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-69 | EM-86 |
EM-111 ![]() |
EM-143 | EM-185 | EM-196 |
EM-333 ![]() |
EM-414 | EM-432 | EM-438 | EM-453 | EM-454 | EM-457 |
EM-467 ![]() |
EM-469 | EM-604 |
EM-618 ![]() |
EM-627 |
EM-718 ![]() |
EM-845 | EM-850 | EM-855 | EM-878 | EM-904 |
EM Short Name
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Soil carbon content, Central French Alps | Area and hotspots of soil retention, South Africa | InVEST water yield, Hood Canal, WA, USA | InVEST habitat quality | Blue crabs and SAV, Chesapeake Bay, USA | N removal by wetlands, Contiguous USA | Evoland v3.5 (unbounded growth), Eugene, OR, USA | SAV occurrence, St. Louis River, MN/WI, USA | Nitrogen fixation rates, Guánica Bay, Puerto Rico | InVESTv3.0 Nutrient retention, Guánica Bay | Reef density of E. striatus, St. Croix, USVI | Reef snorkeling opportunity, St. Croix, USVI | Visitation to reef dive sites, St. Croix, USVI | Yasso07 v1.0.1, Switzerland | Yasso07 - SOC, Loess Plateau, China | Chinook salmon value (household), Yaquina Bay, OR | WaterWorld v2, Santa Basin, Peru | N removal by wetland restoration, Midwest, USA | WESP: Riparian & stream habitat, ID, USA | Red-winged blackbird abun, Piedmont region, USA | Invertebrate community index, Alabama | ARIES: Crop pollination in Rwanda and Burundi | Health, safety and greening urban space, PA, USA | Drag coefficient Laminaria hyperborea |
EM Full Name
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Soil carbon content, Central French Alps | Area and hotspots of soil retention, South Africa | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) water yield, Hood Canal, WA, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs) Habitat Quality | Blue crabs and submerged aquatic vegetation interaction, Chesapeake Bay, USA | Nitrogen removal by wetlands as a function of loading, Contiguous USA | Evoland v3.5 (without urban growth boundaries), Eugene, OR, USA | Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA | Nitrogen fixation rates, Guánica Bay, Puerto Rico, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs)v3.0 Nutrient retention, Guánica Bay, Puerto Rico, USA | Relative density of Epinephelus striatus (on reef), St. Croix, USVI | Relative snorkeling opportunity (in reef), St. Croix, USVI | Visitation to dive sites (reef), St. Croix, USVI | Yasso07 v1.0.1 forest litter decomposition, Switzerland | Yasso07 - Land Use Effects on Soil Organic Carbon Stocks in the Loess Plateau, China | Economic value of Chinook salmon per household method, Yaquina Bay, OR | WaterWorld v2, Santa Basin, Peru | Nitrate removal by potential wetland restoration, Mississippi River subbasins, USA | WESP: Riparian and stream habitat focus projects, ID, USA | Red-winged blackbird abundance, Piedmont ecoregion, USA | Invertebrate community index, Choctawhatchee-Pea Rivers watershed, Alabama | ARIES; Crop pollination in Rwanda and Burundi | Health, safety and greening urban vacant space, Pennsylvania, USA | Drag coefficient Laminaria hyperborea |
EM Source or Collection
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EU Biodiversity Action 5 | None | InVEST |
InVEST ?Comment:From the Natural Capital Project website |
None | US EPA | Envision | US EPA | US EPA | US EPA | InVEST | US EPA | US EPA | US EPA | None | None | US EPA | None | None | None | None | None | ARIES | None | None |
EM Source Document ID
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260 | 271 | 205 | 278 |
292 ?Comment:Conference paper |
63 |
47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
330 |
338 ?Comment:WE received a draft copy prior to journal publication that was agency reviewed. |
338 | 335 | 335 | 335 | 343 | 344 | 324 | 368 |
370 ?Comment:Final project report to U.S. Department of Agriculture; Project number: IOW06682. December 2006. |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
405 | 409 | 411 | 419 | 424 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Toft, J. E., Burke, J. L., Carey, M. P., Kim, C. K., Marsik, M., Sutherland, D. A., Arkema, K. K., Guerry, A. D., Levin, P. S., Minello, T. J., Plummer, M., Ruckelshaus, M. H., and Townsend, H. M. | Natural Capital Project | Mykoniatis, N. and Ready, R. | Jordan, S., Stoffer, J. and Nestlerode, J. | Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Didion, M., B. Frey, N. Rogiers, and E. Thurig | Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Van Soesbergen, A. and M. Mulligan | Crumpton, W. G., G. A. Stenback, B. A. Miller, and M. J. Helmers | Murphy, C. and T. Weekley | Riffel, S., Scognamillo, D., and L. W. Burger | Bennett, H.H., Mullen, M.W., Stewart, P.M., Sawyer, J.A., and E. C. Webber | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. | Branas, C. C., R. A. Cheney, J. M. MacDonald, V. W. Tam, T. D. Jackson, and T. R. Ten Havey | Mendez, F. J. and I. J. Losada |
Document Year
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2011 | 2008 | 2013 | 2014 | 2013 | 2011 | 2008 | 2013 | 2017 | 2017 | 2014 | 2014 | 2014 | 2014 | 2015 | 2012 | 2018 | 2006 | 2012 | 2008 | 2004 | 2018 | 2011 | 2004 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Mapping ecosystem services for planning and management | From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA | Habitat Quality model - InVEST ver. 3.0 | Evaluating habitat-fishery interactions: The case of submerged aquatic vegetation and blue crab fishery in the Chesapeake Bay | Wetlands as sinks for reactive nitrogen at continental and global scales: A meta-analysis | Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Validating tree litter decomposition in the Yasso07 carbon model | Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | Potential benefits of wetland filters for tile drainage systems: Impact on nitrate loads to Mississippi River subbasins | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Development of an invertebrate community index for an Alabama coastal plain watershed | Towards globally customizable ecosystem service models | A difference-in-differences analysis of health, safety, and greening vacant urban space | An empirical model to estimate the propagation of random breaking and nonbreaking waves over vegetation fields |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Not formally documented | 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 | Neither peer reviewed nor published (explain in Comment) | 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 on Natural Capital Project website | Conference proceedings | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-69 | EM-86 |
EM-111 ![]() |
EM-143 | EM-185 | EM-196 |
EM-333 ![]() |
EM-414 | EM-432 | EM-438 | EM-453 | EM-454 | EM-457 |
EM-467 ![]() |
EM-469 | EM-604 |
EM-618 ![]() |
EM-627 |
EM-718 ![]() |
EM-845 | EM-850 | EM-855 | EM-878 | EM-904 |
Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | http://evoland.bioe.orst.edu/ | Not applicable | Not applicable | http://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | Not applicable | www.policysupport.org/waterworld | Not applicable | Not applicable | Not applicable | Not applicable | https://github.com/integratedmodelling/im.aries.global | Not applicable | Not applicable | |
Contact Name
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Sandra Lavorel | Benis Egoh | J.E. Toft | The Natural Capital Project | Nikolaos Mykoniatis | Steve Jordan | Michael R. Guzy | Ted R. Angradi | Susan H. Yee | Susan H. Yee | Susan H. Yee | Susan H. Yee | Susan H. Yee |
Markus Didion ?Comment:Tel.: +41 44 7392 427 |
Xing Wu | Stephen Jordan | Arnout van Soesbergen | William G. Crumpton | Chris Murphy | Sam Riffell | E. Cliff Webber | Javier Martinez | Charles C. Branas | F. J. Mendez |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | The Natural Capital Project, Stanford University, 371 Serra Mall, Stanford, CA 94305-5020, USA | 371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | Department of Agricultural Economics, Sociology and Education The Pennsylvania State University | Gulf Ecology Division U.S. Environmental Protection Agency, 1 Sabine Island Drive, Gulf Breeze, Florida 32561 | Oregon State University, Dept. of Biological and Ecological Engineering | U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Chinese Academy of Sciences, Beijing 100085, China | U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | Dept. of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA 50011 | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Troy State University, 4004 Clairmont Avenue South, Birmingham, Alabama 35222 progress. | BC3-Basque Centre for Climate Chan ge, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain | Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Blockley Hall, Room 936, 423 Guardian Drive, Philadelphia, PA 19104-6021 | Not reported |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | Not reported | jetoft@stanford.edu | invest@naturalcapitalproject.org | Not reported | steve.jordan@epa.gov | Not reported | angradi.theodore@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | markus.didion@wsl.ch | xingwu@rceesac.cn | jordan.steve@epa.gov | arnout.van_soesbergen@kcl.ac.uk | crumpton@iastate.edu | chris.murphy@idfg.idaho.gov | sriffell@cfr.msstate.edu | hbennett1978@hotmail.com | javier.martinez@bc3research.org | cbranas@upenn.edu | mendezf@unican.es |
EM ID
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EM-69 | EM-86 |
EM-111 ![]() |
EM-143 | EM-185 | EM-196 |
EM-333 ![]() |
EM-414 | EM-432 | EM-438 | EM-453 | EM-454 | EM-457 |
EM-467 ![]() |
EM-469 | EM-604 |
EM-618 ![]() |
EM-627 |
EM-718 ![]() |
EM-845 | EM-850 | EM-855 | EM-878 | EM-904 |
Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in soil carbon was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy…Soil carbon for each pixel was calculated and mapped using model estimates...This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on soil carbon. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use." | AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil retention was modelled as a function of vegetation or litter cover and soil erosion potential. Schoeman et al. (2002) modelled soil erosion potential and derived eight erosion classes, ranging from low to severe erosion potential for South Africa. The vegetation cover was mapped by ranking vegetation types using expert knowledge of their ability to curb erosion. We used Schulze (2004) index of litter cover which estimates the soil surface covered by litter based on observations in a range of grasslands, woodlands and natural forests. According to Quinton et al. (1997) and Fowler and Rockstrom (2001) soil erosion is slightly reduced with about 30%, significantly reduced with about 70% vegetation cover. The range of soil retention was mapped by selecting all areas that had vegetation or litter cover of more than 30% for both the expert classified vegetation types and litter accumulation index within areas with moderate to severe erosion potential. The hotspot was mapped as areas with severe erosion potential and vegetation/litter cover of at least 70% where maintaining the cover is essential to prevent erosion. An assumption was made that the potential for this service is relatively low in areas with little natural vegetation or litter cover." | InVEST Water Yield and Scarcity Model Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "We modelled discharge and total nitrogen for the 153 perennial sub- watersheds in Hood Canal based on spatial variation in hydrological factors, land and water use, and vegetation.To do this, we reparame-terized a set of fresh water models available in the InVEST tool (Tallis and Polasky, 2009; Kareiva et al., 2011)… We modelled discharge using the InVESTWater Yield and Scarcity model. The model estimates discharge for user-defined subwatersheds based on the average annual precipitation, annual reference evapotranspiration, and a correction factor for vegetation type, soil depth, plant available water content, land use and land cover, root depth, elevation, saturated hydraulic conductivity, and consumptive water use" (2) | Please note: This ESML entry describes an InVEST model version that was current as of 2014. More recent versions may be available at the InVEST website. AUTHORS DESCRIPTION: "The InVEST habitat quality model combines information on LULC and threats to biodiversity to produce habitat quality maps. This approach generates two key sets of information that are useful in making an initial assessment of conservation needs: the relative extent and degradation of different types of habitat types in a region and changes across time. This approach further allows rapid assessment of the status of and change in a proxy for more detailed measures of biodiversity status. If habitat changes are taken as representative of genetic, species, or ecosystem changes, the user is assuming that areas with high quality habitat will better support all levels of biodiversity and that decreases in habitat extent and quality over time means a decline in biodiversity persistence, resilience, breadth and depth in the area of decline. The habitat rarity model indicates the extent and pattern of natural land cover types on the current or a potential future landscape vis-a-vis the extent of the same natural land cover types in some baseline period. Rarity maps allow users to create a map of the rarest habitats on the landscape relative to the baseline chosen by the user to represent the mix of habitats on the landscape that is most appropriate for the study area’s native biodiversity. The model requires basic data that are available virtually everywhere in the world, making it useful in areas for which species distribution data are poor or lacking altogether. Extensive occurrence (presence/absence) data may be available in many places for current conditions. However, modeling the change in occurrence, persistence, or vulnerability of multiple species under future conditions is often impossible or infeasible. While a habitat approach leaves out the detailed species occurrence data available for current conditions, several of its components represent advances in functionality over many existing biodiversity conservation planning tools. The most significant is the ability to characterize the sensitivity of habitats types to various threats. Not all habitats are affected by all threats in the same way, and the InVEST model accounts for this variability. Further, the model allows users to estimate the relative impact of one threat over another so that threats that are more damaging to biodiversity persistence on the landscape can be represented as such. For example, grassland could be particularly sensitive to threats generated by urban areas yet moderately sensitive to threats generated by roads. In addition, the distance over which a threat will degrade natural systems can be incorporated into the model. Model assessment of the current landscape can be used as an input to a coarse-filter assessment of current conservation needs and opportunities. Model assessment of pote | ABSTRACT: "This paper investigates habitat-fisheries interaction between two important resources in the Chesapeake Bay: blue crabs and Submerged Aquatic Vegetation (SAV). A habitat can be essential to a species (the species is driven to extinction without it), facultative (more habitat means more of the species, but species can exist at some level without any of the habitat) or irrelevant (more habitat is not associated with more of the species). An empirical bioeconomic model that nests the essential-habitat model into its facultative-habitat counterpart is estimated. Two alternative approaches are used to test whether SAV matters for the crab stock. Our results indicate that, if we do not have perfect information on habitat-fisheries linkages, the right approach would be to run the more general facultative-habitat model instead of the essential- habitat one." | ABSTRACT: "We compiled published data from wetland studies worldwide to estimate total Nr removal and to evaluate factors that influence removal rates. Over several orders of magnitude in wetland area and Nr loading rates, there is a positive, near-linear relationship between Nr removal and Nr loading. The linear model (null hypothesis) explains the data better than either a model of declining Nr removal efficiency with increasing Nr loading, or a Michaelis–Menten (saturation) model." | **Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake." | AUTHOR'S DESCRIPTION: " …In Guánica Bay watershed, Puerto Rico, deforestation and drainage of a large lagoon have led to sediment, contaminant, and nutrient transport into the bay, resulting in declining quality of coral reefs. A watershed management plan is currently being implemented to restore reefs through a variety of proposed actions…After the workshops, fifteen indicators of terrestrial ecosystem services in the watershed and four indicators in the coastal zone were identified to reflect the wide range of stakeholder concerns that could be impacted by management decisions. Ecosystem service production functions were applied to quantify and map ecosystem services supply in the Guánica Bay watershed, as well as an additional highly engineered upper multi-watershed area connected to the lower watershed via a series of reservoirs and tunnels,…” AUTHOR''S DESCRIPTION: "The U.S. Coral Reef Task Force (CRTF), a collaboration of federal, state and territorial agencies, initiated a program in 2009 to better incorporate land-based sources of pollution and socio-economic considerations into watershed strategies for coral reef protection (Bradley et al., 2016)...Baseline measures for relevant ecosystem services were calculated by parameterizing existing methods, largely based on land cover (Egoh et al., 2012; Martinez- Harms and Balvanera, 2012), with relevant rates of ecosystem services production for Puerto Rico, and applying them to map ecosystem services supply for the Guánica Bay Watershed...The Guánica Bay watershed is a highly engineered watershed in southwestern Puerto Rico, with a series of five reservoirs and extensive tunnel systems artificially connecting multiple mountainous sub-watersheds to the lower watershed of the Rio Loco, which itself is altered by an irrigation canal and return drainage ditch that diverts water through the Lajas Valley (PRWRA, 1948)...For each objective, a translator of ecosystem services production, i.e., ecological production function, was used to quantify baseline measurements of ecosystem services supply from land use/land cover (LULC) maps for watersheds across Puerto Rico...Two additional metrics, nitrogen fixation and rates of carbon sequestration into soil and sediment, were also calculated as potential measures of soil quality and agricultural productivity. Carbon sequestration and nitrogen fixation rates were assigned to each land cover class" | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "Nutrient retention was estimated by first calculating water yield and establishing the quantity of nitrogen or phosphorus retained by different land cover classes using a water purification model (InVEST 3.0.0; Tallis et al., 2013). Different land cover classes were assumed to have different capacities for retaining nutrients, depending on the efficiency of vegetation in removing either nitrogen or phosphorus and the rates of nitrogen or phosphorus loading." “Use of other models in conjunction with this model:Average runoff per pixel modeled here were derived from the InVEST Water Yield model" | 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)…Synthesis of scientific literature and expert opinion can be used to estimate the relative potential for recreational opportunities across different benthic habitat types (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to recreational opportunities as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative recreational opportunity 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: density of E. striatus" | 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)…Synthesis of scientific literature and expert opinion can be used to estimate the relative potential for recreational opportunities across different benthic habitat types (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to recreational opportunities as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative recreational opportunity 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: snorkeling opportunity" | 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 examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to (i) examine the effect of the application of three different published Yasso07 parameter sets on simulated decay rate; (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests;…" AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root litter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The result of the simulation was an annual estimate of the remaining fraction of the initial mass, which could then be compared with observed data." | ABSTRACT: "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:"Critical habitats for fish and wildlife are often small patches in landscapes, e.g., aquatic vegetation beds, reefs, isolated ponds and wetlands, remnant old-growth forests, etc., yet the same animal populations that depend on these patches for reproduction or survival can be extensive, ranging over large regions, even continents or major ocean basins. Whereas the ecological production functions that support these populations can be measured only at fine geographic scales and over brief periods of time, the ecosystem services (benefits that ecosystems convey to humans by supporting food production, water and air purification, recreational, esthetic, and cultural amenities, etc.) are delivered over extensive scales of space and time. These scale mismatches are particularly important for quantifying the economic values of ecosystem services. Examples can be seen in fish, shellfish, game, and bird populations. Moreover, there can be wide-scale mismatches in management regimes, e.g., coastal fisheries management versus habitat management in the coastal zone. We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for recreational Chinook (Oncorhynchus tshawytscha) salmon fisheries in the U.S. Pacific Northwest (Washington and Oregon) and commercial blue crab (Callinectes sapidus) and penaeid shrimp fisheries in the Gulf of Mexico. | 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: "The primary objective of this project was to estimate the nitrate reduction that could be achieved using restored wetlands as nitrogen sinks in tile-drained regions of the upper Mississippi River (UMR) and Ohio River basins. This report provides an assessment of nitrate concentrations and loads across the UMR and Ohio River basins and the mass reduction of nitrate loading that could be achieved using wetlands to intercept nonpoint source nitrate loads. Nitrate concentration and stream discharge data were used to calculate stream nitrate loading and annual flow-weighted average (FWA) nitrate concentrations and to develop a model of FWA nitrate concentration based on land use. Land use accounts for 90% of the variation among stations in long term FWA nitrate concentrations and was used to estimate FWA nitrate concentrations for a 100 ha grid across the UMR and Ohio River basins. Annual water yield for grid cells was estimated by interpolating over selected USGS monitoring station water yields across the UMR and Ohio River basins. For 1990 to 1999, mass nitrate export from each grid area was estimated as the product of the FWA nitrate concentration, water yield and grid area. To estimate potential nitrate removal by wetlands across the same grid area, mass balance simulations were used to estimate percent nitrate reduction for hypothetical wetland sites distributed across the UMR and Ohio River basins. Nitrate reduction was estimated using a temperature dependent, area-based, first order model. Model inputs included local temperature from the National Climatic Data Center and water yield estimated from USGS stream flow data. Results were used to develop a nonlinear model for percent nitrate removal as a function of hydraulic loading rate (HLR) and temperature. Mass nitrate removal for potential wetland restorations distributed across the UMR and Ohio River basin was estimated based on the expected mass load and the predicted percent removal. Similar functions explained most of the variability in per cent and mass removal reported for field scale experimental wetlands in the UMR and Ohio River basins. Results suggest that a 30% reduction in nitrate load from the UMR and Ohio River basins could be achieved using 210,000-450,000 ha of wetlands targeted on the highest nitrate contributing areas." AUTHOR'S DESCRIPTION: "Percent nitrate removal was estimated based on HLR functions (Figure 19) spanning a 3 fold range in loss rate coefficient (Crumpton 2001) and encompassing the observed performance reported for wetlands in the UMR and Ohio River basins (Table 2, Figure 7). The nitrate load was multiplied by the expected percent nitrate removal to estimate the mass removal. This procedure was repeated for each restoration scenario each year in the simulation period (1990 to 1999)… for a scenario with a wetland/watershed area ratio of 2%. These results are based on the assumption that the FWA nitrate concentration versus percent row crop r | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | ABSTRACT:"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: "Macroinvertebrates were collected from 49 randomly selected sites from first through sixth-order streams in the Choctawhatchee-Pea Rivers watershed and were identified to genus level. Thirty-eight candidate metrics were examined, and an invertebrate community index (ICI) was calibrated by eliminating metrics that failed to separate impaired from unimpaired streams. Each site was scored with those metrics, and narrative scores were assigned based on ICI scores. Least impacted sites scored significantly lower than sites impacted by row crop agriculture, cattle, and urban land uses. Conditions in the watershed suggest that the entire area has experienced degradation through past and current land use practices. An initial validation of the index was performed and is described. Additional evaluations of the index are in progress." | [Abstract:Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The Artificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multicriteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed.] | ABSTRACT: "Greening of vacant urban land may affect health and safety. The authors conducted a decade-long difference-indifferences analysis of the impact of a vacant lot greening program in Philadelphia, Pennsylvania, on health and safety outcomes. ‘‘Before’’ and ‘‘after’’ outcome differences among treated vacant lots were compared with matched groups of control vacant lots that were eligible but did not receive treatment. Control lots from 2 eligibility pools were randomly selected and matched to treated lots at a 3:1 ratio by city section. Random-effects regression models were fitted, along with alternative models and robustness checks. Across 4 sections of Philadelphia, 4,436 vacant lots totaling over 7.8 million square feet (about 725,000 m^2) were greened from 1999 to 2008. Regression adjusted estimates showed that vacant lot greening was associated with consistent reductions in gun assaults across all 4 sections of the city (P < 0.001) and consistent reductions in vandalism in 1 section of the city (P < 0.001). Regression-adjusted estimates also showed that vacant lot greening was associated with residents’ reporting less stress and more exercise in select sections of the city (P < 0.01). Once greened, vacant lots may reduce certain crimes and promote some aspects of health. Limitations of the current study are discussed. Community-based trials are warranted to further test these findings." REVIEWER'S COMMENTS: Regression models were fitted separately for point-based, tract-based, and block group-based outcomes, and for the four sections of Philadelphia separately and combined. This entry presents just the point-based outcomes for the whole of Philadelphia. | ABSTRACT: "In this work, a model for wave transformation on vegetation fields is presented. The formulation includes wave damping and wave breaking over vegetation fields at variable depths. Based on a nonlinear formulation of the drag force, either the transformation of monochromatic waves or irregular waves can be modelled considering geometric and physical characteristics of the vegetation field. The model depends on a single parameter similar to the drag coefficient, which is parameterized as a function of the local Keulegan–Carpenter number for a specific type of plant. Given this parameterization, determined with laboratory experiments for each plant type, the model is able to reproduce the root-mean-square wave height transformation observed in experimental data with reasonable accuracy." AUTHOR'S DESCRIPTION: "Therefore, a relation between C˜D and some nondimensional flow parameters is desirable to characterize hydrodynamically the L. hyperborea model plants for predictable purposes." |
Specific Policy or Decision Context Cited
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None identified | None identified | Land use change | None identified | Not applicable | None identified | Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None identified | None provided | Improving water quality | None identified | None identified | None identified | None identified | None | None identified | None identified | None identified | None identified | None reported | None reported | None identified | None identified | None identified |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | Not additional description provided | Not applicable | Submerged Aquatic Vegetation (SAV), eelgrass | Estuarine Emergent; Agricultural; Salt Marsh; Palustrine Emergent; Palustrine Forested | No additional description provided | submerged aquatic vegetation | No additional description provided | No additional description provided | No additional description provided | No additional description provided | No additional description provided | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | Agricultural plain, hills, gulleys, forest, grassland, Central China | Yaquina Bay estuary | 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 | restored, enhanced and created wetlands | Conservation Reserve Program lands left to go fallow | 1st through 6th order streams on low elevation coastal plains | Entire countries of Rwanda and Burundi considered | No additional description provided | No additional description provided |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | Future land use and land cover; climate change |
Potential land Use Land Class (LULC) future and baseline ?Comment:model requires current landuse but can compare to baseline (prior to intensive management of the land) and potential future landuse. These are the two scenarios suggested in the documentation. |
Essential or Facultative habitat | No scenarios presented | Three scenarios without urban growth boundaries, and with various combinations of unconstrainted development, fish conservation, and agriculture and forest reserves. | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented |
No scenarios presented ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
Land use change | No scenarios presented | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | More conservative, average and less conservative nitrate loss rate | Sites, function or habitat focus | N/A | N/A | N/A | No scenarios presented | No scenarios presented |
EM ID
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EM-69 | EM-86 |
EM-111 ![]() |
EM-143 | EM-185 | EM-196 |
EM-333 ![]() |
EM-414 | EM-432 | EM-438 | EM-453 | EM-454 | EM-457 |
EM-467 ![]() |
EM-469 | EM-604 |
EM-618 ![]() |
EM-627 |
EM-718 ![]() |
EM-845 | EM-850 | EM-855 | EM-878 | EM-904 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
Method + Application | 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 | Method + Application | Method + Application | Method + Application | Method + Application |
New or Pre-existing EM?
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New or revised model | New or revised model | Application of existing model | New or revised model | 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 | Application of existing model | Application of existing model | Application of existing model | New or revised model | Application of existing 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 |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
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EM-69 | EM-86 |
EM-111 ![]() |
EM-143 | EM-185 | EM-196 |
EM-333 ![]() |
EM-414 | EM-432 | EM-438 | EM-453 | EM-454 | EM-457 |
EM-467 ![]() |
EM-469 | EM-604 |
EM-618 ![]() |
EM-627 |
EM-718 ![]() |
EM-845 | EM-850 | EM-855 | EM-878 | EM-904 |
EM Temporal Extent
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2007-2009 | Not reported | 2005-7; 2035-45 | Not applicable | 1993-2011 | 2004 | 1990-2050 | 2010 - 2012 | 1978 - 2009 | 1980 - 2013 | 2006-2007, 2010 | 2006-2007, 2010 | 2006-2007, 2010 | 1993-2013 | 1969-2011 | 2003-2008 | 1950-2071 | 1973-1999 | 2010-2011 | 2008 | 2002 | 2010 | 1998-2008 | Not applicable |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | Not applicable |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | future time | Not applicable | Not applicable | other or unclear (comment) | Not applicable | Not applicable | Not applicable | future time | past time | Not applicable | both | future time | past time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | 2 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Year | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Year | Year | Not applicable | Month | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-69 | EM-86 |
EM-111 ![]() |
EM-143 | EM-185 | EM-196 |
EM-333 ![]() |
EM-414 | EM-432 | EM-438 | EM-453 | EM-454 | EM-457 |
EM-467 ![]() |
EM-469 | EM-604 |
EM-618 ![]() |
EM-627 |
EM-718 ![]() |
EM-845 | EM-850 | EM-855 | EM-878 | EM-904 |
Bounding Type
em.detail.boundingTypeHelp
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Physiographic or Ecological | Geopolitical | Watershed/Catchment/HUC | No location (no locational reference given) | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Geopolitical | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Geopolitical | Watershed/Catchment/HUC | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Watershed/Catchment/HUC | Geopolitical | Geopolitical | Not applicable |
Spatial Extent Name
em.detail.extentNameHelp
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Central French Alps | South Africa | Hood Canal | Not applicable | Chesapeake Bay | Contiguous U.S. | Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | St. Louis River Estuary | Guanica Bay watershed | Guanica Bay Study Area | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Switzerland | Yangjuangou catchment | Pacific Northwest | Santa Basin | Upper Mississippi River and Ohio River basins | Wetlands in idaho | Piedmont Ecoregion | Choctawhatchee-Pea rivers watershed | Rwanda and Burndi | Philadelphia | Not applicable |
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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10-100 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | Not applicable | 10,000-100,000 km^2 | 100,000-1,000,000 km^2 | 10-100 km^2 | 10-100 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | 1-10 km^2 | >1,000,000 km^2 | 10,000-100,000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | 10,000-100,000 km^2 | 100-1000 km^2 | Not applicable |
EM ID
em.detail.idHelp
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EM-69 | EM-86 |
EM-111 ![]() |
EM-143 | EM-185 | EM-196 |
EM-333 ![]() |
EM-414 | EM-432 | EM-438 | EM-453 | EM-454 | EM-457 |
EM-467 ![]() |
EM-469 | EM-604 |
EM-618 ![]() |
EM-627 |
EM-718 ![]() |
EM-845 | EM-850 | EM-855 | EM-878 | EM-904 |
EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:BH: Each individual transect?s data was parceled into location reports, and that each report?s ?quadrat? area was dependent upon the angle of the hydroacoustic sampling beam. The spatial grain is 0.07 m^2, 0.20 m^2 and 0.70 m^2 for depths of 1 meter, 2 meters and 3 meters, respectively. |
spatially distributed (in at least some cases) | 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 distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Point-based measures are continuous and boundary-free, assign each lot to its own unique neighborhood, and avoid aggregation effects while directly accounting for spillover and the variability of neighboring areas. |
spatially lumped (in all cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | 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) | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable | Not applicable | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable |
Spatial Grain Size
em.detail.spGrainSizeHelp
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20 m x 20 m | Distributed across catchments with average size of 65,000 ha | 30 m x 30 m | LULC pixel size | Not applicable | Not applicable | varies | 0.07 m^2 to 0.70 m^2 | HUC | 30 m x 30 m | 10 m x 10 m | 10 m x 10 m | 10 m x 10 m | 5 sites | 30m x 30m | Not applicable | 1 km2 | 1 km2 | Not applicable | Not applicable | Not applicable | 1km | Point based | Not applicable |
EM ID
em.detail.idHelp
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EM-69 | EM-86 |
EM-111 ![]() |
EM-143 | EM-185 | EM-196 |
EM-333 ![]() |
EM-414 | EM-432 | EM-438 | EM-453 | EM-454 | EM-457 |
EM-467 ![]() |
EM-469 | EM-604 |
EM-618 ![]() |
EM-627 |
EM-718 ![]() |
EM-845 | EM-850 | EM-855 | EM-878 | EM-904 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Numeric | Numeric | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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EM ID
em.detail.idHelp
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EM-69 | EM-86 |
EM-111 ![]() |
EM-143 | EM-185 | EM-196 |
EM-333 ![]() |
EM-414 | EM-432 | EM-438 | EM-453 | EM-454 | EM-457 |
EM-467 ![]() |
EM-469 | EM-604 |
EM-618 ![]() |
EM-627 |
EM-718 ![]() |
EM-845 | EM-850 | EM-855 | EM-878 | EM-904 |
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | Yes | Not applicable | Yes | Yes | Unclear | Yes | No | No | Yes | Yes | Yes | No | Yes | No | No | No | No | Yes |
Yes ?Comment:Culled metrics that did not distinguish between impaired and unimpaired sites. |
Unclear | No | Yes |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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Yes | No | No | Not applicable | Yes | Yes | No | Yes | No | No | No | No | No | No |
Yes ?Comment:For the year 2006 and 2011 |
No | No | No | No | No | No | No |
No ?Comment:Each outcome was fitted separatly, with R2 provided. See Variable Value comment for each Response. |
Not applicable |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None |
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None |
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None | None | None | None | None | None |
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None | None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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Yes | No | Yes | Not applicable | Yes | No | No | Yes | No | No | Yes | Yes | Yes | Yes | No | Yes | Yes |
No ?Comment:However, agreement of submodel and intermediate components; annual discharge (R2=0.79), and nitrate-N load (R2=0.74), based on GIS land use were determined in comparison with USGS NASQAN data. |
No | No | Yes | No | No | Unclear |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | Not applicable | Yes | Yes | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | Yes | Not applicable | Yes | Yes | No | No | No | No | No | No | No | No | No | No | No | No | No | Yes | Yes | No | No | No |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | No | Not applicable | Yes | Yes | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Yes | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-69 | EM-86 |
EM-111 ![]() |
EM-143 | EM-185 | EM-196 |
EM-333 ![]() |
EM-414 | EM-432 | EM-438 | EM-453 | EM-454 | EM-457 |
EM-467 ![]() |
EM-469 | EM-604 |
EM-618 ![]() |
EM-627 |
EM-718 ![]() |
EM-845 | EM-850 | EM-855 | EM-878 | EM-904 |
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None |
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None |
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None | None | None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-69 | EM-86 |
EM-111 ![]() |
EM-143 | EM-185 | EM-196 |
EM-333 ![]() |
EM-414 | EM-432 | EM-438 | EM-453 | EM-454 | EM-457 |
EM-467 ![]() |
EM-469 | EM-604 |
EM-618 ![]() |
EM-627 |
EM-718 ![]() |
EM-845 | EM-850 | EM-855 | EM-878 | EM-904 |
None | None |
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None |
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None | None | None | None | None |
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None | None |
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None | None | None | None | None | None | None |
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Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-69 | EM-86 |
EM-111 ![]() |
EM-143 | EM-185 | EM-196 |
EM-333 ![]() |
EM-414 | EM-432 | EM-438 | EM-453 | EM-454 | EM-457 |
EM-467 ![]() |
EM-469 | EM-604 |
EM-618 ![]() |
EM-627 |
EM-718 ![]() |
EM-845 | EM-850 | EM-855 | EM-878 | EM-904 |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | -30 | 47.8 | -9999 | 36.99 | -9999 | 44.11 | 46.72 | 17.96 | 17.97 | 17.73 | 17.73 | 17.73 | 46.82 | 36.7 | 44.62 | -9.05 | 40.6 | 44.06 | 36.23 | 31.39 | -2.59 | 39.95 | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
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6.4 | 25 | -122.7 | -9999 | -75.95 | -9999 | -123.09 | -96.13 | -67.02 | -66.93 | -64.77 | -64.77 | -64.77 | 8.23 | 109.52 | -124.02 | -77.81 | -88.4 | -114.69 | -81.9 | -85.71 | 29.97 | -75.17 | Not applicable |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | 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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Provided | Estimated | Estimated | Not applicable | Estimated | Not applicable | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable |
EM ID
em.detail.idHelp
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EM-69 | EM-86 |
EM-111 ![]() |
EM-143 | EM-185 | EM-196 |
EM-333 ![]() |
EM-414 | EM-432 | EM-438 | EM-453 | EM-454 | EM-457 |
EM-467 ![]() |
EM-469 | EM-604 |
EM-618 ![]() |
EM-627 |
EM-718 ![]() |
EM-845 | EM-850 | EM-855 | EM-878 | EM-904 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | None | Inland Wetlands | Near Coastal Marine and Estuarine | Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Inland Wetlands | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Aquatic Environment (sub-classes not fully specified) | Inland Wetlands | Near Coastal Marine and Estuarine | Open Ocean and Seas | Forests | Agroecosystems | Created Greenspace | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Forests | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Agroecosystems | Inland Wetlands | Grasslands | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Near Coastal Marine and Estuarine |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows. | Not reported | glacier-carved saltwater fjord | Not applicable | Yes | Wetlands (multiple types) | Agricultural-urban interface at river junction | Freshwater estuarine system | Tropical terrestrial | 13 LULC were used | Coral reefs | Coral reefs | Coral reefs | forests | Loess plain | Yaquina Bay estuary and ocean | tropical, coastal to montane | Agroecosystems and associated drainage and wetlands | created, restored and enhanced wetlands | grasslands | 1st - 6th order streams | varied | Urban and urban green space | Near Coastal Marine and Estuarine |
EM Ecological Scale
em.detail.ecoScaleHelp
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Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Yes | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-69 | EM-86 |
EM-111 ![]() |
EM-143 | EM-185 | EM-196 |
EM-333 ![]() |
EM-414 | EM-432 | EM-438 | EM-453 | EM-454 | EM-457 |
EM-467 ![]() |
EM-469 | EM-604 |
EM-618 ![]() |
EM-627 |
EM-718 ![]() |
EM-845 | EM-850 | EM-855 | EM-878 | EM-904 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Not applicable | Not applicable | Not applicable | Yes | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Guild or Assemblage | Not applicable | Community | Not applicable | Other (multiple scales) | Not applicable | Not applicable | Not applicable | Species |
Other (Comment) ?Comment:To species but focused on functional group classes |
Guild or Assemblage | Not applicable | Species |
Taxonomic level and name of organisms or groups identified
EM-69 | EM-86 |
EM-111 ![]() |
EM-143 | EM-185 | EM-196 |
EM-333 ![]() |
EM-414 | EM-432 | EM-438 | EM-453 | EM-454 | EM-457 |
EM-467 ![]() |
EM-469 | EM-604 |
EM-618 ![]() |
EM-627 |
EM-718 ![]() |
EM-845 | EM-850 | EM-855 | EM-878 | EM-904 |
None Available | None Available | None Available | None Available | None Available | None Available |
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None Available | None Available | None Available |
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None Available | None Available | None Available | None Available |
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None Available | None Available | None Available |
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None Available |
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None Available |
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EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-69 | EM-86 |
EM-111 ![]() |
EM-143 | EM-185 | EM-196 |
EM-333 ![]() |
EM-414 | EM-432 | EM-438 | EM-453 | EM-454 | EM-457 |
EM-467 ![]() |
EM-469 | EM-604 |
EM-618 ![]() |
EM-627 |
EM-718 ![]() |
EM-845 | EM-850 | EM-855 | EM-878 | EM-904 |
None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-69 | EM-86 |
EM-111 ![]() |
EM-143 | EM-185 | EM-196 |
EM-333 ![]() |
EM-414 | EM-432 | EM-438 | EM-453 | EM-454 | EM-457 |
EM-467 ![]() |
EM-469 | EM-604 |
EM-618 ![]() |
EM-627 |
EM-718 ![]() |
EM-845 | EM-850 | EM-855 | EM-878 | EM-904 |
None | None |
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None | None |
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None |
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None |
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None |
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None | None |
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None |
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None | None |
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None |