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-12 ![]() |
EM-83 | EM-194 |
EM-224 ![]() |
EM-403 ![]() |
EM-457 | EM-462 | EM-491 |
EM-542 ![]() |
EM-618 ![]() |
EM-672 ![]() |
EM-706 | EM-838 | EM-875 | EM-876 | EM-936 | EM-997 |
EM Short Name
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Evoland v3.5 (bounded growth), Eugene, OR, USA | Soil carbon and plant traits, Central French Alps | Coral and land development, St.Croix, VI, USA | FORCLIM v2.9, West Cascades, OR, USA | HexSim, tule elk, California, USA | Visitation to reef dive sites, St. Croix, USVI | Value of finfish, St. Croix, USVI | EnviroAtlas - Crops with no pollinator habitat | Coastal protection in Belize | WaterWorld v2, Santa Basin, Peru | Alewife nutrients in stream food web, CT, USA | WESP Method | Eastern meadowlark abundance, Piedmont region, USA | Valuing environmental ed., New York, New York | Neighborhood greenness and health, FL, USA | i-Tree species selector v. 4.0 | CEASAR and TRACER models, EU |
EM Full Name
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Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA | Soil carbon potential estimated from plant functional traits, Central French Alps | Coral colony density and land development, St.Croix, Virgin Islands, USA | FORCLIM (FORests in a changing CLIMate) v2.9, West Cascades, OR, USA | HexSim, tule elk, California, USA | Visitation to dive sites (reef), St. Croix, USVI | Relative value of finfish (on reef), St. Croix, USVI | US EPA EnviroAtlas - Acres of pollinated crops with no nearby pollinator habitat, USA | Coastal Protection provided by Coral, Seagrasses and Mangroves in Belize: | WaterWorld v2, Santa Basin, Peru | Alewife derived nutrients in stream food web, Connecticut, USA | Method for the Wetland Ecosystem Services Protocol (WESP) | Eastern meadowlark abundance, Piedmont ecoregion, USA | Valuing environmental education, Hudson River Park, New York, New York | Neighborhood greenness and chronic health conditions in Medicare beneficiaries, Miami-Dade County, Florida, USA | i-Tree species selector v. 4.0 | Modelling remediation scenarios in historical mining catchments |
EM Source or Collection
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Envision | EU Biodiversity Action 5 | US EPA | US EPA | US EPA | US EPA | US EPA | US EPA | EnviroAtlas | InVEST | None | None | None | None | None | None | i-Tree | None |
EM Source Document ID
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47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
260 | 96 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
328 ?Comment:The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships. |
335 | 335 | 262 | 350 | 368 | 384 | 390 | 405 | 416 | 417 |
426 ?Comment:Doc# 427 is an additional source for this EM. |
467 |
Document Author
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Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Huber, P. R., S. E. Greco, N. H. Schumaker, and J. Hobbs | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | US EPA Office of Research and Development - National Exposure Research Laboratory | Guannel, G., Arkema, K., Ruggiero, P., and G. Verutes | Van Soesbergen, A. and M. Mulligan | Walters, A. W., R. T. Barnes, and D. M. Post | Adamus, P. R. | Riffel, S., Scognamillo, D., and L. W. Burger | Hutcheson, W. Hoagland, P., and D. Jin | Brown, S. C., J. Lombard, K. Wang, M. M. Byrne, M. Toro, E. Plater-Zyberk, D. J. Feaster, J. Kardys, M. I. Nardi, G. Perez-Gomez, H. M. Pantin, and J. Szapocznik | i-Tree | Gamarra, J. G., Brewer, P. A., Macklin, M. G., & Martin, K. |
Document Year
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2008 | 2011 | 2011 | 2007 | 2014 | 2014 | 2014 | 2013 | 2016 | 2018 | 2009 | 2016 | 2008 | 2018 | 2016 | None | 2014 |
Document Title
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Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | A priori assessment of reintroduction strategies for a native ungulate: using HexSim to guide release site selection | 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 | EnviroAtlas - National | The Power of Three: Coral Reefs, Seagrasses and Mangroves Protect Coastal Regions and Increase Their Resilience | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | Anadromous alewives (Alosa pseudoharengus) contribute marine-derived nutrients to coastal stream food webs | Manual for the Wetland Ecosystem Services Protocol (WESP) v. 1.3. | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Valuing environmental education as a cultural ecosystem service at Hudson River Park | Neighborhood greenness and chronic health conditions in Medicare beneficiaries | i-Tree Species Selector User's Manual v. 4.0 | Modelling remediation scenarios in historical mining catchments |
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 |
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 on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Webpage | Published journal manuscript |
EM ID
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EM-12 ![]() |
EM-83 | EM-194 |
EM-224 ![]() |
EM-403 ![]() |
EM-457 | EM-462 | EM-491 |
EM-542 ![]() |
EM-618 ![]() |
EM-672 ![]() |
EM-706 | EM-838 | EM-875 | EM-876 | EM-936 | EM-997 |
http://evoland.bioe.orst.edu/ ?Comment:Software is likely available. |
Not applicable | Not applicable | Not applicable | http://www.hexsim.net/download | Not applicable | Not applicable | https://www.epa.gov/enviroatlas | Not identified in paper | www.policysupport.org/waterworld | Not applicable |
http://people.oregonstate.edu/~adamusp/WESP/ ?Comment:This is an Excel spreadsheet calculator |
Not applicable | Not applicable | Not applicable | https://species.itreetools.org/ | Not applicable | |
Contact Name
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Michael R. Guzy | Sandra Lavorel | Leah Oliver | Richard T. Busing | P. R. Huber | Susan H. Yee | Susan H. Yee | EnviroAtlas Team | Greg Guannel | Arnout van Soesbergen | Annika W. Walters | Paul R. Adamus | Sam Riffell | Walter Hutcheson | Scott C. Brown |
Not reported ?Comment:send comments through any of the means listed on the i-Tree support page: http://www.itreetools.org/support/. |
Javier G. P. Gamarra |
Contact Address
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Oregon State University, Dept. of Biological and Ecological Engineering | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | National Health and Environmental Research Effects Laboratory | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | University of California, Davis, One Shields Ave., Davis, CA 95616, 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 | Not reported | The Nature Conservancy, Coral Gables, FL. USA | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | Dept. of Ecology and Evolutionary Biology, Yale University, New Haven CT 06511 | 6028 NW Burgundy Dr. Corvallis, OR 97330 | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | New York University, United States | Department of Public Health Sciences, University of Miami Miller School of Medicine, 1120 NW 14th Street, Clinical Research Building (CRB), Room 1065, Miami FL 33136 | Not reported | Institute of Biological, Environmental and Rural Sciences, Aberystwyth, SY23 3DB, UK |
Contact Email
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Not reported | sandra.lavorel@ujf-grenoble.fr | leah.oliver@epa.gov | rtbusing@aol.com | prhuber@ucdavis.edu | yee.susan@epa.gov | yee.susan@epa.gov | enviroatlas@epa.gov | greg.guannel@gmail.com | arnout.van_soesbergen@kcl.ac.uk | annika.walters@yale.edu | adamus7@comcast.net | sriffell@cfr.msstate.edu | wwh235@nyu.edu | sbrown@med.miami.edu | info@itreetools.org | jgg@aber.ac.uk |
EM ID
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EM-12 ![]() |
EM-83 | EM-194 |
EM-224 ![]() |
EM-403 ![]() |
EM-457 | EM-462 | EM-491 |
EM-542 ![]() |
EM-618 ![]() |
EM-672 ![]() |
EM-706 | EM-838 | EM-875 | EM-876 | EM-936 | EM-997 |
Summary Description
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**Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The soil carbon ecosystem service map was a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to the soil carbon ecosystem service are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions." | AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range…The simulation of both stand-replacing and partial-stand disturbances across western Oregon improved agreement between simulated and actual data." AUTHOR'S DESCRIPTION: "An analysis of forest successional dynamics was performed on ecoregions 4a and 4b, which cover the south Santiam watershed area selected for intensive study. In each of these two ecoregions, a set of 20 simulated sites was compared to survey plot data summaries. Survey data were analysed by stand age class and simulations of corresponding ages. The statistical methods described…were applied in comparison of actual with simulated forest composition and total basal area by age class. Separate simulations were run with and without fire." | AUTHOR'S DESCRIPTION: "HexSim is a simulation framework within which PVA and other models are constructed. HexSim simulations can range from simple and parsimonious, at one extreme, to complex, data intensive, and biologically realistic at the other. Our tule elk simulations were moderately complex, capturing major life history events such as survival, reproduction and movement, while ignoring other details such as impact of environmental stochasticity or the spread of diseases through the population." "One of the features that distinguishes HexSim from its predecessor is the ability to model group, or herd, movement. This is accomplished through use of a ‘‘proto-disperser’’, an imaginary individual that explores the landscape, finds resources, and then serves as a movement target for the other group members who converge on this target. This feature allows for modeling of both individuals and groups. Another useful feature of HexSim is the barriers component. Multiple types of movement barriers can be included in the model, reflecting likely responses to various kinds of blockages to wildlife. Because many of these barriers tend to be human-related, this feature allows for assessing the potential impacts of multiple types of human infrastructure and landscape features on modeled species. This paper examines several reintroduction scenarios for returning an endemic elk subspecies (tule elk; Cervus elaphus nannodes) to a portion of its native range in California, USA." | 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)…Pendleton (1994) used field observations of dive sites to model potential impacts on local economies due to loss of dive tourism with reef degradation. A key part of the diver choice model is a fitted model of visitation to dive sites described by Visitation to dive sites = 2.897+0.0701creef -0.133D+0.0417τ where creef is percent coral cover, D is the time in hours to the dive site, which we estimate using distance from reef to shore and assuming a boat speed of 5 knots or 2.57ms-1, and τ is a dummy variable for the presence of interesting topographic features. We interpret τ as dramatic changes in bathymetry, quantified as having a standard deviation in depth among grid cells within 30 m that is greater than the75th percentile across all grid cells. Because our interpretation of topography differed from the original usage of “interesting features”, we also calculated dive site visitation assuming no contribution of topography (τ=0). Unsightly coastal development, an additional but non-significant variable in the original model, was assumed to be zero for St. Croix." | 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," | DATA FACT SHEET: "This EnviroAtlas national map estimates the total acres of agricultural crops within each 12-digit hydrologic unit (HUC) that have varying amounts of nearby forested pollinator habitat. The crop types selected from the U.S. Department of Agriculture Cropland Data Layer (CDL) require (or would benefit from) the presence of pollinators, but crops may have no nearby native pollinator habitat. This metric is based on the average flight distance of native bees, both social and solitary, that nest in woodland habitats and forage on native plants and cultivated crops." "The metric was generated using the ESRI ArcMap Neighborhood Distance tool in conjunction with a blended landcover grid, which included the 2006 National Land Cover Dataset (NLCD) and U.S. Department of Agriculture National Agricultural Statistics Service Cropland Data Layer (CDL). Pollinator habitat is defined as trees (fruit, nut, deciduous, and evergreen) for nesting and associated woodland for additional pollen sources. Crops that either require or benefit from pollination were selected and a distance measure of 2.8 kilometers (the average bee species’ foraging distance from the nest4) was used to assess presence or absence of nearby native pollinator habitat. The total area of crops without nearby pollinator habitat was summarized by 12-digit HUC boundaries taken from the NHDPlusV2 Watershed Boundary Dataset (WBD Snapshot)." | 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." | 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: "Diadromous fish are an important link between marine and freshwater food webs. Pacific salmon (Oncorhynchus spp.) strongly impact nutrient dynamics in inland waters and anadromous alewife (Alosa pseudoharengus) may play a similar ecological role along the Atlantic coast. The annual spawning migration of anadromous alewife contributes, on average, 1050 g of nitrogen and 120 g of phosphorus to Bride Brook, Connecticut, USA, through excretion and mortality each year. Natural abundance stable isotope analyses indicate that this influx of marine-derived nitrogen is rapidly incorporated into the stream food web. An enriched d15N signal, indicative of a marine origin, is present at all stream trophic levels with the greatest level of enrichment coincident with the timing of the anadromous alewife spawning migration. There was no significant effect of this nutrient influx on water chemistry, leaf decomposition, or periphyton accrual. Dam removal and fish ladder construction will allow anadromous alewife to regain access to historical freshwater spawning habitats, potentially impacting food web dynamics and nutrient cycling in coastal freshwater systems." AUTHOR'S DESCRIPTION: "Here, we examine the effect of alewife-contributed marine- derived nutrients to coastal stream ecosystems in southern New England. We take a comparative approach examining streams with and without anadromous alewife runs. We use natural abundance stable isotope analyses to assess the incorporation of marine-derived nitrogen and carbon into stream food webs." | Author Description: " The Wetland Ecosystem Services Protocol (WESP) is a standardized template for creating regionalized methods which then can be used to rapid assess ecosystem services (functions and values) of all wetland types throughout a focal region. To date, regionalized versions of WESP have been developed (or are ongoing) for government agencies or NGOs in Oregon, Alaska, Alberta, New Brunswick, and Nova Scotia. WESP also may be used directly in its current condition to assess these services at the scale of an individual wetland, but without providing a regional context for interpreting that information. Nonetheless, WESP takes into account many landscape factors, especially as they relate to the potential or actual benefits of a wetland’s functions. A WESP assessment requires completing a single three-part data form, taking about 1-3 hours. Responses to questions on that form are based on review of aerial imagery and observations during a single site visit; GIS is not required. After data are entered in an Excel spreadsheet, the spreadsheet uses science-based logic models to automatically generate scores intended to reflect a wetland’s ability to support the following functions: Water Storage and Delay, Stream Flow Support, Water Cooling, Sediment Retention and Stabilization, Phosphorus Retention, Nitrate Removal and Retention, Carbon Sequestration, Organic Nutrient Export, Aquatic Invertebrate Habitat, Anadromous Fish Habitat, Non-anadromous Fish Habitat, Amphibian & Reptile Habitat, Waterbird Feeding Habitat, Waterbird Nesting Habitat, Songbird, Raptor and Mammal Habitat, Pollinator Habitat, and Native Plant Habitat. For all but two of these functions, scores are given for both components of an ecosystem service: function and benefit. In addition, wetland Ecological Condition (Integrity), Public Use and Recognition, Wetland Sensitivity, and Stressors are scored. Scores generated by WESP may be used to (a) estimate a wetland’s relative ecological condition, stress, and sensitivity, (b) compare relative levels of ecosystem services among different wetland types, or (c) compare those in a single wetland before and after restoration, enhancement, or loss."] | 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. " | ABSTRACT: " The Hudson River and its estuary is once again an ecologically, economically, and culturally functional component of New York City’s natural environment. The estuary’s cultural significance may derive largely from environmental education, including marine science programs for the public. These programs are understood as ‘‘cultural” ecosystem services but are rarely evaluated in economic terms. We estimated the economic value of the Hudson River Park’s environmental education programs. We compiled data on visits by schools and summer camps from 32 New York City school districts to the Park during the years 2014 and 2015. A ‘‘travel cost” approach was adapted from the field of environmental economics to estimate the value of education in this context. A small—but conservative—estimate of the Park’s annual education program benefits ranged between $7500 and 25,500, implying an average capitalized value on the order of $0.6 million. Importantly, organizations in districts with high proportions of minority students or English language learners were found to be more likely to participate in the Park’s programs. The results provide an optimistic view of the benefits of environmental education focused on urban estuaries, through which a growing understanding of ecological systems could lead to future environmental improvements. " | ABSTRACT: "Introduction: Prior studies suggest that exposure to the natural environment may impact health. The present study examines the association between objective measures of block-level greenness (vegetative presence) and chronic medical conditions, including cardiometabolic conditions, in a large population-based sample of Medicare beneficiaries in Miami-Dade County, Florida. Methods: The sample included 249,405 Medicare beneficiaries aged >=65 years whose location (ZIP+4) within Miami-Dade County, Florida, did not change, from 2010 to 2011. Data were obtained in 2013 and multilevel analyses conducted in 2014 to examine relationships between greenness, measured by mean Normalized Difference Vegetation Index from satellite imagery at the Census block level, and chronic health conditions in 2011, adjusting for neighborhood median household income, individual age, gender, race, and ethnicity. Results: Higher greenness was significantly associated with better health, adjusting for covariates: An increase in mean block-level Normalized Difference Vegetation Index from 1 SD less to 1 SD more than the mean was associated with 49 fewer chronic conditions per 1,000 individuals, which is approximately similar to a reduction in age of the overall study population by 3 years. This same level of increase in mean Normalized Difference Vegetation Index was associated with a reduced risk of diabetes by 14%, hypertension by 13%, and hyperlipidemia by 10%. Planned post-hoc analyses revealed stronger and more consistently positive relationships between greenness and health in lower- than higher-income neighborhoods. Conclusions: Greenness or vegetative presence may be effective in promoting health in older populations, particularly in poor neighborhoods, possibly due to increased time outdoors, physical activity, or stress mitigation." | ABSTRACT: "The Species Selector is a free-standing i-Tree utility that ranks tree species based on their environmental benefits at maturity. As such, it complements existing tree selection programs that rank species based on esthetics or other features. Species are selected based on three types of information. First, hardiness is considered. The hardiness zone is determined based on state and city, and all species that are not sufficiently hardy are eliminated from consideration. Second, mature height is considered. Users are asked to specify minimum and maximum heights, and species outside of that range are eliminated. Finally, eight environmental factors are considered in the rankings created by the Species Selector: • Air pollution removal • Air temperature reduction • Ultraviolet radiation reduction • Carbon storage • Pollen allergenicity • Building energy conservation • Wind reduction • Stream flow reduction (stormwater management). Users are asked to rank the importance of each of these factors on a scale of 0 to 10. The combination of hardiness, mature height, and desired functionality produces a ranked list of appropriate species from an initial database of about 1,600 species. The large species database covers a broad range of native, naturalized and exotic trees, some of which are commonly planted in urban areas. Since only city hardiness zone, tree height and user functional preferences are used to produce the list, there may well be many species on the list that are unsuitable to the local context for a variety of reasons. A species may have particular structural, drainage, sun, pest, or soil pH limitations that should exclude it from use. Furthermore, since many native and exotic species are included, items may appear that are simply not available in the local trade. For these reasons, the list should be considered a beginning rather than an end. The list will need to be whittled down to meet local needs and limitations. Relevant cultural needs should be taken into account as well. The result will be a list of recommended species suited for local use that maximizes environmental services." | Local remediation measures, particularly those undertaken in historical mining areas, can often be ineffective or even deleterious because erosion and sedimentation processes operate at spatial scales beyond those typically used in point-source remediation. Based on realistic simulations of a hybrid landscape evolution model combined with stochastic rainfall generation, we demonstrate that similar remediation strategies may result in differing effects across three contrasting European catchments depending on their topographic and hydrologic regimes. Based on these results, we propose a conceptual model of catchment-scale remediation effectiveness based on three basic catchment characteristics: the degree of contaminant source coupling, the ratio of contaminated to non-contaminated sediment delivery, and the frequency of sediment transport events. |
Specific Policy or Decision Context Cited
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Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None identified | Not applicable | None Identified | As part of an ongoing restoration program, HexSim was used to evaluate a portion of the former range of tule elk to identify the release scenario producing the most elk and fewest human conflicts. | None identified | None identified | None Identified | Future rock lobster fisheries management | None identified | Nutrients and water quality related to anadromous alewife restoration efforts | None identified | None reported | None identified | None identified | None identified | None identified |
Biophysical Context
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No additional description provided | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | nearshore; <1.5 km offshore; <12 m depth | West Cascade lowlands (4a), and west Cascade montane (4b) ecoregions | Located in the Central Valley of California. | No additional description provided | No additional description provided | No additional description provided | barrier reef and fringing reef in nearshore coastal marine system | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | No additional description provided | None | Conservation Reserve Program lands left to go fallow | N/A | No additional description provided | No additional description provided | Rver system catchments associated with mining sites distributed across Europe |
EM Scenario Drivers
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Five scenarios that include urban growth boundaries and various combinations of unconstrainted development, fish conservation, agriculture and forest reserves. ?Comment:Additional alternatives included adding agricultural and forest reserves, and adding or removing urban growth boundaries to the three main scenarios. |
No scenarios presented | Not applicable | Two scenarios modelled, forests with and without fire | Four release sites; Kesterson, Arena Plains, San Luis, and East Bear Creek. | No scenarios presented | No scenarios presented | No scenarios presented | Reef type, Sea level increase, storm conditions, seagrass conditions, coral conditions, vegetation types and conditions | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | No scenarios presented | N/A | N/A | N?A | No scenarios presented | No scenarios presented | No scenarios presented |
EM ID
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EM-12 ![]() |
EM-83 | EM-194 |
EM-224 ![]() |
EM-403 ![]() |
EM-457 | EM-462 | EM-491 |
EM-542 ![]() |
EM-618 ![]() |
EM-672 ![]() |
EM-706 | EM-838 | EM-875 | EM-876 | EM-936 | EM-997 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application | Method + Application | Method + Application | Method Only | Method + Application |
New or Pre-existing EM?
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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 | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-12 ![]() |
EM-83 | EM-194 |
EM-224 ![]() |
EM-403 ![]() |
EM-457 | EM-462 | EM-491 |
EM-542 ![]() |
EM-618 ![]() |
EM-672 ![]() |
EM-706 | EM-838 | EM-875 | EM-876 | EM-936 | EM-997 |
Document ID for related EM
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Doc-47 | Doc-313 | Doc-314 ?Comment:Doc 183 is a secondary source for the Evoland model. |
Doc-260 | None | Doc-22 | Doc-23 |
Doc-327 | Doc-2 | Doc-337 ?Comment:The HexSim User's Guide (Doc 327) was used as a secondary source to clarify variable relationships. |
None | None | None | None | None | Doc-384 | Doc-383 | None | Doc-405 | None | None | Doc-427 | None |
EM ID for related EM
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EM-333 | EM-369 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | None | EM-146 | EM-208 | EM-186 | EM-98 | EM-422 | None | None | None | None | None | EM-667 | EM-661 | EM-718 | EM-831 | EM-841 | EM-842 | EM-843 | EM-844 | EM-845 | EM-846 | EM-847 | None | None | None | EM-998 |
EM Modeling Approach
EM ID
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EM-12 ![]() |
EM-83 | EM-194 |
EM-224 ![]() |
EM-403 ![]() |
EM-457 | EM-462 | EM-491 |
EM-542 ![]() |
EM-618 ![]() |
EM-672 ![]() |
EM-706 | EM-838 | EM-875 | EM-876 | EM-936 | EM-997 |
EM Temporal Extent
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1990-2050 | Not reported | 2006-2007 | >650 yrs | 25 years | 2006-2007, 2010 | 2006-2007, 2010 | 2001-2015 | 2005-2013 | 1950-2071 | 2005-2006 (March-July) | Not applicable | 2008 | 2015 | 2010-2011 | Not applicable | 1800-2100 |
EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | Not applicable | time-dependent |
EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | past time | future time | Not applicable | Not applicable | Not applicable | Not applicable | both | past time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | both |
EM Time Continuity
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discrete | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | discrete | discrete |
other or unclear (comment) ?Comment:Sampling conducted at discrete time periods during Alewife migration. Three sampling periods are presented in this entry. |
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | continuous |
EM Temporal Grain Size Value
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2 | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable | Year | Year | Not applicable | Not applicable | Not applicable | Second | Month | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
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EM-12 ![]() |
EM-83 | EM-194 |
EM-224 ![]() |
EM-403 ![]() |
EM-457 | EM-462 | EM-491 |
EM-542 ![]() |
EM-618 ![]() |
EM-672 ![]() |
EM-706 | EM-838 | EM-875 | EM-876 | EM-936 | EM-997 |
Bounding Type
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Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Physiographic or ecological | Geopolitical | Physiographic or ecological | Physiographic or ecological | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Geopolitical | Not applicable | Physiographic or ecological | Geopolitical | Geopolitical | Not applicable | Watershed/Catchment/HUC |
Spatial Extent Name
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Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Central French Alps | St. Croix, U.S. Virgin Islands | West Cascades, Oregon | Grasslands Ecological Area | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | conterminous United States | Coast of Belize | Santa Basin | New London County, Connecticut, USA | Not applicable | Piedmont Ecoregion | Hudson River Park | Miami-Dade County | Not applicable | Ystwyth, Ampoi, and Naracauli |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 10-100 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | Not applicable | 100,000-1,000,000 km^2 | 10-100 ha | 1000-10,000 km^2. | Not applicable | 100-1000 km^2 |
EM ID
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EM-12 ![]() |
EM-83 | EM-194 |
EM-224 ![]() |
EM-403 ![]() |
EM-457 | EM-462 | EM-491 |
EM-542 ![]() |
EM-618 ![]() |
EM-672 ![]() |
EM-706 | EM-838 | EM-875 | EM-876 | EM-936 | EM-997 |
EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. |
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 distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | Not applicable | spatially distributed (in at least some cases) |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | Not applicable | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | map scale, for cartographic feature |
Spatial Grain Size
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varies | 20 m x 20 m | Not applicable | 0.08 ha | Not reported | 10 m x 10 m | 10 m x 10 m | irregular | 1 meter | 1 km2 | variable stream lengths | not reported | Not applicable | Not applicable | Census block | Not applicable | Not reported |
EM ID
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EM-12 ![]() |
EM-83 | EM-194 |
EM-224 ![]() |
EM-403 ![]() |
EM-457 | EM-462 | EM-491 |
EM-542 ![]() |
EM-618 ![]() |
EM-672 ![]() |
EM-706 | EM-838 | EM-875 | EM-876 | EM-936 | EM-997 |
EM Computational Approach
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Numeric | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Analytic | Analytic | Numeric | Not applicable | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic |
EM Determinism
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stochastic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | Not applicable | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic |
Statistical Estimation of EM
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Comment:Agent based modeling results in response indices. |
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EM ID
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EM-12 ![]() |
EM-83 | EM-194 |
EM-224 ![]() |
EM-403 ![]() |
EM-457 | EM-462 | EM-491 |
EM-542 ![]() |
EM-618 ![]() |
EM-672 ![]() |
EM-706 | EM-838 | EM-875 | EM-876 | EM-936 | EM-997 |
Model Calibration Reported?
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Unclear | No | Yes | No | Unclear | Yes | Yes | No | No | No | Not applicable | Not applicable | Yes | No | Not applicable | Not applicable | Yes |
Model Goodness of Fit Reported?
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No | No | Yes | No | Not applicable | No | No | No | No | No | Not applicable | Not applicable | No | No | No | Not applicable | No |
Goodness of Fit (metric| value | unit)
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None | None |
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None | None | None | None | None | None | None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
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No | No | No | Yes | No | Yes | Yes | No |
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 | No | No | No | No | Not applicable | Yes |
Model Uncertainty Analysis Reported?
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No | No | Yes | No | No | No | No | No | No | No | Not applicable | Not applicable | No | No | No | Not applicable | Unclear |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No ?Comment:Sensitivity analysis performed for agent values only. |
No | No | No | No | No | No | No | No | No | Not applicable | Not applicable | Yes | No | No | Not applicable | Unclear |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-12 ![]() |
EM-83 | EM-194 |
EM-224 ![]() |
EM-403 ![]() |
EM-457 | EM-462 | EM-491 |
EM-542 ![]() |
EM-618 ![]() |
EM-672 ![]() |
EM-706 | EM-838 | EM-875 | EM-876 | EM-936 | EM-997 |
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None |
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None | None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-12 ![]() |
EM-83 | EM-194 |
EM-224 ![]() |
EM-403 ![]() |
EM-457 | EM-462 | EM-491 |
EM-542 ![]() |
EM-618 ![]() |
EM-672 ![]() |
EM-706 | EM-838 | EM-875 | EM-876 | EM-936 | EM-997 |
None | None |
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None | None |
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None |
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None |
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None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-12 ![]() |
EM-83 | EM-194 |
EM-224 ![]() |
EM-403 ![]() |
EM-457 | EM-462 | EM-491 |
EM-542 ![]() |
EM-618 ![]() |
EM-672 ![]() |
EM-706 | EM-838 | EM-875 | EM-876 | EM-936 | EM-997 |
Centroid Latitude
em.detail.ddLatHelp
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44.11 | 45.05 | 17.75 | 44.24 | 37.25 | 17.73 | 17.73 | 39.5 | 18.63 | -9.05 | 41.78 | Not applicable | 36.23 | 40.73 | 25.64 | Not applicable |
52.5 ?Comment:There are 3 locations provided in this study with latitudes of 52.5, 46, and 40 as well as longitudes of -4, 10, and 25, respectively. |
Centroid Longitude
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-123.09 | 6.4 | -64.75 | -122.24 | -120.8 | -64.77 | -64.77 | -98.35 | -88.22 | -77.81 | -72.17 | Not applicable | -81.9 | -74.01 | -80.5 | Not applicable | -4 |
Centroid Datum
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WGS84 | WGS84 | NAD83 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | Not applicable | None provided |
Centroid Coordinates Status
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Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated | Estimated | Not applicable | Estimated |
EM ID
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EM-12 ![]() |
EM-83 | EM-194 |
EM-224 ![]() |
EM-403 ![]() |
EM-457 | EM-462 | EM-491 |
EM-542 ![]() |
EM-618 ![]() |
EM-672 ![]() |
EM-706 | EM-838 | EM-875 | EM-876 | EM-936 | EM-997 |
EM Environmental Sub-Class
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Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Forests | Inland Wetlands | Forests | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Grasslands | Created Greenspace | Created Greenspace | Created Greenspace | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Agricultural-urban interface at river junction | Subalpine terraces, grasslands, and meadows. | stony coral reef | Primarily conifer forest | Terrestrial mosaic | Coral reefs | Coral reefs | Terrestrial | coral reefs | tropical, coastal to montane | Coastal streams | Wetlands | grasslands | Park | urban neighborhood greenspace | Urban greenspace | Watershed catchment |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is finer 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 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 |
Other or unclear (comment) ?Comment:Variable data was derived from multiple climate data stations distrubuted across the study area. The location and distribution of the data stations was not provided. |
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 coarser than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-12 ![]() |
EM-83 | EM-194 |
EM-224 ![]() |
EM-403 ![]() |
EM-457 | EM-462 | EM-491 |
EM-542 ![]() |
EM-618 ![]() |
EM-672 ![]() |
EM-706 | EM-838 | EM-875 | EM-876 | EM-936 | EM-997 |
EM Organismal Scale
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Not applicable | Community | Guild or Assemblage | Species | Individual or population, within a species | Not applicable | Guild or Assemblage | Guild or Assemblage | Guild or Assemblage | Not applicable | Individual or population, within a species | Not applicable | Species | Not applicable | Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-12 ![]() |
EM-83 | EM-194 |
EM-224 ![]() |
EM-403 ![]() |
EM-457 | EM-462 | EM-491 |
EM-542 ![]() |
EM-618 ![]() |
EM-672 ![]() |
EM-706 | EM-838 | EM-875 | EM-876 | EM-936 | EM-997 |
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None Available |
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None Available |
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None Available | None Available |
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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-12 ![]() |
EM-83 | EM-194 |
EM-224 ![]() |
EM-403 ![]() |
EM-457 | EM-462 | EM-491 |
EM-542 ![]() |
EM-618 ![]() |
EM-672 ![]() |
EM-706 | EM-838 | EM-875 | EM-876 | EM-936 | EM-997 |
|
|
|
|
|
|
|
|
|
|
None |
|
|
|
|
|
|
<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-12 ![]() |
EM-83 | EM-194 |
EM-224 ![]() |
EM-403 ![]() |
EM-457 | EM-462 | EM-491 |
EM-542 ![]() |
EM-618 ![]() |
EM-672 ![]() |
EM-706 | EM-838 | EM-875 | EM-876 | EM-936 | EM-997 |
|
None |
|
None | None |
|
|
|
|
|
None |
|
|
|
|
None |
|