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Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-85 | EM-94 | EM-106 |
EM-375 
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EM-415 | EM-430 | EM-461 |
EM-480
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EM-630 |
EM-661
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EM-719
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EM-844 | EM-882 | EM-938 |
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EM Short Name
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Area and hotspots of flow regulation, South Africa | Reduction in pesticide runoff risk, Europe | Value of Habitat for Shrimp, Campeche, Mexico | VELMA hydro, Oregon, USA | Esocid spawning, St. Louis River, MN/WI, USA | Carbon sequestration, Guánica Bay, Puerto Rico | Presence of Euchema sp., St. Croix, USVI | Yasso07 - Land use SOC dynamics, China | WaterWorld v2, Santa Basin, Peru | Alwife phosphorus flux in lakes, Connecticut, USA | Seed mix for native plant establishment, IA, USA | Common yellowthroat abun, Piedmont region, USA | Human well being index for U.S. | OpenNSPECT v. 1.2 |
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EM Full Name
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Area and hotspots of water flow regulation, South Africa | Reduction in pesticide runoff risk, Europe | Value of Habitat for Shrimp, Campeche, Mexico | VELMA (visualizing ecosystems for land management assessments) hydro, Oregon, USA | Esocid spawning, St. Louis River estuary, MN & WI, USA | Carbon sequestration, Guánica Bay, Puerto Rico, USA | Relative presence of Euchema sp. (on reef), St. Croix, USVI | Yasso07 - Land use dynamics of Soil Organic Carbon in the Loess Plateau, China | WaterWorld v2, Santa Basin, Peru | Net phosphorus flux in freshwater lakes from alewives, Connecticut, USA | Cost-effective seed mix design for native plant establishment, Iowa, USA | Common yellowthroat abundance, Piedmont ecoregion, USA | Human well being index for multiple scales, United States | OpenNSPECT v. 1.2 |
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EM Source or Collection
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None | None | None | US EPA | US EPA | US EPA | US EPA | None | None | None | None | None | US EPA | None |
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EM Source Document ID
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271 | 255 | 227 | 13 | 332 | 338 | 335 | 344 | 368 | 383 | 394 | 405 | 421 | 431 |
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Document Author
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Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Lautenbach, S., Maes, J., Kattwinkel, M., Seppelt, R., Strauch, M., Scholz, M., Schulz-Zunkel, C., Volk, M., Weinert, J. and Dormann, C. | Barbier, E. B., and Strand, I. | Abdelnour, A., Stieglitz, M., Pan, F. and McKane, R. B. | Ted R. Angradi, David W. Bolgrien, Jonathon J. Launspach, Brent J. Bellinger, Matthew A. Starry, Joel C. Hoffman, Mike E. Sierszen, Anett S. Trebitz, and Tom P. Hollenhorst | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu | Van Soesbergen, A. and M. Mulligan | West, D. C., A. W. Walters, S. Gephard, and D. M. Post | Meissen, J. | Riffel, S., Scognamillo, D., and L. W. Burger | Smith, L.M., Harwell, L.C., Summers, J.K., Smith, H.M., Wade, C.M., Straub, K.R. and J.L. Case | Eslinger, David L., H. Jamieson Carter, Matt Pendleton, Shan Burkhalter, Margaret Allen |
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Document Year
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2008 | 2012 | 1998 | 2011 | 2016 | 2017 | 2014 | 2015 | 2018 | 2010 | 2018 | 2008 | 2014 | 2012 |
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Document Title
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Mapping ecosystem services for planning and management | Mapping water quality-related ecosystem services: concepts and applications for nitrogen retention and pesticide risk reduction | Valuing mangrove-fishery linkages: A case study of Campeche, Mexico | Catchment hydrological responses to forest harvest amount and spatial pattern | Mapping ecosystem service indicators of a Great Lakes estuarine Area of Concern | 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 | Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | Nutrient loading by anadromous alewife (Alosa pseudoharengus): contemporary patterns and predictions for restoration efforts | Cost-effective seed mix design and first-year management | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | A U.S. Human Well-being index (HWBI) for multiple scales: linking service provisioning to human well-being endpoints (2000-2010) | “OpenNSPECT: The Open-source Nonpoint Source Pollution and Erosion Comparison Tool.” NOAA Office for Coastal Management, Charleston, South Carolina. Accessed (11/2022) at https://coast.noaa.gov/digitalcoast/tools/opennspect.html |
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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 |
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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 report | Published journal manuscript | Published EPA report | Webpage |
Software and Access
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EM ID
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EM-85 | EM-94 | EM-106 |
EM-375
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EM-415 | EM-430 | EM-461 |
EM-480
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EM-630 |
EM-661
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EM-719
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EM-844 | EM-882 | EM-938 |
| Not applicable | Not applicable | Not applicable | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable | Not applicable | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | www.policysupport.org/waterworld | Not applicable | Not applicable | Not applicable | Not applicable | https://coast.noaa.gov/digitalcoast/tools/opennspect.html | |
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Contact Name
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Benis Egoh | Sven Lautenbach | E.B. Barbier | A. Abdelnour | Ted R. Angradi | Susan H. Yee | Susan H. Yee | Xing Wu | Arnout van Soesbergen | Derek C. West | Justin Meissen | Sam Riffell | Lisa Smith | Not reported |
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Contact Address
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Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany | Environment Department, University of York, York YO1 5DD, UK | Dept. of Civil and Environmental Engineering, Goergia Institute of Technology, Atlanta, GA 30332-0335, USA | United States Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboraty, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804 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 | Chinese Academy of Sciences, Beijing 100085, China | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | Dept. of Ecology and Evolutionary Biology, Yale University, 165 Prospect Street, New Haven, CT 06511, USA | Tallgrass Prairie Center, University of Northern Iowa | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | 1 Sabine Island Dr, Gulf Breeze, FL 32561 | NOAA Coastal Services Center, 2234 South Hobson Avenue Charleston, South Carolina 29405-2413 |
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Contact Email
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Not reported | sven.lautenbach@ufz.de | Not reported | abdelnouralex@gmail.com | angradi.theodore@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | xingwu@rceesac.cn | arnout.van_soesbergen@kcl.ac.uk | derek.west@yale.edu | Not reported | sriffell@cfr.msstate.edu | smith.lisa@epa.gov | Not reported |
EM Description
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EM ID
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EM-85 | EM-94 | EM-106 |
EM-375
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EM-415 | EM-430 | EM-461 |
EM-480
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EM-630 |
EM-661
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EM-719
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EM-844 | EM-882 | EM-938 |
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Summary Description
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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…Water flow regulation is a function of the storage and retention components of the water supply service (de Groot et al., 2002). The ability of a catchment to regulate flows is directly related to the volume of water that is retained or stored in the soil and underlying aquifers as moisture or groundwater; and the infiltration rate of water which replenishes the stored water (Kittredge, 1948; Farvolden, 1963). Groundwater contribution to surface runoff is the most direct measure of the water regulation function of a catchment. Data on the percentage contribution of groundwater to baseflows were obtained from DWAF (2005) per quaternary catchment and expressed as a percentage of total surface runoff, the range and hotspot being defined as areas with at least 10% and 30%, respectively (Colvin et al., 2007)." | 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." | AUTHOR'S DESCRIPTION: "We assume throughout that shrimp harvesting occurs through open access management that yields production which is exported internationally, and we modify a standard open access fishery model to account explicitly for the effect of the mangrove area on carrying capacity and thus production.We derive the conditions determining the long-run equilibrium of the model, including the comparative static effects of a change in mangrove area, on this equilibrium. Through regressing a relationship between shrimp harvest, effort and mangrove area over time, we estimate parameters based on the combinations of the bioeconomic parameters of the model determining the comparative statics. By incorporating additional economic data, we are able to simulate an estimate of the effect of changes in mangrove area in Laguna de Terminos on the production and value of shrimp harvests in Campeche state." (153) | AUTHOR'S DESCRIPTION: "VELMA uses a distributed soil column framework to simulate the movement of water and nutrients (NH4, NO3, DON, DOC) within the soil, between the soil and the vegetation, and between the soil surface and vegetation to the atmosphere. The soil column model consists of three coupled submodels: (1) a hydrological model that simulates vertical and lateral movement of water within soil, losses of water from soil and vegetation to the atmosphere, and the growth and ablation of the seasonal snowpack, (2) a soil temperature model that simulates daily soil layer temperatures from surface air temperature and snow depth, and (3) a plant-soil model that simulates C and N dynamics. (Note: for the purposes of this paper we describe only the hydrologic aspects of the model.) Each soil column consists of n soil layers. Soil water balance is solved for each layer (equations (A1)–(A6)). We employ a simple logistic function that is based on the degree of saturation to capture the breakthrough characteristics of soil water drainage (equations (A7)–(A9)). Evapotranspiration increases exponentially with increasing soil water storage and asymptotically approaches the potential evapotranspiration rate (PET) as water storage reaches saturation [Davies and Allen, 1973; Federer, 1979, 1982; Spittlehouse and Black, 1981] (equation (A12)). PET is estimated using a simple temperature-based method [Hamon, 1963] (equation (A13)). An evapotranspiration recovery function is used to account for the effects of changes in stand-level transpiration rates during succession, e.g., after fire or harvest (equation (B2)). Snowmelt is estimated using the degree-day approach [Rango and Martinec, 1995] and accounts for the effects of rain on snow [Harr, 1981] (equation (A10)). [15] The soil column model is placed within a catchment framework to create a spatially distributed model applicable to watersheds and landscapes. Adjacent soil columns interact with each other through the downslope lateral transport of water (Figures A1 and A2). Surface and subsurface lateral flow are routed using a multiple flow direction method [Freeman, 1991; Quinn et al., 1991]. As with vertical drainage of soil water, lateral subsurface downslope flow is modeled using a simple logistic function multiplied by a factor to account for the local topographic slope angle (equation (A16))… The model is forced with daily temperature and precipitation. Daily observed streamflow data is used to calibrate and validate simulated discharge." "Model calibration is needed to accurately capture the pre- and postharvest dynamics at WS10. This model calibration consists of two simulations: an old-growth simulation for the period 1969-1974 and a post-harvest simulation for the period 1975-2008." Two additional sets of VELMA simulations examining changes in streamflow are presented in the paper, but not included here. Twenty simulations were conducted varying the location across the watershed of a 20% har | ABSTRACT: "Estuaries provide multiple ecosystem services from which humans benefit…We described an approach, with examples, for assessing how local-scale actions affect the extent and distribution of coastal ecosystem services, using the St. Louis River estuary (SLRE) of western Lake Superior as a case study. We based our approach on simple models applied to spatially explicity biophysical data that allows us to map the providing area of ecosystem services at high resolution (10-m^2 pixel) across aquatic and riparian habitats…Aspects of our approach can be adapted by communities for use in support of local decision-making." AUTHOR'S DESCRIPTION: "We derived the decision criteria used to map the IEGS habitat proxy of esocid spawning from habitat suitability information for two species that have similar but not identical spawning habitat and behavior." | AUTHOR'S DESCRIPTION: "In addition to affecting water quality, the ecosystem services of nitrogen retention, phosphorous retention, and sediment retention were also considered to contribute to stakeholder goals of maintaining the productivity of agricultural land and reducing soil loss. 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, applying the mean of rates for natural sub-tropical ecosystems obtained from the literature." | 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:...(4) density of Euchema sp. seaweed," | ABSTRACT: "Land use changes are known to significantly affect the soil C balance by altering both C inputs and losses. Since the late 1990s, a large area of the Loess Plateau has undergone intensive land use changes during several ecological restoration projects to control soil erosion and combat land degradation, especially in the Grain for Green project. By using remote sensing techniques and the Yasso07 model, we simulated the dynamics of soil organic carbon (SOC) stocks in the Yangjuangou catchment of the Loess Plateau. The performance of the model was evaluated by comparing the simulated results with the intensive field measurements in 2006 and 2011 throughout the catchment. SOC stocks and NPP values of all land use types had generally increased during our study period. The average SOC sequestration rate in the upper 30 cm soil from 2006 to 2011 in the Yangjuangou catchment was approximately 44 g C m-2 yr-1, which was comparable to other studies in the Loess Plateau. Forest and grassland showed a more effective accumulation of SOC than the other land use types in our study area. The Yasso07 model performed reasonably well in predicting the overall dynamics of SOC stock for different land use change types at both the site and catchment scales. The assessment of the model performance indicated that the combination of Yasso07 model and remote sensing data could be used for simulating the effect of land use changes on SOC stock at catchment scale in the Loess Plateau." | ABSTRACT: "Water resources in the Santa basin in the Peruvian Andes are increasingly under pressure from climate change and population increases. Impacts of temperature-driven glacier retreat on stream flow are better studied than those from precipitation changes, yet present and future water resources are mostly dependent on precipitation which is more difficult to predict with climate models. This study combines a broad range of projections from climate models with a hydrological model (WaterWorld), showing a general trend towards an increase in water availability due to precipitation increases over the basin. However, high uncertainties in these projections necessitate the need for basin-wide policies aimed at increased adaptability." AUTHOR'S DESCRIPTION: "WaterWorld is a fully distributed, process-based hydrological model that utilises remotely sensed and globally available datasets to support hydrological analysis and decision-making at national and local scales globally, with a particular focus on un-gauged and/or data-poor environments, which makes it highly suited to this study. The model (version 2) currently runs on either 10 degree tiles, large river basins or countries at 1-km2 resolution or 1 degree tiles at 1-ha resolution utilising different datasets. It simulates a hydrological baseline as a mean for the period 1950-2000 and can be used to calculate the hydrological impact of scenarios of climate change, land use change, land management options, impacts of extractives (oil & gas and mining) and impacts of changes in population and demography as well as combinations of these. The model is ‘self parameterising’ (Mulligan, 2013a) in the sense that all data required for model application anywhere in the world is provided with the model, removing a key barrier to model application. However, if users have better data than those provided, it is possible to upload these to WaterWorld as GIS files and use them instead. Results can be viewed visually within the web browser or downloaded as GIS maps. The model’s equations and processes are described in more detail in Mulligan and Burke (2005) and Mulligan (2013b). The model parameters are not routinely calibrated to observed flows as it is designed for hydrological scenario analysis in which the physical basis of its parameters must be retained and the model is also often used in un-gauged basins. Calibration is inappropriate under these circumstances (Sivapalan et al., 2003). The freely available nature of the model means that anyone can apply it and replicate the results shown here. WaterWorld’s (V2) snow and ice module is capable of simulating the processes of melt water production, snow fall and snow pack, making this version highly suited to the current application. The model component is based on a full energy-balance for snow accumulation and melting based on Walter et al., (2005) with input data provided globally by the SimTerra database (Mulligan, 2011) upon which the model r | ABSTRACT: "Anadromous alewives (Alosa pseudoharengus) have the potential to alter the nutrient budgets of coastal lakes as they migrate into freshwater as adults and to sea as juveniles. Alewife runs are generally a source of nutrients to the freshwater lakes in which they spawn, but juveniles may export more nutrients than adults import in newly restored populations. A healthy run of alewives in Connecticut imports substantial quantities of phosphorus; mortality of alewives contributes 0.68 g P_fish–1, while surviving fish add 0.18 g P, 67% of which is excretion. Currently, alewives contribute 23% of the annual phosphorus load to Bride Lake, but this input was much greater historically, with larger runs of bigger fish contributing 2.5 times more phosphorus in the 1960s..." AUTHOR'S DESCRIPTION: "Here, we evaluate the patterns of net nutrient loading by alewives over a range of population sizes. We concentrate on phosphorus, as it is generally the nutrient that limits production in the lake ecosystems in which alewives spawn (Schindler 1978). First, we estimate net alewife nutrient loading and parameterize an alewife nutrient loading model using data from an existing run of anadromous alewives in Bride Lake. We then compare the current alewife nutrient load to that in the 1960s when alewives were more numerous and larger. Next, since little is known about the actual patterns of nutrient loading during restoration, we predict the net nutrient loading for a newly restored population across a range of adult escapement… Anadromous fish move nutrients both into and out of freshwater ecosystems, although inputs are typically more obvious and much better studied (Moore and Schindler 2004). Net loading into freshwater ecosystems is fully described as inputs due to adult mortality, gametes, and direct excretion of nutrients minus the removal of nutrients from freshwater ecosystems by juvenile fish when they emigrate… Our research was conducted at Bride Lake and Linsley Pond in Connecticut. Bride Lake contains an anadromous alewife population that we used to both evaluate contemporary and historic net nutrient loading by an alewife population and parameterize our general alewife nutrient loading model." | AUTHOR'S DESCRIPTION: "Restoring ecosystem services at scale requires executing conservation programs in a way that is resource and cost efficient as well as ecologically effective…Seed mix design is one of the largest determinants of project cost and ecological outcomes for prairie reconstructions. In particular, grass-to-forb seeding ratio affects cost since forb seed can be much more expensive relative to grass species (Prairie Moon Nursery 2012). Even for seed mixes with the same overall seeding rates, a mix with a low grass-to-forb seeding ratio is considerably more expensive than one with a high grass-to-forb ratio. Seeding rates for different plant functional groups that are too high or low may also adversely affect ecological outcomes…First-year management may also play a role in cost-effective prairie reconstruction. Post-agricultural sites where restoration typically occurs are often quickly dominated by fast-growing annual weeds by the time sown prairie seeds begin germinating (Smith et al. 2010)… Williams and others (2007) showed that prairie seedlings sown into established warm-season grasses were reliant on high light conditions created by frequently mowing tall vegetation in order to survive in subsequent years…Our objective was to compare native plant establishment and cost effectiveness with and without first-year mowing for three different seed mixes that differed in grass to forb ratio and soil type customization. With knowledge of plant establishment, cost effectiveness, and mowing management outcomes, conservation practitioners will be better equipped to restore prairie efficiently and successfully." | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. " | Executive summary: "The HWBI is a composite assessment covering 8 domains based on 25 indicators measured using 80 different metrics. Service flow and stock assessments include 7 economic services (23 indicators, 40 metrics), 5 ecosystem services (8 indicators, 24 metrics) and 10 social services (37 indicators, 76 metrics). Data from 64 data sources were included in the HWBI and services provisioning characterizations (Fig. ES-3). For each U.S. county, state, and GSS region, data were acquired or imputed for the 2000-2010 time period resulting in over 1.5 million data points included in the full assessment linking service flows to well-being endpoints. The approaches developed for calculation of the HWBI, use of relative importance values, service stock characterization and functional modeling are transferable to smaller scales and specific population groups. Additionally, tracked over time, the HWBI may be useful in evaluating the sustainability of decisions in terms of EPA’s Total Resources Impact Outcome (TRIO) approaches. " | "This open-source version of the Nonpoint Source Pollution and Erosion Comparison Tool is used to investigate potential water quality impacts from climate change and development to other land uses. The downloadable tool is designed to be broadly applicable for coastal and noncoastal areas alike. Tool functions simulate erosion, pollution, and the accumulation from overland flow. OpenNSPECT uses spatial elevation data to calculate flow direction and flow accumulation throughout a watershed. To do this, land cover, precipitation, and soils data are processed to estimate runoff volume at both the local and watershed levels. Coefficients representing the contribution of each land cover class to the expected pollutant load are also applied to land cover data to approximate total pollutant loads. These coefficients are taken from published sources or can be derived from local water quality studies. The output layers display estimates of runoff volume, pollutant loads, pollutant concentration, and total sediment yield. Requires MapWindow GIS v.4.8.8 (open source software)" |
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Specific Policy or Decision Context Cited
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None identified | European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | None identified | None identified | Federal delisting of an area of concern (AOC) | None identified | None identified | None identified | None identified | Restoration and management of diadromous fish runs in coastal New England | Seed mix design and management practices for native plant restoration | None reported | None reported | None identified |
|
Biophysical Context
|
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. | Not applicable | Gulf of Mexico; mangrove-lagoon system | 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. Mean annual precipitation is 2300 mm and falls primarily as rain between October and April. Total rainfall during June– September averages 200 mm. Snow rarely persists longer than a couple of weeks and usually melts within 1 to 2 days. Average annual streamflow is 1600 mm, which is approximately 70% of annual precipitation. Soils are of the Frissel series, classified as Typic Dystrochrepts with fine loamy to loamy-skeletal texture that are generally deep and well drained. These soils quickly transmit subsurface water to the stream. Prior to the 1975 100% clearcut, WS10 was a 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). The dominant vegetation of WS10 today is a 35 year old mixed Douglasfir and western hemlock stand. | No additional description provided | No additional description provided | No additional description provided | Agricultural plain, hills, gulleys, forest, grassland, Central China | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | Bride Lake is 28.7 ha and linked to Long Island Sound by the 3.3 km Bride Brook. | The soils underlying the study site are primarily poorly drained Clyde clay loams, with a minor component of somewhat poorly drained Floyd loams in the northwest (NRCS 2016). Topographically, the study site is level, and slopes do not exceed 5% grade. Land use prior to this experiment was agricultural, with corn and soybeans consistently grown in rotation at the site. | Conservation Reserve Program lands left to go fallow | Not applicable | No additional description provided |
|
EM Scenario Drivers
em.detail.scenarioDriverHelp
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|
No scenarios presented | No scenarios presented | No scenarios presented | Stand age; old-growth (pre-harvest), and harvested (postharvest) | The effect of habitat restoration on esocid spawning area was simulated by varying biophysical changes. | No scenarios presented | No scenarios presented | Land use change | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | current and historical run size | No scenarios presented | N/A | geographic region | No scenarios presented |
EM Relationship to Other EMs or Applications
|
EM ID
em.detail.idHelp
?
|
EM-85 | EM-94 | EM-106 |
EM-375
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EM-415 | EM-430 | EM-461 |
EM-480
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EM-630 |
EM-661
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EM-719
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EM-844 | EM-882 | EM-938 |
|
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
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|
Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method Only |
|
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
em.detail.relatedEmHelp
?
|
EM ID
em.detail.idHelp
?
|
EM-85 | EM-94 | EM-106 |
EM-375
|
EM-415 | EM-430 | EM-461 |
EM-480
|
EM-630 |
EM-661
|
EM-719
|
EM-844 | EM-882 | EM-938 |
|
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
Doc-271 |
Doc-254 | Doc-256 ?Comment:Document 254 was also used as a source document for this EM |
None | Doc-317 | None | None | None | Doc-343 | Doc-342 | None | None | Doc-395 | Doc-405 | Doc-418 | None |
|
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
EM-86 | EM-87 | EM-88 | None | EM-185 | EM-319 | EM-379 | EM-380 | EM-605 | EM-884 | EM-883 | EM-887 | None | None | None | EM-466 | EM-467 | EM-469 | EM-485 | None | EM-667 | EM-672 | EM-674 | EM-673 | EM-728 | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-843 | EM-845 | EM-846 | EM-847 | EM-880 | EM-940 |
EM Modeling Approach
EM Relationship to Time
|
EM ID
em.detail.idHelp
?
|
EM-85 | EM-94 | EM-106 |
EM-375
|
EM-415 | EM-430 | EM-461 |
EM-480
|
EM-630 |
EM-661
|
EM-719
|
EM-844 | EM-882 | EM-938 |
|
EM Temporal Extent
em.detail.tempExtentHelp
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|
Not reported | 2000 | 1980-1990 | 1969-2008 | 2013 | 1978 - 2013 | 2006-2007, 2010 | 1969-2011 | 1950-2071 | 1960"s and early 2000's | 2015-2017 | 2008 | 2000-2010 | Not applicable |
|
EM Time Dependence
em.detail.timeDependencyHelp
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|
time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary |
|
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | past time | both | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
|
EM Time Continuity
em.detail.continueDiscreteHelp
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|
Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable |
|
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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|
Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | 1 | Not applicable | Not applicable | Not applicable |
|
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
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Not applicable | Not applicable | Year | Day | Not applicable | Not applicable | Not applicable | Year | Month | Not applicable | Year | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
|
EM ID
em.detail.idHelp
?
|
EM-85 | EM-94 | EM-106 |
EM-375
|
EM-415 | EM-430 | EM-461 |
EM-480
|
EM-630 |
EM-661
|
EM-719
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EM-844 | EM-882 | EM-938 |
|
Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Geopolitical | Physiographic or Ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Other | Physiographic or ecological | Geopolitical | Not applicable |
|
Spatial Extent Name
em.detail.extentNameHelp
?
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South Africa | EU-27 | Laguna de Terminos Mangrove system | H. J. Andrews LTER WS10 | St. Louis River estuary | Guanica Bay watershed | Coastal zone surrounding St. Croix | Yangjuangou catchment | Santa Basin | Bride Lake and Linsley Pond | Iowa State University Northeast Research and Demonstration Farm | Piedmont Ecoregion | Continental U.S. | Not applicable |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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>1,000,000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 10-100 ha | 10-100 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 1-10 km^2 | 10,000-100,000 km^2 | 10-100 ha | <1 ha | 100,000-1,000,000 km^2 | >1,000,000 km^2 | Not applicable |
Spatial Distribution of Computations
|
EM ID
em.detail.idHelp
?
|
EM-85 | EM-94 | EM-106 |
EM-375
|
EM-415 | EM-430 | EM-461 |
EM-480
|
EM-630 |
EM-661
|
EM-719
|
EM-844 | EM-882 | EM-938 |
|
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially 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) |
|
Spatial Grain Type
em.detail.spGrainTypeHelp
?
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other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | 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 | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature |
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Spatial Grain Size
em.detail.spGrainSizeHelp
?
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Distributed by catchments with average size of 65,000 ha | 10 km x 10 km | 1 km x 1 km | 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 | 30m x 30m | 1 km2 | Not applicable | 20 ft x 28 ft | Not applicable | county | 30 m |
EM Structure and Computation Approach
|
EM ID
em.detail.idHelp
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EM-85 | EM-94 | EM-106 |
EM-375
|
EM-415 | EM-430 | EM-461 |
EM-480
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EM-630 |
EM-661
|
EM-719
|
EM-844 | EM-882 | EM-938 |
|
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Numeric | * | Analytic | Analytic | Analytic | Numeric | Analytic |
|
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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None |
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Model Checking Procedures Used
|
EM ID
em.detail.idHelp
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EM-85 | EM-94 | EM-106 |
EM-375
|
EM-415 | EM-430 | EM-461 |
EM-480
|
EM-630 |
EM-661
|
EM-719
|
EM-844 | EM-882 | EM-938 |
|
Model Calibration Reported?
em.detail.calibrationHelp
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|
No | No | Yes | Yes | No | No | Yes | No | No | Yes | Not applicable | Yes | No | Not applicable |
|
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | Yes | Yes | No | No | No |
Yes ?Comment:p value: p<0.001 |
No | No | Not applicable | No | No | Not applicable |
|
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None |
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None | None | None |
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None | None | None | None | None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | Yes | No | No | No | No | Yes | No | Yes | No | No | No | No | Not applicable |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | Yes | No | No | No | No | No | No | No | Not applicable | No | Unclear | Not applicable |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | Yes | No | No | No | No | No | No | Yes | Not applicable | Yes | Yes | Not applicable |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | Not applicable | Unclear | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Unclear | Yes | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
em.detail.relationToSpaceTerrestrialHelp
?
| EM-85 | EM-94 | EM-106 |
EM-375
|
EM-415 | EM-430 | EM-461 |
EM-480
|
EM-630 |
EM-661
|
EM-719
|
EM-844 | EM-882 | EM-938 |
|
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None |
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None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
?
| EM-85 | EM-94 | EM-106 |
EM-375
|
EM-415 | EM-430 | EM-461 |
EM-480
|
EM-630 |
EM-661
|
EM-719
|
EM-844 | EM-882 | EM-938 |
| None | None |
|
None | None |
|
|
None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
|
EM ID
em.detail.idHelp
?
|
EM-85 | EM-94 | EM-106 |
EM-375
|
EM-415 | EM-430 | EM-461 |
EM-480
|
EM-630 |
EM-661
|
EM-719
|
EM-844 | EM-882 | EM-938 |
|
Centroid Latitude
em.detail.ddLatHelp
?
|
-30 | 50.53 | 18.61 | 44.15 | 46.74 | 17.96 | 17.73 | 36.7 | -9.05 | 41.33 | 42.93 | 36.23 | 39.83 | Not applicable |
|
Centroid Longitude
em.detail.ddLongHelp
?
|
25 | 7.6 | -91.55 | -122.2 | -92.14 | -67.02 | -64.77 | 109.52 | -77.81 | -72.24 | -92.57 | -81.9 | -98.58 | Not applicable |
|
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable |
|
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Provided | Estimated | Estimated | Not applicable |
Environments and Scales Modeled
|
EM ID
em.detail.idHelp
?
|
EM-85 | EM-94 | EM-106 |
EM-375
|
EM-415 | EM-430 | EM-461 |
EM-480
|
EM-630 |
EM-661
|
EM-719
|
EM-844 | EM-882 | EM-938 |
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Near Coastal Marine and Estuarine | Rivers and Streams | Ground Water | Forests | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Inland Wetlands | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Agroecosystems | None | Rivers and Streams | Lakes and Ponds | Agroecosystems | Grasslands | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
|
Specific Environment Type
em.detail.specificEnvTypeHelp
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|
Not reported | Streams and near upstream environments | Mangrove | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | freshwater estuary | 13 LULC were used | Coral reefs | Loess plain | tropical, coastal to montane | Coastal lakes and ponds and associated streams | Research farm in historic grassland | grasslands | All land of the continental US | Coastal and non-coastal |
|
EM Ecological Scale
em.detail.ecoScaleHelp
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|
Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is coarser 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 | Other or unclear (comment) | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
em.detail.nameOfOrgsOrGroupsHelp
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|
EM ID
em.detail.idHelp
?
|
EM-85 | EM-94 | EM-106 |
EM-375
|
EM-415 | EM-430 | EM-461 |
EM-480
|
EM-630 |
EM-661
|
EM-719
|
EM-844 | EM-882 | EM-938 |
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Not applicable | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Species | Not applicable | Not applicable | Individual or population, within a species | Community | Species | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
?
| EM-85 | EM-94 | EM-106 |
EM-375
|
EM-415 | EM-430 | EM-461 |
EM-480
|
EM-630 |
EM-661
|
EM-719
|
EM-844 | EM-882 | EM-938 |
| None Available | None Available |
|
None Available |
|
None Available |
|
None Available | None Available |
|
None Available |
|
None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
em.detail.cicesHelp
?
| EM-85 | EM-94 | EM-106 |
EM-375
|
EM-415 | EM-430 | EM-461 |
EM-480
|
EM-630 |
EM-661
|
EM-719
|
EM-844 | EM-882 | EM-938 |
|
|
|
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None |
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|
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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>
fegs1Help
?
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
?
| EM-85 | EM-94 | EM-106 |
EM-375
|
EM-415 | EM-430 | EM-461 |
EM-480
|
EM-630 |
EM-661
|
EM-719
|
EM-844 | EM-882 | EM-938 |
| None | None |
|
None |
|
None |
|
None | None | None |
|
|
None | None |