. The CAESAR model has been altered to improve the representation of hydraulic and geomorphic processes in an alluvial environment. In-channel and overbank flow, sediment entrainment and deposition, suspended load and bed load transport, lateral erosion and bank failure have all been represented as local cellular automaton rules. Although these rules are relatively simple and straightforward, their combined and repeatedly iterated effect is such that complex, non-linear geomorphological response can be simulated within the model. Examples of such larger-scale, emergent responses include channel incision and aggradation, terrace formation, channel migration and river meandering, formation of meander cutoffs, and transitions between braided and single-thread channel patterns. In the current study, the model is illustrated on a reach of the River Teifi, near Lampeter, Wales, UK. |
Specific Policy or Decision Context Cited
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Not reported | None identified | European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | Not reported | Transport of solids for characterizing rivers in the European Water Framework Directive (WFD) | None identified | None identified | None identified | None identified | None identified | None identified | Future rock lobster fisheries management | Future rock lobster fisheries management | climate change | None identified | None identified | None identified | None identified | None | None identified | N/A | None identified |
Biophysical Context
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Urban areas 3.0% of land in U.S. and Urban/community land (5.3%) in 2000. | Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Not applicable | Upper Mississipi River basin, elevation 142-194m, | The Aixola watershed drains into the Aixola reservoir, which has a cpacity of 2.73 x 10^6 m^3, and is used for water supply. The elevation ranges from 340 m at the outlet of the watershed to 750 m at the highest peak, with a mean elevation of 511 m a.s.l. Most slopes in the watershed are less than 30%. The region is characterized by a humid and temperate climate. The mean annual precipitation is about 1480 mm, distributed fairly evenly throughout the year.; the mean annual temperature is 12 degrees C; and the mean annual discharge is 600 mm (around 0.092 m^3 s^−1). Autochthonus vegetation is limited to small patches, and commercial foresty, mostly evergreen stands composed mainly of Pinus radiata (Monterey pine), occupies more than 80% of the watershed. The lithology is highly homogenous, with most of the bedrock (94%) consisting of impervious Upper Cretaceous Calcareous Flysch. The main types of soils are relatively deep cambisols and regosols, with depths ranging from 0.8 to 10 m and a silt-loam texture. During the 2003-2008 period, mean suspended sediment yield calculated for the watershed was 36 t km^-2. | Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter. | No additional description provided | No additional description provided | No additional description provided | No additional description provided | No additional description provided | No additional description provided | barrier reef and fringing reef in nearshore coastal marine system | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | wetlands | No additional description provided | restored, enhanced and created wetlands | 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). | Continential Scale | No additional description provided | Jashore city, Bangladesh | River Teifi, Lampeter, Wales |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | Four future climate change scenarios combining two IPCC SRES scenarios and two GCMs | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Fisheries exploitation; fishing vulnerability (of age classes) | Reef type, Sea level increase, storm conditions, seagrass conditions, coral conditions, vegetation types and conditions | fertilization | N/A | No scenarios presented | Sites, function or habitat focus | No scenarios presented | N.A. | No scenarios presented | No scenarios presented | Varying flow velocities and durations |
EM ID
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EM-24 | EM-66 | EM-94 | EM-97 |
EM-275 ![]() |
EM-379 | EM-418 | EM-423 | EM-456 | EM-462 | EM-465 |
EM-541 ![]() |
EM-542 ![]() |
EM-598 | EM-617 | EM-657 |
EM-729 ![]() |
EM-836 | EM-939 | EM-945 | EM-979 | EM-998 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method Only | Method + Application | Method + Application | Method Only |
New or Pre-existing EM?
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Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | New or revised model | Application of existing model | New or revised model | New or revised model | WESP - Urban Stormwater Treatment | 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-24 | EM-66 | EM-94 | EM-97 |
EM-275 ![]() |
EM-379 | EM-418 | EM-423 | EM-456 | EM-462 | EM-465 |
EM-541 ![]() |
EM-542 ![]() |
EM-598 | EM-617 | EM-657 |
EM-729 ![]() |
EM-836 | EM-939 | EM-945 | EM-979 | EM-998 |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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None | Doc-260 |
Doc-254 | Doc-256 ?Comment:Document 254 was also used as a source document for this EM |
Doc-142 | None | Doc-13 | Doc-317 | None | None | None | None | None | None | None | None | None | None | Doc-390 | None | None | Doc-439 | None | Doc-467 |
EM ID for related EM
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None | EM-65 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | None | None | EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | None | None | None | None | None | None | None | EM-593 | None | None | EM-718 | EM-734 | EM-837 | EM-941 | None | None | EM-997 |
EM Modeling Approach
EM ID
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EM-24 | EM-66 | EM-94 | EM-97 |
EM-275 ![]() |
EM-379 | EM-418 | EM-423 | EM-456 | EM-462 | EM-465 |
EM-541 ![]() |
EM-542 ![]() |
EM-598 | EM-617 | EM-657 |
EM-729 ![]() |
EM-836 | EM-939 | EM-945 | EM-979 | EM-998 |
EM Temporal Extent
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1989-2010 | Not reported | 2000 | 1980-2006 | 1961-2100 | 1969-2008 | 2006-2007, 2010 | 2013 | 2006-2007, 2010 | 2006-2007, 2010 | 2006-2007, 2010 | 1986-2115 | 2005-2013 | 1961-1990 | Not applicable |
1996-2003 ?Comment:All the ecological analyses are based on the production of a 1:10,000 scale map of land cover with detailed classes for the vegetation obtained by overlapping the photogrammetric analysis (AIMA flight 1996) and the 2003 land-use map. |
2010-2011 | Not applicable | Not applicable | July 2006 to July 2007 | 2000-2050 | Not applicable |
EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | Not applicable | time-dependent | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | Not applicable | future time | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | both | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable | both | Not applicable |
EM Time Continuity
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discrete | Not applicable | Not applicable | Not applicable | continuous | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | continuous |
EM Temporal Grain Size Value
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1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 10 | Not applicable |
EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Second | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Month | Year | Not applicable |
EM ID
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EM-24 | EM-66 | EM-94 | EM-97 |
EM-275 ![]() |
EM-379 | EM-418 | EM-423 | EM-456 | EM-462 | EM-465 |
EM-541 ![]() |
EM-542 ![]() |
EM-598 | EM-617 | EM-657 |
EM-729 ![]() |
EM-836 | EM-939 | EM-945 | EM-979 | EM-998 |
Bounding Type
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Geopolitical | Physiographic or Ecological | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Geopolitical | Geopolitical | Point or points | Not applicable | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | No location (no locational reference given) | Geopolitical | Geopolitical | Watershed/Catchment/HUC |
Spatial Extent Name
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United States | Central French Alps | EU-27 | East Fork Kaskaskia River watershed basin | Aixola watershed | H. J. Andrews LTER WS10 | Coastal zone surrounding St. Croix | Guanica Bay watershed | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Table Mountain National Park Marine Protected Area | Coast of Belize | Oak Park Research centre | Not applicable | Marecchia river catchment | Wetlands in idaho | East Midland | Not applicable | Chicago | Jashore city, Bangladesh | River Teifi |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 10-100 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 1-10 km^2 | 10-100 ha | 100-1000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 1-10 ha | Not applicable | 100-1000 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | >1,000,000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. |
EM ID
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EM-24 | EM-66 | EM-94 | EM-97 |
EM-275 ![]() |
EM-379 | EM-418 | EM-423 | EM-456 | EM-462 | EM-465 |
EM-541 ![]() |
EM-542 ![]() |
EM-598 | EM-617 | EM-657 |
EM-729 ![]() |
EM-836 | EM-939 | EM-945 | EM-979 | EM-998 |
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) |
spatially distributed (in at least some cases) ?Comment:See below, grain includes vertical, subsurface dimension. |
spatially distributed (in at least some cases) |
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 lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially 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) |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | volume, for 3-D 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 | length, for linear feature (e.g., stream mile) | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | map scale, for cartographic feature | Not applicable |
Spatial Grain Size
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1 m^2 | 20 m x 20 m | 10 km x 10 km | 1 km^2 | Average size 0.2 km^2 | 30 m x 30 m surface pixel and 2-m depth soil column | 10 m x 10 m | 30 m x 30 m | 10 m x 10 m | 10 m x 10 m | 10 m x 10 m | Not applicable | 1 meter | Not applicable | Not reported | 500 m x 1000 m | Not applicable | multiple unrelated sites | Pixel size | plot (green roof) size | 30m | Not applicable |
EM ID
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EM-24 | EM-66 | EM-94 | EM-97 |
EM-275 ![]() |
EM-379 | EM-418 | EM-423 | EM-456 | EM-462 | EM-465 |
EM-541 ![]() |
EM-542 ![]() |
EM-598 | EM-617 | EM-657 |
EM-729 ![]() |
EM-836 | EM-939 | EM-945 | EM-979 | EM-998 |
EM Computational Approach
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Numeric | Analytic | Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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EM ID
em.detail.idHelp
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EM-24 | EM-66 | EM-94 | EM-97 |
EM-275 ![]() |
EM-379 | EM-418 | EM-423 | EM-456 | EM-462 | EM-465 |
EM-541 ![]() |
EM-542 ![]() |
EM-598 | EM-617 | EM-657 |
EM-729 ![]() |
EM-836 | EM-939 | EM-945 | EM-979 | EM-998 |
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | No | No | Yes | No | Yes | Yes | Yes | Yes | Yes | No | No | Yes | Not applicable | Not applicable | No | Not applicable | No | Unclear | Yes | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | Yes | No | No | No | No | No | No | No | No | No | No | No |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
Not applicable | Not applicable | No | Not applicable | Not applicable | No | Yes | Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None |
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None | None | None | None | None | None | None | None | 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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No | Yes | Yes | Yes | Yes | No | Yes | No | Yes | Yes | Yes |
Yes ?Comment:A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data. |
No ?Comment:Used the SWAN model (see below for referenece) with Generation 1 or 2 wind-wave formulations to validate the wave development portion of the model. Booij N, Ris RC, Holthuijsen LH. A third-generation wave model for coastal regions 1. Model description and validation. J Geophys Res. American Geophysical Union; 1999;104: 7649?7666. |
Yes | Not applicable |
Yes ?Comment:R2 values of the analysis between the REQI, the capacity of land cover to provide ESs, and the Italian River Functionality Quality Index ? IFF. |
No | Not applicable | Unclear | No | Yes | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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Yes ?Comment:An error of sampling was reported, but not an error of estimation Estimation error was unknown and reported as likely larger than the error of sampling. |
No | No | Yes | No | No | No | No | No | No | No | No | No | No | Not applicable | Not applicable | No | Not applicable | No | No | Unclear | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | Unclear | Yes | No | No | No | No | No | No | No | No | No | Not applicable | Not applicable | No | Not applicable | Yes | No | Unclear | Not applicable |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | No | 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 | Unclear | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-24 | EM-66 | EM-94 | EM-97 |
EM-275 ![]() |
EM-379 | EM-418 | EM-423 | EM-456 | EM-462 | EM-465 |
EM-541 ![]() |
EM-542 ![]() |
EM-598 | EM-617 | EM-657 |
EM-729 ![]() |
EM-836 | EM-939 | EM-945 | EM-979 | EM-998 |
Comment:EM presents carbon storage and sequestration rates for country and by individual state |
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None |
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None | None | None | None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-24 | EM-66 | EM-94 | EM-97 |
EM-275 ![]() |
EM-379 | EM-418 | EM-423 | EM-456 | EM-462 | EM-465 |
EM-541 ![]() |
EM-542 ![]() |
EM-598 | EM-617 | EM-657 |
EM-729 ![]() |
EM-836 | EM-939 | EM-945 | EM-979 | EM-998 |
None | None | None | None | None | None |
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None | None | None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-24 | EM-66 | EM-94 | EM-97 |
EM-275 ![]() |
EM-379 | EM-418 | EM-423 | EM-456 | EM-462 | EM-465 |
EM-541 ![]() |
EM-542 ![]() |
EM-598 | EM-617 | EM-657 |
EM-729 ![]() |
EM-836 | EM-939 | EM-945 | EM-979 | EM-998 |
Centroid Latitude
em.detail.ddLatHelp
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40.16 | 45.05 | 50.53 | 38.69 | 43 | 44.25 | 17.73 | 17.96 | 17.73 | 17.73 | 17.73 | -34.18 | 18.63 | 52.86 | Not applicable | 43.89 | 44.06 | 52.22 | Not applicable | 41.88 | 23.95 | 52.04 |
Centroid Longitude
em.detail.ddLongHelp
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-99.79 | 6.4 | 7.6 | -89.1 | -1 | -122.33 | -64.77 | -67.04 | -64.77 | -64.77 | -64.77 | 18.35 | -88.22 | 6.54 | Not applicable | 12.3 | -114.69 | -0.91 | Not applicable | 87.65 | 89.12 | -4.39 |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | Not applicable | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | other | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Provided | Estimated | Provided | Provided | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Provided | Not applicable | Estimated | Estimated | Estimated | Not applicable | Provided | Provided | Estimated |
EM ID
em.detail.idHelp
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EM-24 | EM-66 | EM-94 | EM-97 |
EM-275 ![]() |
EM-379 | EM-418 | EM-423 | EM-456 | EM-462 | EM-465 |
EM-541 ![]() |
EM-542 ![]() |
EM-598 | EM-617 | EM-657 |
EM-729 ![]() |
EM-836 | EM-939 | EM-945 | EM-979 | EM-998 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Forests | Created Greenspace | Agroecosystems | Grasslands | Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Agroecosystems | Rivers and Streams | Forests | Barren | Forests | Near Coastal Marine and Estuarine | Inland Wetlands | Open Ocean and Seas | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Atmosphere | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Agroecosystems | Inland Wetlands | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Created Greenspace | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Urban forests | Subalpine terraces, grasslands, and meadows | Streams and near upstream environments | Row crop agriculture in Kaskaskia river basin | Forested watershed used for commercial forestry | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | Coral reefs | Multiple environmental types present | Coral reefs | Coral reefs | Coral reefs | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | coral reefs | farm pasture | Restored wetlands | Riparian zone along major river | created, restored and enhanced wetlands | restored landfills and conserved grasslands | Not applicable | urban green roofs | Urban city | River |
EM Ecological Scale
em.detail.ecoScaleHelp
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Zone within an ecosystem | Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-24 | EM-66 | EM-94 | EM-97 |
EM-275 ![]() |
EM-379 | EM-418 | EM-423 | EM-456 | EM-462 | EM-465 |
EM-541 ![]() |
EM-542 ![]() |
EM-598 | EM-617 | EM-657 |
EM-729 ![]() |
EM-836 | EM-939 | EM-945 | EM-979 | EM-998 |
EM Organismal Scale
em.detail.orgScaleHelp
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Species ?Comment:Trees were identified to species for the differential growth and biomass estimates part of the analysis. |
Community | Not applicable | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Guild or Assemblage | Guild or Assemblage | Guild or Assemblage | Individual or population, within a species | Guild or Assemblage | Not applicable | Not applicable |
Species ?Comment:Bird species for faunistic index of conservation. |
Not applicable | Individual or population, within a species | Not applicable | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-24 | EM-66 | EM-94 | EM-97 |
EM-275 ![]() |
EM-379 | EM-418 | EM-423 | EM-456 | EM-462 | EM-465 |
EM-541 ![]() |
EM-542 ![]() |
EM-598 | EM-617 | EM-657 |
EM-729 ![]() |
EM-836 | EM-939 | EM-945 | EM-979 | EM-998 |
None Available | None Available | None Available | None Available | None Available | None Available |
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None Available | None Available |
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None Available | None Available | None Available | None Available | None Available |
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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-24 | EM-66 | EM-94 | EM-97 |
EM-275 ![]() |
EM-379 | EM-418 | EM-423 | EM-456 | EM-462 | EM-465 |
EM-541 ![]() |
EM-542 ![]() |
EM-598 | EM-617 | EM-657 |
EM-729 ![]() |
EM-836 | EM-939 | EM-945 | EM-979 | EM-998 |
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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-24 | EM-66 | EM-94 | EM-97 |
EM-275 ![]() |
EM-379 | EM-418 | EM-423 | EM-456 | EM-462 | EM-465 |
EM-541 ![]() |
EM-542 ![]() |
EM-598 | EM-617 | EM-657 |
EM-729 ![]() |
EM-836 | EM-939 | EM-945 | EM-979 | EM-998 |
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None | None |
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None | None | None |
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None | None | None | None | None |
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